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Abstract:

The last twenty years of success in BCI design have led to the realisation of basic control functions by engineers, psychologists, machine learners and end users. These basic functions provide us with the freedom to design future BCI applications that go beyond the reliability of isolated intention detection events. Such a design process for the overall system comprises finding a suitable control metaphor, respecting neuroergonomic principles, designing visually aesthetic feedback, dealing with the learnability of the system, creating an effective application structure (navigation), and exploring the power of social aspects of an interactive BCI system.

Designing a human-machine system also involves eliciting a user’s knowledge, preferences, requirements and priorities. In order not to overload end users with evaluation tasks and to take into account issues specific to BCI, techniques and processes from other fields that aim to acquire these must be adapted for applications that use BCI. We present examples which illustrate this process.

Abstract:

Brain-Computer Interface (BCI) research is a currently very active and fast growing field, in particular in bringing the BCI out of the lab and moving from prototypes to real world applications such as brain-controlled writing applications, wheelchairs, and games. The research focus has been widened and BCIs are no longer only useful for patients, but also for healthy users, especially for BCI controlled or supported computer games. In this chapter, we focus on current devices and application scenarios for various user groups. Up to now, typical applications require a very good and precise control channel to achieve performances comparable to users without a BCI. However, current day BCIs offer low throughput information and are insufficient for the full dexterous control of such complex applications. Techniques like shared control can enhance the interaction to a similar level as without a BCI. With shared control the user is giving high-level commands on a low pace (e.g. directions of a wheelchair) and the system is executing fast and precise low-level interactions (e.g. obstacle avoidance). Furthermore, the performance of the applications can be improved by novel hybrid BCIs architectures, which are a synergetic combination of a BCI with other residual input channels. All together, modern human-computer interaction techniques combined with applications based on shared control principles which are controlled by a hybrid BCI are able to provide powerful interactions and applications for healthy and disabled users.

Abstract:

Long-term disability is often associated with persistent impairment of an upper limb. In this respect, neurological rehabilitation aims to lessen motor impairment and related disability either by restoring functions with the help of assistive de-vices to aid daily living activities or by applying rehabilitative protocols based on task-specific training and practice to enhance recovery of motor functions. Brain-computer interface technology is a promising rehabilitation device in every such sense. On the one hand, BCI systems can be utilized to bypass central nervous system injury by controlling neuroprosthetics for patient's arm to manage reach and grasp functional activities in peripersonal space. On the other, BCI technology can encourage motor training and practice by offering an on-line feedback about brain signals associated with mental practice, motor intention and other neural recruitment strategies, and thus helping to guide neuroplasticity associated with post-stroke motor impairment and its recovery. This chapter aims to provide a focused overview of non invasive-BCI technology advancement to serve patients in the field of restoration and recovery of hand motor function impairment accompanying spinal cord injuries and stroke.

Abstract:

There is growing interest in the use of brain signals for communication and operation of devices – in particular, for physically disabled people. Brain states can be detected and translated into actions such as selecting a letter from a virtual keyboard, playing a video game, or moving a robot arm. This chapter presents what is known about the effects of visual stimuli on brain activity and introduces means of monitoring brain activity. Possibilities of brain controlled interfaces, either with the brain signals as the sole input or in combination with the measured point of gaze, are discussed.

Abstract:

All successful BCI systems rely on an efficient real-time feedback. For this
reason, the data processing methods must be also suitable for online and real-
time processing. This requires causal algorithms which can only use sample
values from the past and present but not from the future. Adaptive methods
typically fulfill this requirement, while minimizing also the time delay. The
data processing in BCI consists typically of two main steps, (i) signal process-
ing and feature extraction, and (ii) classification or feature translation.
It is the aim of this work to introduce adaptive methods for both
steps which are also closely related to two types of non-stationarities.

Abstract:

In this paper, the results of a pilot interview study with 19 subjects participating in an EEG-based non-invasive brain–computer interface (BCI) research study on stroke rehabilitation and assistive technology and of a survey among 17 BCI professionals are presented and discussed in the light of ethical, legal, and social issues in research with human subjects. Most of the users were content with study participation and felt well informed. Negative aspects reported include the long and cumbersome preparation procedure, discomfort with the cap and the wet electrodes, problems concerning BCI control, and strains during the training sessions. In addition, some users reflected on issues concerning system security. When asked for morally problematic issues in this field of non-invasive BCI research, the BCI professionals stressed the need for correct information transfer, the obligation to avoid unrealistic expectations in study participants, the selection of study participants, benefits and strains of participation, BCI illiteracy, the possibility of detrimental brain modifications induced by BCI use, and problems that may arise at the end of the trials. Furthermore, privacy issues were raised. Based on the results obtained, psychosocial and ethical aspects of EEG-based non-invasive BCI research are discussed and possible implications for future research addressed.

Abstract:

Objectives: Recently, we proposed a new stimulation paradigm for brain computer interfaces (BCI) based on event-related potentials (ERP), i.e. flashing characters with superimposed pictures of well-known faces. This new face flashing (FF) paradigm significantly outperformed the commonly used character flashing (CF) approach, i.e. simply highlighting characters.
Methods: In the current study we assessed the impact of face stimuli on BCI inefficiency in patients with neurodegenerative disease, i.e. on their inability to communicate by means of a BCI. Healthy participants (N=16) and those with neurodegenerative disease (N=9) performed spelling tasks using CF and FF paradigms.
Results: Online performance with FF was significantly increased as compared to CF in both healthy and impaired users. Importantly, two patients who were classified “highly inefficient” with the classic CF stimulation were able to spell with high accuracy using FF. Our results particularly emphasize great benefit of the FF paradigm for those users displaying low signal-to-noise ratio of the recorded ERPs in the classic stimulation approach.
Conclusion: In conclusion, we confirm previously reported results now systematically validated in an online setting and display specifically beneficial effects of FF for motor-impaired users.
Significance: The FF paradigm thus constitutes a big step forward against the BCI inefficiency phenomenon.

Abstract:

Objective
The study aimed to improve brain–computer interface (BCI)-usability by using distinct control strategies and evaluating performance, brain activity and psychological variables on a long-term basis over several months.

Methods
Fourteen able-bodied users participated in 10 sessions, plus a follow-up session. Users were trained to control an EEG-based 4-class BCI with the mental tasks, word association, mental subtraction, spatial navigation, and motor imagery.

Results
Eight users reached mean accuracies of 61–72% and managed to control all 4 classes above chance in single-sessions. Performance and brain patterns stayed stable over 10weeks without training. Motor imagery showed the best performance and most distinct brain patterns. Participants’ fear of incompetence decreased while the quality of their imagery and task ease increased over sessions. The evaluation of feedback differed between tasks and correlated with performance.

Conclusion
Users can control a real-time 4-class BCI, driven by distinct mental tasks, with stable performance over months. However, general performance was rather low for effective BCI control in daily life. Possibilities for future optimizations to increase performance are discussed.

Significance
The evaluation of alternatives to motor imagery, long-term BCI use, and psychological variables is important to improve usability for mental imagery-based BCIs.

Abstract:

The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.

Abstract:

In the present study inhibitory cortical mechanisms have been investigated during execution and inhibition of learned motor programs by means of multi-channel functional near infrared spectroscopy (fNIRS). fNIRS is an emerging non-invasive optical technique for the in-vivo assessment of cerebral oxygenation, concretely changes of oxygenated [oxy-Hb] and deoxygenated [deoxy-Hb] hemoglobin. Eleven healthy subjects executed or inhibited previous learned finger and foot movements indicated by a visual cue. The execution of finger/foot movements caused a typical activation pattern namely an increase of [oxy-Hb] and a decrease of [deoxy-Hb] whereas the inhibition of finger/foot movements caused a decrease of [oxy-Hb] and an increase of [deoxy-Hb] in the hand or foot representation area (left or medial somatosensory and primary motor cortex). Additionally an increase of [oxy-Hb] and a decrease of [deoxy-Hb] in the medial area of the anterior prefrontal cortex (APFC) during the inhibition of finger/foot movements were found. The results showed, that inhibition/execution of learned motor programs depends on an interplay of focal increases and decreases of neural activity in prefrontal and sensorimotor areas regardless of the effector. As far as we know, this is the first study investigating inhibitory processes of finger/foot movements by means of multi-channel fNIRS.

Abstract:

Future neuroprosthetic devices, in particular upper limb, will require decoding and executing
not only the user’s intended movement type, but also when the user intends to
execute the movement. This work investigates the potential use of brain signals recorded
non-invasively for detecting the time before a self-paced reaching movement is initiated
which could contribute to the design of practical upper limb neuroprosthetics. In particular,
we show the detection of self-paced reaching movement intention in single trials using the
readiness potential, an EEG slow cortical potential computed in a narrow frequency range
(0.1 to 1 Hz). Our experiments with 12 human volunteers, two of them stroke subjects,
yield high recognition rates prior to the movement onset and low recognition rates during
the non-movement intention period. With the proposed approach, movement intention was
detected around 500 ms before actual onset, which clearly matches previous literature on
readiness potentials. Interestingly, the result obtained with one of the stroke subjects is coherent
with those achieved in healthy subjects, with single-trial performance of up to 92%
for the paretic arm. These results suggest that, apart from contributing to our understanding
of voluntary motor control for designing more advanced neuroprostheses, our work could
also have a direct impact on advancing robot-assisted neurorehabilitation.

Abstract:

Background and purpose ― Recently new strategies like motor imagery based brain-computer interfaces, which employ brain signals such as event-related (de)sychronisation (ERD/S) for motor rehabilitation after a stroke, are under investigation. However, little is known about the relationship between ERD/S patterns and the degree of stroke impairment. The aim of this
work was to clarify this relationship.
Methods ― EEG during motor imagery and execution was measured in 29 upper limb affected stroke patients. The strength and laterality of the ERD/S patterns were correlated with the degree of motor impairment.
Results ― Significant correlations were found between the degree of functional motor impairment and the intensity and laterality of the ERD/S pattern.
Conclusion ― The results of this study may have implications for the design of potential post-stroke rehabilitation interventions, based on brain-computer interface technologies which use neurophysiological signals like ERD/S as neural substrates for the mutual interaction between brain and machine and, ultimately help stroke patients to regain motor control.

Abstract:

Brain Computer Interfaces (BCI) based on event-related potentials (ERP) allow for selection of characters from a visually presented character-matrix and thus provide a communication channel for users with neurodegenerative disease. Although they have been topic of research for more than 20 years and were multiply proven to be a reliable communication method, BCIs are almost exclusively used in experimental settings, handled by qualified experts. This study investigates if ERP-BCIs can be handled independently by laymen without expert interference, which is inevitable for establishing BCIs in end-user’s daily life situations. Furthermore we compared the classic character-by-character text entry against a predictive text entry (PTE) that directly incorporates predictive text into the character matrix. N=19 BCI novices handled a user-centred ERP-BCI application on their own without expert interference. The software individually adjusted classifier weights and control parameters in the background, invisible to the user (auto-calibration). All participants were able to operate the software on their own and to twice correctly spell a sentence with the auto-calibrated classifier (once with PTE, once without). Our PTE increased spelling speed and importantly did not reduce accuracy. In sum, this study demonstrates feasibility of auto-calibrating ERP-BCI use, independently by laymen and the strong benefit of integrating predictive text directly into the character-matrix.

Abstract:

The Farwell and Donchin P300 speller interface is one of the most widely used brain–computer interface (BCI) paradigms for writing text. Recent studies have shown that the recognition accuracy of the P300 speller decreases significantly when eye movement is impaired. This report introduces the GeoSpell interface (Geometric Speller), which implements a stimulation framework for a P300-based BCI that has been optimised for operation in covert visual attention. We compared the Geospell with the P300 speller interface under overt attention conditions with regard to effectiveness, efficiency and user satisfaction. Ten healthy subjects participated in the study. The performance of the GeoSpell interface in covert attention was comparable with that of the P300 speller in overt attention. As expected, the effectiveness of the spelling decreased with the new interface in covert attention. The NASA task load index (TLX) for workload assessment did not differ significantly between the two modalities.

Abstract:

The present review systematically examines the literature reporting gaze independent interaction modalities in non-invasive brain computer interfaces (BCIs) for communication.
BCIs measure signals related to specific brain activity and translate them into device control signals. This technology can be used to provide users with severe motor disability (e.g. late stage amyotrophic lateral sclerosis (ALS); acquired brain injury) with an assistive device that does not rely on muscular contraction. Most of the studies on BCIs explored mental tasks and paradigms using visual modality. Considering that in ALS patients the oculomotor control can deteriorate and also other potential users could have impaired visual function, tactile and auditory modalities have been investigated over the past years to seek alternative BCI systems which are independent from vision. In addition, various attentional mechanisms, such as covert attention and feature-directed attention, have been investigated to develop gaze independent visual-based BCI paradigms. Three areas of research were considered in the present review: (i) auditory BCIs, (ii) tactile BCIs and (iii) independent visual BCIs. Out of a total of 130 search results, 34 articles were selected on the basis of pre-defined exclusion criteria. Thirteen articles dealt with independent visual BCIs, 15 reported on auditory BCIs and the last six on tactile BCIs, respectively. From the review of the available literature, it can be concluded that a crucial point is represented by the trade-off between BCI systems/paradigms with high accuracy and speed, but highly demanding in terms of attention and memory load, and systems requiring lower cognitive effort but with a limited amount of communicable information. These issues should be considered as priorities to be explored in future studies to meet users’ requirements in a real-life scenario.

Abstract:

There is increasing interest in the intrinsic activity in the resting brain, especially on ultraslow and
slow oscillations. Using near-infrared spectroscopy, EEG, blood pressure (BP), respiration and heart
recordings during 5 minutes of rest, combined with cross spectral and sliding cross correlation calculations,
we identied a short-lasting coupling (duration about 100s) between prefrontal oxyhemoglobin (HbO2) in the
frequency band between 0.07 and 0.13 Hz and central EEG beta/alpha power oscillations in 8 of the 9
subjects investigated. The HbO2 peaks preceded the EEG band power peaks by 3.7 s in 6 subjects, with
moderate or no coupling between BP and HbO2 oscillations. HbO2 and EEG band power oscillations were
approximately in phase with BP oscillations in the 2 subjects with an extremely high coupling between
BP and HbO2 oscillation. No coupling was identied in one subject. These results indicate that slow
precentral (de)oxyhemoglobin concentration oscillations during awake rest can be temporarily coupled
with EEG uctuations in sensorimotor areas and modulate the excitability level in the brains motor
areas, respectively, and therefore provides support that routine state networks flutuate with frequencies between 0.01 and 0.1 Hz [1].

Abstract:

Assistive devices for persons with limited motor control translate or amplify remaining functions to allow otherwise impossible actions. These assistive devices usually rely on just one type of input signal which can be derived from residual muscle functions or any other kind of biosignal. When only one signal is used, the functionality of the assistive device can be reduced as soon as the quality of the provided signal is impaired. The quality can decrease in case of fatigue, lack of concentration, high noise, spasms, tremors, depending on the type of signal. To overcome this dependency on one input signal, a combination of more inputs should be feasible.
This work presents a hybrid Brain-Computer Interface (hBCI) approach where two different input signals (joystick and BCI) were monitored and only one of them was chosen as a control signal at a time. Users could move a car in a game-like feedback application to collect coins and avoid obstacles via either joystick or BCI control. Both control types were constantly monitored with four different long term quality measures to evaluate the current state of the signals. As soon as the quality dropped below a certain threshold, a monitoring system would switch to the other control mode and vice versa. Additionally, short term quality measures were applied to check for strong artifacts that could render voluntary control impossible. These measures were used to prohibit actions carried out during times when highly uncertain signals were recorded.
The switching possibility allowed more functionality for the users. Moving the car was still possible even after one control mode was not working any more. The proposed system serves as a basis that shows how BCI can be used as an assistive device, especially in combination with other assistive technology.

Abstract:

Patients who benefit from Brain–Computer Interfaces (BCIs) may have difficulties to generate more than one distinct brain pattern which can be used to control applications. Other BCI issues are low performance, accuracy, and, depending on the type of BCI, a long preparation and/or training time. This study aims to show possible solutions. First, we used time-coded motor imagery (MI) with only one pattern. Second, we reduced the training time by recording only 20 trials of active MI to set up a BCI classifier. Third, we investigated a way to record error potentials (ErrPs) during continuous feedback. Ten subjects controlled an artificial arm by performing MI over target time periods between 1 and 4 s. The subsequent movement of this arm served as continuous feedback. Discrete events, which are required to elicit ErrPs, were added by mounting blinking LEDs on top of the continuously moving arm to indicate the future movements. Time epochs after these events were used to evaluate ErrPs offline. The achieved error rate for the arm movement was on average 26.9%. Obtained ErrPs looked similar to results from the previous studies dealing with error detection and the detection rate was above chance level which is a positive outcome and encourages further investigation.

Abstract:

Objective: To investigate whether error-related potentials can be used to increase information transfer rate of a P3
brain-computer interface (BCI) in healthy and motor-impaired individuals.
Methods: Extraction and classication of the error-related potential was performed oine on data recorded from
6 amyotrophic lateral sclerosis (ALS) patients. An online study with 17 healthy and 6 motor impaired participants
followed, using a modied P3 speller to provide explicit feedback of spelled letters. On recognition of error-related
potentials, the interface informed users that the incorrect letter was automatically deleted.
Results: The oine cross-validation estimate of P3 speller data of 6 ALS patients increased bit rate by 0.44 bit/trial.
During online copy spelling, the participants increased their bit rate by 0.52 bit/trial with the error correction system
(ECS). Some participants performed free spelling and were able to increase their bit rate. Finally, we demonstrated
that healthy participants could increase their bit rate by using a classier pre-trained on other users' data.
Conclusions: Error-related potentials as a secondary source of information can be used to increase overall bit rate in
a P3 BCI.
Signicance: The method should be made available to any patient using the P3 BCI for communication.
Keywords: error-related potential, brain-computer interface, amyotrophic lateral sclerosis, P3 evoked response
potential, electroencephalogram

Abstract:

OBJECTIVE: Brain computer interfaces (BCI) can serve as a communication system for people with severe impairment in speech and motor function due to neurodegenerative disease or injury. Reasons for inter-individual differences in capability of BCI usage are not yet fully understood. Paradigms making use of the P300 event-related potential are widely used. Success in a P300 based BCI requires the capability to focus attention and inhibit interference by distracting irrelevant stimuli. Such inhibitory control has been closely linked to peripheral physiological parameters, such as heart rate variability (HRV). The present study investigated the association between resting HRV and performance in the P300-BCI.

METHODS: Heart rate was recorded from 34 healthy participants under resting conditions, and subsequently a P300-BCI task was performed.

RESULTS:Frequency domain measures of HRV were significantly associated with BCI-performance, in that higher vagal activation was related to better BCI-performance.

CONCLUSIONS:Resting HRV accounted for almost 26% of the variance of BCI performance and may, therefore, serve as a predictor for the capacity to control a P300 oddball based BCI.

SIGNIFICANCE:This is the first study to demonstrate resting vagal-cardiac activation to predict capability of P300-BCI usage.

Abstract:

Recently, several studies have started to explore covert visuospatial attention as a control signal for brain–computer interfaces (BCIs). Covert visuospatial attention represents the ability to change the focus of attention from one point in the space without overt eye movements. Nevertheless, the full potential and possible applications of this paradigm remain relatively unexplored. Voluntary covert visuospatial attention might allow a more natural and intuitive interaction with real environments as neither stimulation nor gazing is required. In order to identify brain correlates of covert visuospatial attention, classical approaches usually rely on the whole α-band over long time intervals. In this work, we propose a more detailed analysis in the frequency and time domains to enhance classification performance. In particular, we investigate the contribution of α sub-bands and the role of time intervals in carrying information about visual attention. Previous neurophysiological studies have already highlighted the role of temporal dynamics in attention mechanisms. However, these important aspects are not yet exploited in BCI. In this work, we studied different methods that explicitly cope with the natural brain dynamics during visuospatial attention tasks in order to enhance BCI robustness and classification performances. Results with ten healthy subjects demonstrate that our approach identifies spectro-temporal patterns that outperform the state-of-the-art classification method. On average, our time-dependent classification reaches 0.74 ± 0.03 of the area under the ROC (receiver operating characteristic) curve (AUC) value with an increase of 12.3% with respect to standard methods (0.65 ± 0.4). In addition, the proposed approach allows faster classification (<1 instead of 3 s), without compromising performances. Finally, our analysis highlights the fact that discriminant patterns are not stable for the whole trial period but are changing over short time intervals. These results support the hypothesis that visual attention information is actually indexed by subject-specific α sub-bands and is time dependent.

Abstract:

This off-line study aims to assess the performance of five classifiers commonly used in the brain–computer interface (BCI) community, when applied to a gaze-independent P300-based BCI. In particular, we compared the results of four linear classifiers and one nonlinear: Fisher's linear discriminant analysis (LDA), stepwise linear discriminant analysis (SWLDA), Bayesian linear discriminant analysis (BLDA), linear support vector machine (LSVM) and Gaussian supported vector machine (GSVM). Moreover, different values for the decimation of the training dataset were tested. The results were evaluated both in terms of accuracy and written symbol rate with the data of 19 healthy subjects. No significant differences among the considered classifiers were found. The optimal decimation factor spanned a range from 3 to 24 (12 to 94 ms long bins). Nevertheless, performance on individually optimized classification parameters is not significantly different from a classification with general parameters (i.e. using an LDA classifier, about 48 ms long bins).

Abstract:

Moving from well-controlled, brisk artificial stimuli to natural and less-controlled stimuli seems counter-intuitive for event-related potential (ERP) studies. As natural stimuli typically contain a richer internal structure, they might introduce higher levels of variance and jitter in the ERP responses. Both characteristics are unfavorable for a good single-trial classification of ERPs in the context of a multi-class brain–computer interface (BCI) system, where the class-discriminant information between target stimuli and non-target stimuli must be maximized. For the application in an auditory BCI system, however, the transition from simple artificial tones to natural syllables can be useful despite the variance introduced. In the presented study, healthy users (N = 9) participated in an offline auditory nine-class BCI experiment with artificial and natural stimuli. It is shown that the use of syllables as natural stimuli does not only improve the users' ergonomic ratings; also the classification performance is increased. Moreover, natural stimuli obtain a better balance in multi-class decisions, such that the number of systematic confusions between the nine classes is reduced. Hopefully, our findings may contribute to make auditory BCI paradigms more user friendly and applicable for patients.

Abstract:

Currently, the event-related potential (ERP)-based spelling device, often referred to as P300-Speller, is the most commonly used brain–computer interface (BCI) for enhancing communication of patients with impaired speech or motor function. Among numerous improvements, a most central feature has received little attention, namely optimizing the stimulus used for eliciting ERPs. Therefore we compared P300-Speller performance with the standard stimulus (flashing characters) against performance with stimuli known for eliciting particularly strong ERPs due to their psychological salience, i.e. flashing familiar faces transparently superimposed on characters. Our results not only indicate remarkably increased ERPs in response to familiar faces but also improved P300-Speller performance due to a significant reduction of stimulus sequences needed for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-Speller.

Abstract:

The aim of this work is to present the development of a hybrid
Brain-Computer Interface (hBCI) which combines existing input de-
vices with a BCI. Thereby, the BCI should be available if the user
wishes to extend the types of inputs available to an assistive technol-
ogy system, but the user can also choose not to use the BCI at all;
the BCI is active in the background. The hBCI might decide on the
one hand which input channel(s) oer the most reliable signal(s) and
switch between input channels to improve information transfer rate,
usability, or other factors, or on the other hand fuse various input
channels. One major goal therefore is to bring the BCI technology to
a level where it can be used in a maximum number of scenarios in a
simple way. To achieve this, it is of great importance that the hBCI
is able to operate reliably for long periods, recognizing and adapting
to changes as it does so. This goal is only possible if many dierent
subsystems in the hBCI can work together. Since one research insti-
tute alone cannot provide such dierent functionality, collaboration
between institutes is necessary. To allow for such a collaboration, a
new concept and common software framework is introduced. It con-
sists of four interfaces connecting the classical BCI modules signal
acquisition, pre-processing, feature extraction, classication, and the
application. But it provides also the concept of fusion and shared
control. In a proof of concept, the functionality of the propose system
was demonstrated.

Abstract:

Recently brain-computer interface (BCI) control was integrated into the commercial assistive technology product QualiWOLRD (QualiLife Inc., Paradiso-Lugano, CH). Usability of the first prototype was evaluated in terms of effectiveness (accuracy), efficiency (information transfer rate and subjective workload/NASA Task Load Index) and user satisfaction (Quebec User Evaluation of Satisfaction with assistive Technology, QUEST 2.0) by four end-users with severe disabilities. Three assistive technology experts evaluated the device from a third person perspective. The results revealed high performance levels in communication and internet tasks. Users and assistive technology experts were quite satisfied with the device. However, none could imagine using the device in daily life without improvements. Main obstacles were the EEG-cap and low speed.

Abstract:

In the event of a spinal cord injury, the motor cortex is usually left unharmed. In this situation a by-pass of the lesion in order to restore part of the patient's mobility would be very helpful. A first step in this direction would be to decode hand movements using a non-invasive brain-computer interface. In the present study, a 8-class 2-dimensional center-out task is carried out, with a final average accuracy of 65 %, with best performance at 83 %.

Abstract:

Within this work we propose a standardized interface (called TiA) to transmit raw biosignals,
supporting multirate and block-oriented transmission of different kinds of signals from various acquisition devices (e.g. EEG, EOG, NIRS,...)
at the same time. To facilitate a distinction between those kinds of signals, so-called signal types are introduced. TiA is a
single server, multiple client system, whereby clients can connect to the server at runtime.
Information transfer between client and server is divided into control- and data connections. The
control connection uses TCP and transmits XML encoded meta information.
The data transmission utilizes UDP or TCP with a binary data stream. A standardized handshaking procedure for
connection setup and a standardized binary data packet has been defined. Thus a standardized layer, abstracting
used hardware devices and facilitating distributed raw data transmission in a standardized way has been evolved.
A cross platform library, implemented in C++, is available for download.

Abstract:

In this study, we propose a system in which the BCI stimulation overlays the application. We merged Qualiworld, an already existing assistive technology
commercial solution, with a p300-based BCI interaction channel.
In a preliminary testing phase, six healthy users and one Acquired Brain Injury (ABI) end-user were challenged with 4 complex tasks.
Four of the healthy users controlled the system with 84% accuracy, on average. The non-healthy end-user controlled the system with 73,5 % accuracy on
average.

Abstract:

Representing an intuitive spelling interface for Brain-Computer Interfaces (BCI) in the auditory domain is not straightforward. In consequence, all existing approaches based on event-related potentials (ERP) rely at least partially on a visual representation of the interface. This online study introduces an auditory spelling interface that eliminates the necessity for such a visualization. In up to two sessions, a group of healthy subjects (N=21) was asked to use a text entry application, utilizing the spatial cues of the AMUSE paradigm (Auditory Multiclass Spatial ERP). The speller relies on the auditory sense both for stimulation and the core feedback. Without prior BCI experience, 76% of the participants were able to write a full sentence during the first session. By exploiting the advantages of a newly introduced dynamic stopping method, a maximum writing speed of 1.41 characters/minute (7.55 bits/minute) could be reached during the second session (average: .94 char/min, 5.26 bits/min). For the first time, the presented work shows that an auditory BCI can reach performances similar to state-of-the-art visual BCIs based on covert attention. These results represent an important step towards a purely auditory BCI.

Abstract:

Brain–computer interfaces (BCIs) based on event related potentials (ERPs) strive for offering communication pathways which are independent of muscle activity. While most visual ERP-based BCI paradigms require good control of the user’s gaze direction, auditory BCI paradigms overcome this restriction. The present work proposes a novel approach using auditory evoked potentials for the example of a multiclass text spelling application. To control the ERP speller, BCI users focus their attention to two-dimensional auditory stimuli that vary in both, pitch (high/medium/low) and direction (left/middle/right) and that are presented via headphones. The resulting nine different control signals are exploited to drive a predictive text entry system. It enables the user to spell a letter by a single nine-class decision plus two additional decisions to confirm a spelled word. This paradigm – called PASS2D – was investigated in an online study with 12 healthy participants. Users spelled with more than 0.8 characters per minute on average (3.4 bits/min) which makes PASS2D a competitive method. It could enrich the toolbox of existing ERP paradigms for BCI end users like people with amyotrophic lateral sclerosis disease in a late stage.

Abstract:

Abstract. Advancing Brain Computer Interface (BCI) toward practical applications in technology-based assistive solutions for people with disabilities, requires coping with problems of accessibility and usability to increase user acceptance and satisfaction. The main objective of this study was to introduce a usability-oriented approach in the assessment of BCI technology development by focusing on the evaluation of user’s subjective workload and satisfaction. A secondary aim was to compare two applications for a P300-based BCI. Eight healthy subjects were asked to use an assistive technology solution which integrates a P300-based BCI with a commercially available software under two conditions – visual stimuli needed to evoke the P300 response were either overlaid onto the application’s graphical user interface or presented on a separate screen. The two conditions were compared for effectiveness (level of performance), efficiency (subjective workload measured by mean of NASA-TXL) and satisfaction of the user. Although no significant difference in usability could be detected between the two conditions, the methodology proved to be an effective tool to highlight weaknesses in the technical solution.

Abstract:

Brain-Computer Interfaces (BCIs) have been investigated for more than 20 years. Many BCIs use non-invasive electroencephalography as a measurement technique and the P300 event related potential as an input signal (P300 BCI). Since the first experiment with a P300 BCI system in 1988 by Farwell and Donchin not only data processing has improved, but also stimuli presentation has been varied and a plethora of applications was developed and refined. Nowadays, these applications are facing the challenge of being transferred from the research laboratory into real life situations to serve motor-impaired people in their homes as assistive technology.

Abstract:

Brain–computer interfaces (BCIs) enable people with paralysis to communicate with their environment. Motor imagery can be used to generate distinct patterns of cortical activation in the electroencephalogram(EEG) and thus control a BCI. To elucidate the cortical correlates of BCI control, users of a sensory motor rhythm (SMR)-BCI were classified according to their BCI control performance. In a second session these participants performed a motor imagery, motor observation and motor execution task in a functional magnetic resonance imaging (fMRI) scanner. Group difference analysis between high and low aptitude BCI users revealed significantly higher activation of the supplementary motor areas (SMA) for the motor imagery and the motor observation tasks in high aptitude users. Low aptitude users showed no activation when observing movement. The number of activated voxels during motor observation was signficantly correlated with accuracy in the EEG-BCI task (r=0.53). Furthermore, the number of activated voxels in the right middle frontal gyrus, an area responsible for processing of movement observation, correlated (r=0.72) with BCI-performance. This strong correlation highlights the importance of these areas for task monitoring and working memory as task goals have to be activated throughout the BCI session. The ability to regulate behavior and the brain through learning mechanisms involving imagery such as a BCI constitutes the consequence of ideo-motor co-activation of motor brain systems during observation of movements. The results demonstrate that acquisition of a sensorimotor program reflected in SMR-BCI-control is tightly related to the recall of such sensorimotor programs during observation of movements and unrelated to the actual execution of these movement sequences.

Abstract:

Laplacian filters are widely used in neuroscience. In the context of Brain-
Computer Interfacing (BCI), they might be preferred to data-driven approaches such
as Common Spatial Patterns (CSP) in a variety of scenarios as e.g. when no or few user
data is available or a calibration session with a multi-channel recording is not possible,
which is the case in various applications. In this manuscript we propose the use of
an ensemble of local CSP patches (CSPP) which can be considered as a compromise
between Laplacian filters and CSP. Our CSPP only needs a very few number of trials
to be optimized and significantly outperforms Laplacian filters in all settings studied.
Additionally, CSPP also outperforms multi-channel CSP and a regularized version of
CSP even when only very little calibration data is available, acting as a CSP regularizer
without the need of additional hyperparameters and at a very low cost: 2-5 minutes
of data recording, i.e. 10 times less than CSP.

Abstract:

All Brain-Computer Interface (BCI) groups that have published results of studies
involving a large number of users performing BCI control based on the voluntary
modulation of sensorimotor rhythms (SMR) report that BCI control could not be
achieved by a non-negligible number of subjects (estimated 20% to 25%). This failure
of the BCI system to read the intention of the user is one of the greatest problems
and challenges in BCI research. There are two main causes for this problem in
SMR-based BCI systems: either no idle SMR is observed over motor areas of the
user, or this idle rhythm is not modulated during motor imagery, resulting in a
classication performance lower than 70% (criterion level) that renders the control of
a BCI application (like a speller) dicult or impossible. Previously, we introduced the
concept of machine learning based co-adaptive calibration which provided substantially
improved performance for a variety of users. Here, we use a similar approach and
investigate to what extent co-adaptive learning enables signicant BCI control for
completely novice users and as well for those who could not achieve control with a
conventional SMR-based BCI.

Abstract:

There is a step of significant difficulty experienced
by Brain-Computer Interface (BCI) users when going from
the calibration measurement to the feedback application. This
effect has been previously studied and a supervised adaptation
solution has been proposed. In this paper we suggest a simple
unsupervised adaptation method of the LDA classifier that
effectively solves this problem by counteracting the harmful
effect of non-class related non-stationarities in EEG during BCI
sessions performed with motor imagery tasks. For this, we first
introduce three types of adaptation procedures and investigate
them in an offline study with 19 data-sets. Then, we select one
of the proposed methods and analyze it further. The chosen
classifier is off-line tested in data from 80 healthy users and
four high spinal cord injury patients. Finally, for the first time
in BCI literature, we apply this unsupervised classifier in online
experiments. Additionally, we show that its performance is
significantly better than the state-of-the-art supervised approach.

Abstract:

This paper discusses and evaluates the role of shared control approach in a BCI-based telepresence framework. By means of a bidirectional audio/video connection to a robot, the BCI user is able to interact actively with relatives and friends located in different rooms. However, the control of robots through an uncertain channel as a BCI may be complicated and exhaustive. Shared control can facilitate the operation of brain-controlled telepresence robots, as demonstrated by the experimental results reported here.

Abstract:

Brain-Computer Interfaces convey a great potential in the field of stroke rehabilitation, where
the continuous monitoring of the execution of mental tasks could support the positive effects of the
therapy by reinforcing specific mental patterns. We propose the use of EEG Microstates as the building
blocks of a novel BCI for Operators, and we show that preliminary results on a stroke patient seem to
confirm that topographic information can provide hints on salient activity patterns on the affected
hemisphere not recognizable within a simple discriminant framework. Furthermore, the technique could
easily keep track of the evolution of these maps over different rehabilitation sessions, thus providing a
strong hint of plasticity phenomena and cortical reorganization.

Abstract:

The non-invasive Brain-Computer Interface (BCI) developed in our lab targets asynchronous
operation of devices by monitoring electroencephalographic (EEG) activity and identifying oscillatory
patterns that the user can voluntary modulate through the execution of motor imagery (MI) tasks.
Successful self-paced interaction under this framework requires the incorporation of an evidence
accumulation module to eliminate the uncertainty of single-sample classification and to drive an
efficient feedback visualization. In this work, we motivate the need for this additional module, describe
its role in a closed-loop MI BCI and present a comparative study of two different frameworks for
evidence accumulation.

Abstract:

The article shows where the argument of responsibility-gap regarding brain-computer interfaces acquires its plausibility from, and suggests why the argument is not plausible. As a way of an explanation, a distinction between the descriptive third-person perspective and the interpretative first-person perspective is introduced. Several examples and metaphors are used to show that ascription of agency and responsibility does not, even in simple cases, require that people be in causal control of every individual detail involved in an event. Taking up the current debate on liability in BCI use, the article provides and discusses some rules that should be followed when potentially harmful BCI-based devices are brought from the laboratory into everyday life.

Abstract:

Brain-actuated wheelchairs offer paraplegics the potential to gain a degree of independence in performing activities of daily living. It is not currently possible to achieve precise proportional control of devices using the low resolution output of a brain-computer interface (BCI). Consequently, we have developed a shared control system that interprets such commands, given the context of the surroundings. In this paper we show that a vision system provides sufficiently reliable information to the shared controller, to enable synthesized BCI subjects to drive safely in an office environment. The shared controller reduces both the time and number of commands required to perform a task.

Abstract:

The Speller based on N by N matrix is the most commonly used approach for text writing in a P300-based Brain Computer Interface. This study presents an alternative P300 Speller interface, GeoSpell (Geometric Speller), where stimuli are delivered in a covert attention modality and thus, not requiring eye gaze. Moreover, GeoSpell interface allows to optimize the stimulation sequence in order to reduce the “Attentional Blink” phenomenon. A performances comparison between the two interfaces showed a comparable classification accuracy

Abstract:

The brain computer interface technologies.can be considered not only as a sostitutive communication means but also as an additional channel to interact with external world. For this reason it is necessary to improve these systems in terms of reliability, stability and ease of use. This study starting from an asynchronous P300-based Brain Computer Interface system improved it by introducing a strategy for the online autocalibration. The reported results showed high robustness in avoiding errors (< 2%) even without subject specific control parameters, providing the basis for the implementation of a completely autoadaptive system

Abstract:

In the context of the EEG-based BCI, the P300 is a promising control feature that can be elicited with the "Oddball" paradigm. There are no evidences about the possibility to enhance brain responses through training. The aim of this study was to investigate if the use of P300-based BCI systems can increase ERPs stability and therefore potentiate user’s perfomances

Abstract:

If we want to use Brain-Computer Interfaces (BCI) as feedback training for motor rehabilitation after stroke a fast and easy acquisition of a reliable classifier is indispensible. In order to find new strategies for setting up a classifier to correctly detect activation patterns of motor imagery two approaches, active movement and passive movement, were examined and tested in a sample of 20 healthy elderly participants. For this purpose we recorded data from passive movement (hands were moved by a handrobot), active movement (opening and closing of hands) and motor imagery. In this paper we set up a classifier of data from passive movement and a classifier of data from active movement and use these classifiers to detect motor imagery. Subsequently the classification accuracies of the two classifiers are compared to check which strategy is more successful in detecting motor imagery. In addition the patterns of cortical activation for the different tasks were compared.

Abstract:

In this paper, the components of a modular FES-hybrid orthosis aiming at 1. the substitution of a lost restoration of the hand grasp and elbow movements in high spinal cord injured individuals and 2. the recovery of functions in the sense a BCI-controlled rehabilitative training for stroke patients is presented. The novel device can be individually adapted to different users and application scenarios and supports a variety of input modalities for control including a hybrid-BCI. This approach aims at a personalized setup of the device, selecting only those modules, which are needed to fullfil a given user need.

Abstract:

This work aims at the investigation of the artefacts in EEG-signals caused by Functional Electrical Stimulation (FES). In a first step an “in-vitro” testbed with a conductivity similar to the human body has been set up. The experiements revealed that the impluse response of the filters of the EEG amplifier are not dependent on the electrode parameters. In a second step these results have been confirmed in healthy volunteers, who received a low-frequency (1 Hz) stimulation at the arm. It was found that the artefacts have a similar shape. Finally, a variety of methods for minimizing the artefacts have been tested.

Abstract:

In a classic hand vs. foot sensory motor rhythm based brain computer interface (SMR-BCI)
participants control the direction of a computer cursor movement by either imagining hand (upward cursor movement) or foot movement (downward). Herein we present a multimodal feedback approach that provides participants with additional information on their actual ability to control the cursor. Two groups of participants performed either the classic or the multimodal feedback approach. Preliminary results are promising in that the increased complexity of the display has not led to a worsening of performance due to anticipated higher attentional demands of processing of multimodal feedback information.

Abstract:

Abstract. In this study we investigated the effect of motivation on Brain-Computer Interface (BCI) performance. We pooled N=90 participants from six different data sets for analysis. The group of the highest motivated participants (N=24) performed significantly better in their BCI task as compared to the least motivated group (N=22). Therefore, we recommend to monitor motivation in BCI settings and to increase it in a training setting.

Keywords: Brain-Computer Interface (BCI), motivation, P300

Abstract:

This work presents the concept of a hybrid BCI (hBCI) which allows a modular design through defined interfaces. It defines an integrated system where a BCI is defined as one assistive device among others for the entry of control commands.

Abstract:

Neuroprostheses can enable motor activities that would otherwise be impossible for some
disabled users, thus expanding their capabilities of daily. In the present experiment, healthy subjects
performed a center out reaching task. Our goal was to detect their hand/arm direction from noninvasive
EEG measures. Preliminary, we found that the hand direction can be successfully inferred
from the raw EEG.

Abstract:

This study investigates the cortical training effects using a 2-class motor imagery (MI) based
BCI. Twelve subjects were trained to use right hand or foot MI to control a cursor on a screen. The
feedback was calculated by using features based on the EEG. To assess which areas are involved in the
training, and how activity in these areas changes over time, three fNIRS measurements were applied
before, in between and after the training. The statistical analysis of the measurement revealed that
significant activity changes in the involved areas during the training can be found and that they occur
accordingly to the task.

Abstract:

TOBI interface A (TiA) describes a standardized interface to transmit raw biosignals,
supporting multirate and block-oriented transmission of different kinds of signals at the same time. To
facilitate a distinction between those kinds of signals, so-called signal types are introduced. TiA is a
single server, multiple client system, whereby clients can connect to the server at runtime. Meta
information transfer between client and server is divided into a control- and data connection. The
control communication is using TCP with xml messages, and data transmission is using UDP or TCP
with a binary data stream.

Abstract:

The aim of this work was to investigate cortical effects of user learning in a BCI training. 15
participants absolved six sessions of a two- class BCI training (rigth hand vs. feet motor imagery),
whereby the classifier gained from an initial screening session was not adapted. Good performers
showed distinct patterns right from the beginning and no changes due to the training could be observed.
In bad performers a cortical effect of BCI training was found. A significant difference in brain activity
pattern between right hand and feet motor imagery developed in the course of the training.

Abstract:

Here we present initial results of the preprocessing method Slow Feature Analysis (SFA) for a
BCI data set. It is the first time SFA is applied to EEG. SFA is an unsupervised learning method that
optimizes the signal representation with respect to temporal slowness. Its objective as well as its
computational properties render it a possibly useful candidate for the preprocessing of BCI EEG data in
order to detect task relevant components as well as components that represent artifacts or non-
stationarities of the background brain activity or external sources.

Abstract:

Evaluation and testing of Brain-Computer Interface applications using an electroencephalogram is time-consuming and tiring for end users. This paper describes the process of developing a simulator for a paradigm based on visual event related potentials. Used as input into a real BCI application, we demonstrate that simulation at this level is useful for debugging applications and obtaining feedback from end users quickly. The simulator itself also fulfills a user requirement as it can be used to ‘handicap’ a healthy person, balancing the pace of communication and creating a level playing field in a game between both healthy and disabled users.

Abstract:

While Brain-Computer Interfaces (BCIs) should generate control commands based on neural activity only, the electroencephalogram contains artifacts such as eye- or muscle activity, and healthy subjects might use those (sub-)consciously for BCI-control. We analyze the influence of an automatic, subject independent artifact reduction step on the performance of a motor imagery setup that uses Common Spatial Patterns. The offline test conducted on data from 80 subjects revealed no performance drop of the Berlin Brain-Computer-Interface after very rigorous artifact reduction.

Abstract:

Data from 9 healthy subjects collected during BCI experiments with a visual ERP paradigm for a photo browser application has been analyzed offline. A comparison of four stimulus presentation modes revealed that complex highlighting effects composed of brightness enhancement, rotation, enlargement and a trichromatic grid overlay in combination with row-column spatial arrangements of simultaneously highlighted objects boosted single subtrial classification performance best.

Abstract:

Results from online experiments with healthy users show that the AMUSE paradigm, using spatially distributed auditory cues, can successfully be controlled by most users. In order to test its applicability to end-users with severe motor disabilities, a study is performed with 5 acquired brain injury end-users, a potential user group of BCI. Promising preliminary results have been obtained so far and will be presented here.

Abstract:

Two separate online studies with healthy subjects investigate the usability and the speed of novel Brain-Computer Interface paradigms that exclusively use spatial-auditory stimuli to drive an ERP speller. It was found that participants could use both paradigms (names AMUSE and PASS2D) for a spelling task with high accuracy (90%) and speed (~0.9char/min). Based on these results, the paradigms qualify for future studies with patients, that suffer from a loss of gaze control.

Abstract:

Abstract. That human beings ‘have a body’ is an abstract way of speaking. As a detached entity, the body arises as a unit of self-restitance in learning, failure, acrasia, and disease/impairment. Making the body diappear in an undivided practice is an implicit aim of education, morals and technology. Disability is a complex social phenomenon of comparing people’s performances to the practice of the average majority in a given society. Impairment preventing the disappearance of the body results in disability. Only a technological strategy aiming at the emulation of average body practice, but not the strategy of (only) function substitution, could ever overcome disability in a comprehensive manner.
Keywords: Body, disability studies, concepts of disability, assistive technology

Abstract:

Abstract. It has been argued that BCI technology faces special problems regarding moral and/or legal responsibility. The article shows that the arguments behind this assumption are based on descriptive and causal analyses of BCI use while judgements on agency and responsibility are interpretative in character. The ascription of agency and responsibility does, even in simple cases, not require that people are in causal control of all particular details involved in an event. From a pragmatic point of view, responsibility in BCI use can be handled in parallel with rules and regulations currently followed in dealing with established technolgy.
Keywords: Responsibility Gap, Ethics, Brain-Computer-Interaction

Abstract:

Brain Computer Interface (BCI) systems allow interaction with the external world for people with disabilities, and in severe cases may become the only means of communication. The P300 potential is the control signal most used for EEG-based BCI. An efficient BCI system should be instead able to understand user's intentions from the ongoing EEG: it has to abstain from doing a selection when the user is engaged in a different activity and it should to increase or decrease its speed of selection depending on current user's attention level. On the contrary classic P300-based BCIs work in synchronous mode, engaging the user in continuous control. We propose an asynchronous BCI based on the introduction of thresholds in the classifier: the thresholds were extracted through a heuristic procedure from a database containing data of periods in which user wishes to exercise its control over the interface and periods in which he was engaged in different tasks. We tested its capabilities for effective environmental monitoring, involving 11 volunteers in three recording sessions. Results show that this BCI can increase bit-rate during control periods while the system has proved very robust in avoiding false negatives when the users are engaged in other tasks

Abstract:

The main purpose of electroencephalography (EEG) based brain computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how a sensorimotor rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with Transcranial Magnetic Stimulation (TMS) and high-density EEG.
MI-based BCI training in naïve participants led to a significant increase in the motor cortical excitability, as revealed by post-training TMS mapping of the hand muscle’s cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the brain functional networks constructed using functional connectivity matrix between scalp electrodes revealed a significant decrease of the global efficiency index for the higher-beta frequency range (22-29 Hz) indicating that the functional network changes its topology with practice of hand grasping MI.
Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus, may render BCI a viable tool for post-stroke rehabilitation.

Abstract:

The aim of this study was to investigate whether users retain satisfactory mental imagery-based brain-computer interface (BCI) control during auditory distraction, and whether there are mental task related differences. Fourteen participants controlled the BCI with four different mental tasks (word association, mental subtraction, spatial navigation, motor imagery) by modulation of EEG frequency bands (ERD/S) in ten 2-hour sessions. “Passive distraction” (ignoring all auditory stimuli presented to the user while performing the mental tasks) led to an increased user performance compared to “active distraction” (reacting to auditory target stimuli by button press) and no distraction condition. Differences between tasks in performance were reflected in the P300 amplitude, latency and reaction time and thus might indicate differences in workload. Our results are encouraging for real-world application as participants succeeded in operating the 4-class BCI during auditory distraction.

"This work is supported by the European ICT Program Project FP7-224631. This paper only reflects the authors' views and funding agencies are not liable for any use that may be made of the information contained herein."

Abstract:

This work presents a hybrid BCI approach where two different input signals (joystick and BCI) are monitored and only one of them is chosen as a control signal at a time. A game-like feedback was used as an application where users could move a car to collect coins and avoid obstacles via either joystick or BCI control. For both types four different quality measures were constantly applied to evaluate the quality of the signal. As soon as the quality dropped below a certain threshold the system would switch to the other control mode and vice versa.

Abstract:

Hybrid Brain-Computer Interfaces (BCI) are representing a recent approach to develop practical BCIs. In such a system disabled users are able to use all their remaining functionalities as control possibilities in parallel with the BCI. Sometimes these people have residual activity of their muscles. Therefore, in the presented hybrid BCI framework we want to explore the parallel usage of electroencephalographic (EEG) and electromyographic (EMG) activity, whereby the control abilities of both channels are fused. Results showed that the participants could achieve a good control of their hybrid BCI independently of their level of muscular fatigue. Thereby the multimodal fusion approach of muscular and brain activity yielded better and more stable performance compared to the single conditions. Even in the case of an increasing muscular fatigue a good control (moderate and graceful degradation of the performance compared to the non-fatigued case) and a smooth handover could be achieved. Therefore, such systems allow the users a very reliable hybrid BCI control although they are getting more and more exhausted or fatigued during the day.

Abstract:

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing
(BCI) out of its infancy and into a phase of relative maturity through many demonstrated
prototypes such as brain-controlled wheelchairs, keyboards, and computer games.
With this proof-of-concept phase in the past, the time is now ripe to focus on the development
of practical BCI technologies that can be brought out of the lab and into real-world
applications. In particular, we focus on the prospect of improving the lives of countless
disabled individuals through a combination of BCI technology with existing assistive technologies
(AT). In pursuit of more practical BCIs for use outside of the lab, in this paper,
we identify four application areas where disabled individuals could greatly benefit from
advancements in BCI technology, namely,“Communication & Control”, “Motor Substitution”,
“Entertainment”, and “Motor Recovery”. We review the current state of the art and
possible future developments, while discussing the main research issues in these four areas.
In particular, we expect the most progress in the development of technologies such
as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’
mental states for BCI reliability and confidence measures, the incorporation of principles in
human-computer interaction (HCI) to improve BCI usability, and the development of novel
BCI technology including better EEG devices.

Abstract:

Brain-Computer Interfaces (BCIs) allow a user to control a computer application by
brain activity as acquired, e.g., by EEG. In the Machine Learning approach, partici-
pants undertake a calibration measurement without feedback to acquire data to train
the BCI system. After the training, the user can try to control a BCI and improve
the operation through some type of feedback. However, not all BCI users are able to
perform sufficiently well during feedback operation. Actually, a non-negligible por-
tion of participants (estimated 15% to 30%) can not control the system (BCI illiteracy
problem). Based on previous experience, we hypothesize that one main difficulty for
a BCI user is the transition from off-line calibration to on-line feedback. In this work
we investigate adaptive machine learning methods to eliminate offline calibration and
analyze the performance of 11 volunteers in a BCI based on the modulation of senso-
rimotor rhythms. We present a sophisticated adaptation scheme that guides the user
from an initial subject-independent classifier operating on simple features to a subject-
optimized state-of-the-art classifier within one session, while the user interacts contin-
uously. Initial runs use supervised techniques for robust co-adaptive learning of user
and machine. Fnal runs use unsupervised adaptation that allow tracking the features
drift during the session and provide as well an unbiased measure of BCI performance.
Using this approach, without off-line calibration measurement, good performance was
obtained by good BCI users (also one novice) after 3-6 minutes of adaptation. More
importantly, the use of machine learning techniques allowed participants who were
unable to achieve successful feedback before to gain significant control with the BCI
system. Additionally, one volunteer 1 without sensory motor idle rhythm peak in the
beginning of the experiment could develop it during the course of the session and use
voluntary modulation of its amplitude to control the feedback application.

Abstract:

Brain–computer interfaces (BCIs) enable paralyzed patients to communicate; however, up to date, no creative expression was possible. The current study investigated the accuracy and user-friendliness of P300-Brain Painting, a new BCI application developed to paint pictures using brain activity only. Two different versions of the P300-Brain Painting application were tested: A colored matrix tested by a group of ALS-patients (n = 3) and healthy participants (n = 10), and a black and white matrix tested by healthy participants (n = 10). The three ALS-patients achieved high accuracies; two of them reaching above 89% accuracy. In healthy subjects, a comparison between the P300-Brain Painting application (colored matrix) and the P300-Spelling application revealed significantly lower accuracy and P300 amplitudes for the P300-Brain Painting application. This drop in accuracy and P300 amplitudes was not found when comparing the P300-Spelling application to an adapted, black and white matrix of the P300-Brain Painting application. By employing a black and white matrix, the accuracy of the P300-Brain Painting application was significantly enhanced and reached the accuracy of the P300-Spelling application. ALS-patients greatly enjoyed P300-Brain Painting and were able to use the application with the same accuracy as healthy subjects. P300-Brain Painting enables paralyzed patients to express themselves creatively and to participate in the prolific society through exhibitions.

Abstract:

An electroencephalographic (EEG) brain-computer interface (BCI) internet browser was designed and evaluated with 10 healthy volunteers and 3 individuals with advanced amyotrophic lateral sclerosis (ALS), all of whom were given tasks to execute on the internet using the browser. Participants with ALS achieved an average accuracy of 73% and a subsequent information transfer rate (ITR) of 8.6 bits/minute and healthy participants with no prior BCI experience over 90% accuracy and an ITR of 14.4 bits/minute. We define additional criteria for unrestricted internet access for evaluation of the presented and future internet browsers, and we provide a review of the existing browsers in the literature. The P300-based browser provides unrestricted access and enables free web surfing for individuals with paralysis.

Abstract:

With this issue of the magazine, we are adding a new feature, "Conversations in BME," in which distinguished academics and researchers discuss a biomedical issue in depth, highlighting pros and cons of different approaches. Our goal for this feature is to promote discussion as a way to facilitate scientific growth in our community and, in particular, among students. It is a pleasure to introduce the guests for this issue: Prof. José del R. Millán, Swiss Federal Institute of Technology, Lausanne, and Prof. Jose M. Carmena, University of California, Berkeley, who discuss how noninvasive and invasive cortical signals can be used to control robotic systems in a successful way and examine the potentials and limits of noninvasive and invasive cortical neural prostheses. Representing excellence in their respective fields, Dr. Millán and Dr. Carmena here share thoughtful ideas for the future of brain–machine interface technology.
--Silvestro Micera

Abstract:

Most P300-based brain-computer interface (BCI) approaches use the visual modality for stimulation. For use with patients suffering from amyotrophic lateral sclerosis (ALS) this might not be the preferable choice because of sight deterioration. Moreover, using a modality different from the visual one minimizes interference with possible visual feedback. Therefore, a multi-class BCI paradigm is proposed that uses spatially distributed, auditory cues. Ten healthy subjects participated in an offline oddball task with the spatial location of the stimuli being a discriminating cue. Experiments were done in free field, with an individual speaker for each location. Different inter-stimulus intervals of 1000 ms, 300 ms and 175 ms were tested. With averaging over multiple repetitions, selection scores went over 90% for most conditions, i.e., in more than 90% of the trials the right location was selected. Two subjects reached a 100% correct score. Corresponding information transfer rates were high, up to an average score of 16.13 bits/minute for the 175 ms condition (best subject 24.20 bits/minute). When presenting the stimuli through a single speaker, thus effectively canceling the spatial properties of the cue, selection scores went down below 70% for most subjects. We conclude that the proposed spatial auditory paradigm is successful for healthy subjects and shows promising results that may lead to a fast BCI that solely relies on the auditory sense.

Abstract:

This paper discusses current clinical applications and possible future uses of brain-computer interfaces (BCIs) as a means for communication, motor control and entertainment. After giving a brief account of the various approaches to direct brain-computer interaction, the paper will address individual, social and ethical implications of BCI technology to extract signals from the brain.
These include reflections on medical and psychosocial benefits and risks, user control, informed consent, autonomy and privacy as well as ethical and social issues implicated in putative future developments with focus on human self understanding and the idea of man. BCI use which involves direct interrelation and mutual interdependence between human brains and technical devices raises anthropological questions concerning self-perception and the technicalization of the human body.

Abstract:

Brain-Computer Interfaces (BCIs) allow a user to control a computer application
by brain activity as acquired, e.g., by EEG. One of the biggest challenges in BCI research
is to understand and solve the problem of “BCI Illiteracy”, which is that BCI control does
not work for a non-negligible portion of users (estimated 15% to 30%). Here, we investigate
the illiteracy problem in BCI systems which are based on the modulation of sensorimotor
rhythms. In this paper, a sophisticated adaptation scheme is presented which guides the user
from an initial subject-independent classifier that operates on simple features to a subject-optimized
state-of-the-art classifier within one session while the user interacts the whole
time with the same feedback application and does not need to care about what is going
on behind the scenes. While initial runs use supervised adaptation methods for robust coadaptive
learning of user and machine, final runs use unsupervised adaptation and therefore
provide an unbiased measure of BCI performance. Using this approach, which does not
involve any offline calibration measurement, good performance was obtained by good BCI
participants (also one novice) after 3-6 minutes of adaptation. More importantly, the use of
machine learning techniques allowed users who were unable to achieve successful feedback
before to gain significant control over the BCI system. In particular, one participant had no
peak of the sensory motor idle rhythm in the beginning of the experiment, but could develop
such peak during the course of the session (and use voluntary modulation of its amplitude
to control the feedback application).

Abstract:

Several feature types have been used with EEG-based Brain Computer Interfaces.
Among the most popular are logarithmic band-power estimates with more or less
subject-specific optimization of the frequency bands. In this paper we introduce
a feature called Time Domain Parameter, that is defined by the generalization of
the Hjorth parameters. Time Domain Parameters are studied under two different
conditions. The first setting is defined when no data from a subject is available.
In this condition our results show that Time Domain Parameters outperform all
band power features tested with all spatial filters applied. The second setting is
the transition from calibration (no feedback) to feedback, in which the frequency
content of the signals can change for some subjects. We compare Time Domain
Parameters with logarithmic band power in subject-specific bands and show that
these features are advantageous in this situation as well.

Abstract:

Designing user interfaces which can cope with unconventional control properties is challenging, and conventional interface design techniques are of little help. This paper examines how interactions can be designed to explicitly take into account the uncertainty and dynamics of control inputs. In particular, the asymmetry of feedback and control channels is highlighted as a key design constraint, which is especially obvious in current noninvasive brain-computer interfaces. Brain-computer interfaces (BCIs) are systems capable of decoding neural activity in real time, thereby allowing a computer application to be directly controlled by thought. BCIs, however have totally different signal properties than most conventional interaction devices. Bandwidth is very limited and there are comparatively long and unpredictable delays. Such interfaces cannot simply be treated as unwieldy
mice. In this respect they are an example of a growing field of sensor-based interfaces which have unorthodox control properties. As a concrete example, we present the text entry application ‘Hex-o-Spell’, controlled via motor-imagery based electroencephalography (EEG).
The system utilises the high visual display bandwidth to help compensate for the limited control signals, where the timing of the state changes encodes most of the information. We present results showing the comparatively high performance of this interface, with entry rates exceeding seven characters per minute.

Abstract:

One basic rationale for Brain-Computer Interfaces (BCIs) is to enable severely paretic persons to interact again with their environment. While advancements of BCI techniques are significant in healthy volunteers, there are only few studies that investigated the applicability of BCIs in patients afflicted by spinal cord injury (SCI), and the spatiotemporal characteristics of sensorimotor cortical event-related potentials in these subjects is largely unknown. In this study we evaluated the feasibility and performance rate of the Berlin Brain-Computer Interface in a first-session setting in high-level SCI with tetraplegia.
In a one-dimensional online feedback four out of seven subjects were were able to control the BCI via attempted movements with their plegic limbs during the first session with a mean accuracy of 75%. Interestingly, subjects achieved an even higher performance rate of about 83 % (range: 74-95%) in a ‘cursor off’ mode, in which the feedback signal was provided only at the end of each trial. In contrast to a previous SCI-BCI study, topographical and temporal patterns of event related desynchronizations (ERDs) in the µ- and beta-frequency bands were well distinguishable in these patients.

Abstract:

The analysis of EEG signals for the use in BCI systems and for mental state monitoring applications is often impeded by artifacts caused by muscular activity or external technical sources. A promising approach for the reduction or removal of artifacts is based on methods of Blind Source Separation (BSS), which transform the original EEG signal into independent source components. In order to avoid the time-consuming hand rating of sources into artifactual and non-artifactual components, an automated method for their classification is proposed. Applying state of the art machine learning algorithms and nonlinear classification with a Support Vector Machine (SVM), the automated method shows a high level of agreement (90.5%) on unseen data with ratings of human experts.

Abstract:

Most P300 BCI approaches use the visual modality for stimulation. For use with ALS patients this might not be the preferable choice because of sight deterioration. Moreover, using a modality different from the visual one minimizes interference with possible visual feedback. Therefore, a multi-class brain-computer interface paradigm is proposed that uses spatially distributed, auditive cues. Ten subjects participated in an offline oddball task with the spatial location of the stimuli being a discriminating cue. Different inter-stimulus intervals of 1000 ms, 300 ms and 175 ms were tested. With averaging over multiple classifier outputs, selection scores went over 90% for most conditions; two subjects reached a 100% correct score. Corresponding information transfer rates were high, up to an average optimal score of 20.99 bits/minute for the 175 ms condition (best subject 37.80 bits/minute). We conclude that the proposed paradigm is successful for healthy subjects and shows promising results that may lead to a fast BCI that solely relies on the auditory sense.

Abstract:

Objective: This study examined the effect of motivation as a possible psychological influencing variable
on P300 amplitude and performance in a brain–computer interface (BCI) controlled by event-related
potentials (ERP).
Methods: Participants were instructed to copy spell a sentence by attending to cells of a randomly flashing
7 x 7 matrix. Motivation was manipulated by monetary reward. In two experimental groups participants
received 25 (N = 11) or 50 (N = 11) Euro cent for each correctly selected character; the control group
(N = 11) was not rewarded. BCI performance was defined as the overall percentage of correctly selected
characters (correct response rate = CRR).
Results: Participants performed at an average of 99%. At electrode location Cz the P300 amplitude was
positively correlated to self-rated motivation. The P300 amplitude of the most motivated participants
was significantly higher than that of the least motivated participants. Highly motivated participants were
able to communicate correctly faster with the ERP-BCI than less motivated participants.
Conclusions: Motivation modulates the P300 amplitude in an ERP-BCI.
Significance: Motivation may contribute to variance in BCI performance and should be monitored in BCI
settings.

Abstract:

The objective of this work is to provide a Brain Computer Interface (BCI) – driven rehabilitative device, which incorporates Functional Electrical Stimulation (FES), for the rehabilitation of the upper limb in stroke patients. The proposed system is designed in agreement with the neurorehabilitation experts and is meant to comply with the current rehabilitation principles. The main innovative characteristic of the device would be the ―hybrid‖ control of the FES system, meant to reinforce both motor intent (as recorded by EEG) and the associated residual motor ability (electromyograpy, EMG). Furthermore, FES would provide for an enriched sensorimotor feedback with the aim of boosting motor scheme re-learning by allowing voluntary access to the stroke affected hand.

Abstract:

A novel way of using Brain Computer Interface (BCI) has emerged, which proposes to use these systems to monitor users mental states. In this work we would investigate if the users’ morning/evening activity preference could be related to BCI performances. Preliminary findings highlighted a correlation between chronotype and the latter.

Abstract:

This work presents the fusion of a gaze independent P300-based Brain Computer Interface with an asynchronous classifier. The latter can automatically adapt the speed of selection depending on the user psychophysical state and avoid misclassifications when the signal is not reliable enough. Preliminary findings suggest that these features can significantly improve communication efficiency in terms of classification accuracy and errors recovery.

Abstract:

In this talk I like to (1) introduce some information about the technology of brain-computer-interfaces (BCIs) and (2) use them as models for philosophical anthropology in connection with the increasing medialization of human-world-interaction. I will present some speculations as well as some data concerning the ability of human beings to live and to organize their world without any motor activities.
(1) For about the last 20 years brain-computer-interfaces have been investigated with increasing intensity and have in principle shown their potential to be useful tools in diagnostics, rehabilitation and assistive technology. The central promise of BCI technology is enabling severely impaired people. Successful applications are for instance communication devices enabling locked-in patients in staying in contact with their environment, or prostheses enabling paralysed people in reaching and grasping. A BCI usually consists of three parts: a) a component acquiring signals coming from the brain, b) a unit that amplifies and interprets those signals, and c) an ‘actuator’ that is (partly) controlled and steered by the interpreted and modified signals. In applications designed for prospective widespread and/or everyday use, technologies using electrical signals acquired by surface electrodes are currently the core solutions because of their portability, easiness of use, and relative cheapness.
(2) As interfaces not requiring any motor activity from the user’s side, BCIs connected to electronic media or to tele-presence-robots might be taken as radical illustrations and models for an ultimate destination of the modern tendency of medialization. While technology skeptics would argue that this tendency contradicts essential human features and leads to a loss of quality of experiences, for the trans-humanist technologies like BCIs would be only first but necessary steps in human technological self-evolution towards minds running on hardware more durable than biological bodies. An anthropologically interesting question is whether we can really think of living ‘full’ human lives by organizing our world of experiences and communication on the basis of thought-activity only. As Heidegger has shown, our regular all-day life is characterized by the ‘invisibility’ or transparency of the things we work with. Therefore, a necessary requirement for any technology mediating the way human beings approach the world is that they ‘disappear’ in use, i.e. become ‘invisible’. Does brain-computer-interfaces have this potential? Preliminary qualitative studies give some evidence that they have. Motor impaired users taking part in a semi-structured interview study declared that, after training, they were able ‘just to do’ what they wanted to do via the BCI. One might, modestly, conclude that human life has enough plasticity to be engaged in a totally technologically mediated world without losing its ‘essence’.

Abstract:

Motor Imagery (MI) was proposed to enhance motor recovery after stroke. It has been suggested that EEG-based Brain Computer Interfaces (BCI) operated by MI can provide monitoring and reinforcement of such task-specific training. A BCI rehabilitation device was specifically developed in our laboratory for recovery of hand function after stroke. Here we report the validation of this device, conducted in accordance with the guidelines to demonstrate the efficacy of novel rehabilitation interventions. The validation procedure of such BCI application was designed as a rehabilitation stage II pilot trial with two endpoints: primarily, to demonstrate the efficacy this BCI-based MI training as an adjunctive intervention in post-stroke rehabilitation and, secondarily to foster the transfer of BCI technology into clinical practice.

Abstract:

Objectives: To evaluate the efficacy of a Brain Computer Interface (BCI) device that was specifically designed for recovery of hand function after stroke.
Materials: An EEG-based BCI device based on motor imagery (MI) was developed to provide monitoring and reinforcement of task-specific EEG patterns. In the prototype setting, an ecological feedback (“virtual hand”) is provided to the patient who is asked to perform MI of hand movements, while the therapist is allowed to directly monitor the patient’s EEG activity in terms of desynchronization on motor related electrodes and frequencies.
Method: 20 stroke patients were consecutively recruited upon their admission to the Fondazione Santa Lucia clinic for post-stroke rehabilitation treatment, and randomly assigned to the BCI-supported MI group (BCI) or MI control group (CTRL). Both groups performed a one-month MI training as an add on intervention to their conventional therapy. The primary outcome measure was the arm section of the Fugl-Meyer scale. A minimal clinically important difference (MCID) for this scale was described to 7 points. Secondary outcome measures were European Stroke Scale and the arm MRC scale for muscle strength. Finally, we quantified the parameter “effectiveness” for all the adopted functional scales, reflecting the proportion of potential improvement that could be achieved after the intervention.
Results: No significant group differences at baseline were found on primary and secondary outcome measures. Regarding the primary outcome measure, a mean change of 9 was observed in the BCI group, exceeding the MCID of 7, with respect to an improvement of 5 observed in CTRL control group. The effectiveness of outcomes scale scores showed a clear trend of higher improvement for the BCI group Vs CTRL group, though statistically significant difference could be highlighted for MRC only.
Discussion: MI was proposed to enhance motor recovery after stroke (1). EEG-based BCI operated by MI can provide monitoring and reinforcement of such task-specific training (2). The study was designed to evaluate the efficacy of the EEG-based BCI generated neurofeedback on arm motor recovery and was conducted in accordance with the guidelines to demonstrate the efficacy of novel rehabilitation interventions (3).
Conclusion: To our knowledge this is the first randomized controlled trial to evaluate the efficacy of BCI-supported MI for motor recovery after stroke. Our findings support the efficacy of this approach.
Acknowledgements: This work is supported by the European ICT Programme Project FP7-224631.
REFERENCES
1. Ietswaart M, Johnston M, Dijkerman HC, Joice S, Scott CL, MacWalter RS, Hamilton SJ. Mental practice with motor imagery in stroke recovery: randomized controlled trial of efficacy. Brain. 2011 May; 134(Pt 5):1373-86. Epub 2011 Apr 22.
2. Daly JJ, Wolpaw JR. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 2008 Nov;7(11):1032-43. Epub 2008 Oct 2.
3. B. H. Dobkin. Progressive staging of pilot studies to improve phase III trials for motor interventions. Neurorehabilitation and Neural Repair, 23(3):197–206, Apr. 2009. PMID: 19240197.

Abstract:

The prospect of controlling devices merely by the power of one’s thoughts is compelling, especially for assistive technology applications. In the accompanying video, we show how we have strived to push brain–computer interface (BCI) technology out of the lab and into the real world, while simultaneously moving away from testing solely with healthy subjects to undertaking trials with patients and potential end–users. We describe the evolution of the motor imagery based BCI, which has resulted in a major milestone: the first patient trial of a motor imagery based BCI controlled wheelchair.

Abstract:

One fundamental limitation of EEG-based braincomputer
interfaces is the time needed to calibrate the system
prior to the detection of signals, due to the wide variety of issues
affecting the EEG measurements. For event-related potentials
(ERP), one of these sources of variability is the application
performed: Protocols with different cognitive workloads might
yield to different latencies of the ERPs. In this sense, it is still
not clear the effect that these latency variations have on the
single-trial classification. This work studies the differences in
the latencies of error potentials across three experiments with
increasing cognitive workloads. A delay-correction algorithm
based on the cross-correlation of the averaged signals is
presented, and tested with a single-trial classification of the
signals. The results showed that latency variations exist between
different protocols, and that it is feasible to re-use data from
previous experiments to calibrate a classifier able to detect the
signals of a new experiment, thus reducing the calibration time.

Abstract:

Neural signatures of humans’ movement intention
can be exploited by future neuroprosthesis. We propose a
method for detecting self-paced upper limb movement intention
from brain signals acquired with both invasive and noninvasive
methods. In the first study with scalp electroencephalograph
(EEG) signals from healthy controls, we report
single trial detection of movement intention using movementrelated
potentials (MRPs) in a frequency range between 0.1 to
1 Hz. Movement intention can be detected above chance level
(p<0.05) on average 460 ms before the movement onset with
low detection rate during the non-movement intention period.
Using intracranial EEG (iEEG) from one epileptic subject, we
detect movement intention as early as 1500 ms before movement
onset with accuracy above 90% using electrodes implanted in
the bilateral supplementary motor area (SMA). The coherent
results obtained with non-invasive and invasive method and its
generalization capabilities across different days of recording,
strengthened the theory that self-paced movement intention can
be detected before movement initiation for the advancement in
robot-assisted neurorehabilitation.

Abstract:

Giving direct, continuous feedback on a brain state is common practice in motor imagery based brain-computer interfaces (BCI), but has not been reported for BCIs based on event-related potentials (ERP), where feedback is only given once after a sequence of stimuli. Potentially, direct feedback could allow the user to adjust his strategy during a running trial to obtain the required response.

In order to test the usefulness of such feedback, directionally congruent vibrotactile feedback was given during an online auditory BCI experiment. Users received either no feedback, short feedback pulses or continuous feedback. The feedback conditions showed reduced performance both on a behavioral task and in terms of classification accuracy. Several explanations are discussed that give interesting starting points for further research on this topic.

Abstract:

Introduction: In the field of Brain-Computer Interfaces (BCI), the original two-class oddball paradigm has been extended to multiple stimuli with balanced probabilities and random presentation sequences. Exploiting the differences between standard and deviant ERP responses, these multi-class paradigms are suitable for communication and control.

Methods: The present study investigates the effect of giving up the randomness of stimulation sequences in favor of a repeated, predictable pattern. Data of healthy subjects (n=10)
who performed a single session with a 6-class spatial auditory ERP paradigm were analyzed offline. Their auditory evoked potentials (AEP) resulting from the potentially simpler task
(using fixed sequences) are compared with the AEP evoked by pseudo-randomized stimulation sequences.

Results: Class-discriminative EEG responses between target and non-target stimuli were observed for both conditions. The binary classification error estimated for standard epochs of
was comparable for both conditions (random: 24%, fixed: 25%). Expanding the standard epochs to include pre-stimulus intervals, we found that the regular structure of the fixed sequence can be exploited. Compared to the standard epoch, the MSE improves by 7%, while in the random condition an improvement could not be observed.

Abstract:

In this paper we propose a method to modulate the level of assistance provided by a shared controller, not only given the environmental context, but also according to the context of the user’s current behaviour. We show that the enhanced situational context can be adequately captured by using online performance metrics (such as those more usually found in the evaluation of shared control systems). The resultant controller not only allows the user to perform better in the primary task (like many shared control systems), but has also has increased the level of user acceptance, due to the personalised dynamics of the control policy.

Abstract:

For most ERP-based BCI paradigms, stimuli are presented with a pre-defined and constant speed. In order to boost BCI performance by optimizing the parameters of stimulation, this offline study investigates the impact of the stimulus onset asynchrony (SOA) on ERPs and the resulting classification accuracy. The SOA represents the time between the onsets of two consecutive stimuli. Therefore, a simple auditory oddball paradigm was tested in 14 SOA conditions with a SOA between 50 ms and 1000 ms. Based on an ERP analysis, the BCI performance (quantified by the Information Transfer Rate) was simulated. A great variability in the simulated BCI performance was observed within subjects (N=11). This indicates a potential increase in BCI performance (> 1.6
bits/min), if the SOA is specified for each subject individually.

Abstract:

For most ERP-based BCI paradigms, the stimuli are presented with a predefined and
constant speed. Based on the idea to boost BCI performance by optimizing the parameters of
stimulation, this offline study investigates the impact of the stimulus onset asynchrony (SOA)
on ERPs and the resulting classification accuracy. Therefore, a simple auditory oddball
paradigm was tested in eight SOA conditions. It was found that the binary classication
accuracy is not directly correlated to the SOA. A variability within subjects (n = 5) was
observed, which indicates a potential increase in BCI performance, if the SOA is specied for
each subject individually.

Abstract:

Neuroergonomic design of Brain-Computer Interface (BCI) experiments can be realized
as a data driven optimization of stimuli. The goal of this process is to increase the number
and information content of class-discriminant features of the EEG for the BCI task at hand.
While existing electrophysiological literature indicated the influence of confounding variables
on e.g. P300 latency and amplitude by group studies and grand average statistics, the BCI
performance can be boosted by a large amount when optimized stimuli are explored and
designed for individual users and then used in single-trials. The potential of this design
principle is shown in an offline analysis for the example of a visual (n = 8) and an auditory
(n = 5) ERP study with healthy subjects, where the optimization of stimulus parameters
leads to both a decrease in classication errors and an increase in speed.

Abstract:

Neuroprostheses can enable motor activities that would otherwise be impossible for some disabled users, thus expanding their capabilities of daily activities. In the present experiment, healthy subjects performed a center out reaching task. Our goal was to detect their hand/arm direction from non-invasive EEG measures. Preliminary, we found that the hand direction can be successfully inferred from the raw EEG.

Abstract:

In this study we investigated the eect of motivation on performance when using a Brain-
Computer Interface (BCI) based on sensorimotor rhythms (SMR). After pooling the data
acquired with four dierent SMR-BCI protocols in one sample of N=41 participants, we found
a positive correlation between the motivational components "challenge" and "incompetence
fear" and accuracy in percent correct responses. As motivation seems to have a positive eect
on SMR-BCI performance, we recommend to enhance motivation if possible and to monitor
motivation in BCI settings.

This work is supported by the European ICT
Programme Project FP7-224631. This paper only re
ects the authors' views and funding agencies
are not liable for any use that may be made of the information contained herein.

Abstract:

Motor imagery (MI) brain-computer interfaces
(BCIs) translate a subject’s motor intention to a command
signal. Most MI BCIs use power features in the mu or beta
rhythms, while several results have been reported using a
measure of phase synchrony, the phase-locking value (PLV). In
this study, we investigated the performance of various phasebased
features, including instantaneous phase difference (IPD)
and PLV, for control of a MI BCI. Patterns of phase synchrony
differentially appear over the motor cortices and between the
primary motor cortex (M1) and supplementary motor area
(SMA) during MI. Offline results, along with preliminary online
sessions, indicate that IPD serves as a robust control signal for
differentiating between MI classes, and that the phase relations
between channels are relatively stable over several months.
Offline and online trial-level classification accuracies based on
IPD ranged from 84% to 99%, whereas the performance for the
corresponding amplitude features ranged from 70% to 100%.

Abstract:

Introduction: Electroencephalographic (EEG)-based BCI (Brain Computer Interface)
technology operated via motor imagery (MI) appears a unique option to promote motor
recovery after stroke. With the aim of developing a specific BCI system for stroke
rehabilitation of the upper limb we performed an extensive neurophysiological screening of
21 stroke patients consecutively enrolled from a rehabilitation clinic. A subgroup of patients
underw ent a one- month BCI training with a system developed specifically and installed in the
rehabilitation hospital ward.
Methods: EEG and Transcranial Magnetic Stimulation (TMS) data w ere collected from 21
monolateral stroke patients during MI of simple hand movements. Stroke impairment was
assessed by means of clinical and functional scales. In a subgroup of 6 patients, clinical and
neurophysiological measurements were repeated after a one- month MI-based BCI training
and compared to a control group.
Results: All patients were able to perform MI of affected hand (AH) as revealed by the EEG
desynchronization of the alpha and beta rhythms over the ipsilesional scalp electrodes and
by the increase in motor evoked potential measured w ith TMS. During the BCI training,
stroke patients w ere able to control the movement of a visual representation of their own AH
by MI.
Conclusions: The preliminary results of the BCI training with stroke patients are encouraging:
it has been shown that EEG features related to the MI task can be collected from the affected
hemisphere of stroke patients and successfully adopted to control a BCI system.
Acknow ledgements: "This work is supported by the European ICT Programme Project FP7-
224631."

Abstract:

Brain Computer Interfaces (BCI) provide a non-muscular communication channel for peo-
ple with severe motor impairment. The most commonly used BCI for communication is the
so called P300-Speller that received its name from the event-related potential (ERP) P300
which is elicited by the speller (oddball) paradigm. Several researchers reported that it is not
only the P300 but also other ERPs that are classied with this type of BCI. This study thus,
aims at contributing to the discussion by assessing the ERPs which contribute most to classi-
cation in a large sample size of N=51 participants. Our results indicate that almost 30% of
all participants reached highest determination coeffcients with the N200, not with the P300.
Furthermore it is often a combination of both potentials that is picked up for classication.
Therefore the "P300"-BCI is truely a BCI that is controlled by various ERPs, especially the
N200 and P300

Abstract:

Control of brain-computer interfaces (BCI) is achieved either by voluntary regulation of
EEG activity, e.g. sensorimotor rhythms (SMR), or by eliciting well dened responses of the
brain to stimulation, e.g. the P300 event-related potential in an oddball paradigm. Many
subjects, patients with neurological diseases as well as healthy volunteers, achieve high a level of accuracy. However, in a considerable amount of participants, no stimulus or task related EEG pattern can be detected by the BCI or the bitrate is not high enough to allow for mean- ingful and satisfactory communication despite statistically signicant accuracy [1]. Thus, recently, few studies were dedicated to elucidate factors that contribute to BCI performance and to dene predictors of BCI control. Available results indicate four aspects: (1) individual characteristics of the BCI user, (2) characteristics of the BCI, (3) type of feedback and instruction, and (4) the BCI-controlled application. An integration of these aspects leads to a neuro-bio-psychological, data analytical, and ergonomical model of BCI-control.

Abstract:

This paper compares classification accuracies of feature extraction methods (FEMs) as
used in sensory motor rhythm (SMR) based Brain-Computer Interfaces (BCIs). Features
were extracted offline from 9 subjects and classified with linear discriminant analysis. The
following FEMs were compared: adaptive autoregressive parameters, band power, phase lock-
ing value, time domain parameters, and Hjorth parameters. FEM parameters were optimized
individually with a genetic algorithm in advance. In summary, time domain parameters com-
bined with a bipolar spatial filter yielded the best classification accuracies.

Abstract:

Introduction: Motor impairment after stroke is related to altered brain activation, like reduced activation of ipsilesional motor cortical areas. A change of these alterations back to normal is related to good rehabilitation outcome with motor function recovery. Aim of this study was to investigate if this relationship between motor impairment and brain activation is reflected in the event-related desynchronization (ERD) of motor cortical areas.
Methods: EEG was recorded from 21 positions covering motor cortex in seven subacute subcortical stroke patients with left hand hemiparesis. The participant’s task was executing cue-guided grasping and extension of affected/unaffected hand for 4s (60 trials each hand). ERD was calculated referenced to the intertrial interval (1.5s), where patients stayed at rest. Stroke impairment was measured by European Stroke Scale (ESS), Medical Research Council Scale for muscle strength (MRC, upper limb) and Modified Ashworth Scale for spasticity (MAS). These measures were then correlated with ERD values.
Results: Between 8 and 20 Hz significant correlations between MRC, MAS and right centroparietal ERD were found. The correlation between ERD and MRC was highly negative (r = -0.76 - -0.90; higher MRC values/less motor impairment = lower ERD values/stronger ERD). The correlation between ERD and MAS was highly positive (r = 0.77 – 0.95; higher MAS values/stronger spastic = higher ERD values/weaker or no ERD).
Conclusions: The results are in line with fMRI findings and show that higher motor impairment after stroke correlates with reduced ipsilesional brain activation.

Acknowledgements: "This work is supported by the European ICT Programme Project FP7-224631."

Abstract:

During recent years there has been a growing interest in Brain Computer Interface (BCI) systems as an alternative means of interaction with the external world for people with severe disabilities. The use of P300 event-related potentials as control feature allows users to choose between various options (letters or icons) requiring a very short training phase. The aim of this work is to improve performances and flexibility of P300 based BCIs. An efficient BCI system should be able to understand user's intentions from the ongoing EEG, abstaining from doing a selection when the user is engaged in a different activity, and changing its selection speed depending on current user's attention level. Our self-paced system addresses all these issues representing an important step beyond the classical synchronous P300 BCI that forces the user in a continuous control task. Experimentation has been performed on ten volunteers acting on a BCI-controlled domestic environment in order to demonstrate the potential usability of BCI systems in everyday life. Results show that the self-paced BCI increases information transfer rate with respect to the synchronous one, being very robust, at the same time, in avoiding false negatives when the user is not engaged in a control task

Abstract:

One important source of performance degradation in BCIs is bias towards one of the mental classes. Recent literature has focused on the general problem of classification accuracy drop, identifying non-stationarity as the generating factor, thus leading to several classifier adaptation approaches suggested as of today. In this work, we explicitly focus on bias elimination, demonstrating that the problem has two separate components, one related to non-stationarity and another one attributed to the nature of the feature distributions and the assumptions made by the classification methods. We propose a cued recalibration protocol including a supervised adaptation method and a novel framework for unbiased classification with a modified, unbiased Linear Discriminant Analysis classifier. Preliminary results show that our protocol can assist the subject to achieve quickly accurate and unbiased control of
the BCI.

Abstract:

The consequences of a spinal cord injury (SCI) are tremendous for the patients. The loss of motor functions, especially of grasping, leads to a dramatic decrease in quality of life. With the help of neuroprostheses, the grasp function can be substantially improved in cervical SCI patients. Nowadays, systems for grasp restoration can only be used by patients with preserved voluntary shoulder and elbow function. In patients with lesions above the 5th vertebra, not only the voluntary movements of the elbow are restricted, but also the overall number of preserved movements available for control purposes decreases. In this work, a new method for the non-invasive use of a Brain-Computer Interface (BCI) for the control of the hand and elbow function is presented.

Abstract:

Persons with movement disabilities can use a wide range of assistive devices (ADs). The set of ADs ranges from simple switches connected to a remote controller to complex sensors (e.g., mouth mouse) attached to a computer and to eye tracking systems. All of these systems work very well after being adjusted individually for each person. However, there are still situations where the systems do not work properly, e.g., when residual muscles become fatigued or users have such severe disabilities that no movement is possible. In such situations, a Brain-Computer Interface (BCI) might be the only available option, since it uses brain signals (usually the electroencephalogram, EEG) for control without requiring any movement whatsoever.
BCIs are systems that establish a direct connection between the human brain and a computer, thus providing an additional communication channel. As noted, some people use a BCI because their disabilities make it impossible to use any interface requiring movement. BCIs can also be used to control neuroprostheses in patients suffering from a high spinal cord injury. After 20 years of research and development, Brain-Computer Interface technology is ready to leave the lab and to be used in practical applications in real world settings such as homes or hospitals.

A BCI could replace an existing AD. However, it would be even better to couple the BCI with the existing AD and develop a new system called a hybrid BCI (hBCI) [1,2]. Ideally, an hBCI should let the user extend the types of inputs available to an assistive technology, or choose not to use the BCI at all. The hBCI might decide which input channel(s) offer(s) the most reliable signal(s) and switch between input channels to improve information transfer rate, usability, or other factors, or could instead fuse various input channels.
In the past as well as in the present, various studies about hBCIs have been conducted, but they have in common that they all combine a BCI with another BCI (using different brain signals) or a BCI with another biosignal. In general, a hBCI does not depend on the BCI as an input. Instead, it simply allows the BCI to function as an input channel when the BCI could increase the overall performance for that user. The hBCI can perform fusion to switch between multiple inputs, but (depending on the configuration) can also weight signals and combine/fuse them to achieve one control signal from a combination of multiple inputs.

The principle of such an hBCI can be explained as following: in addition to the EEG-based BCI, there are other input and control signals possible. These include other biosignals as well as signals from manual controls such as from ADs (e.g., mouth mouse, push buttons, …). A “fusion” generates a new control signal out of all inputs. Besides a quality check (e.g., artifact detection), those signals will be weighted and fused to a control signal, or the most reliable one will be chosen. Followed by the so-called “shared control”, sensor signals from the application (neuroprosthesis, software, assistive robot) will also be included and used to generate an accurate final control signal.
One major goal is to bring the BCI technology to a level where it can be used in a maximum number of scenarios in a simple way. To achieve this, the hBCI must be able to operate reliably for long periods, recognizing and adapting to changes as it does so. Reaching this goal requires that many different subsystems in the hBCI are able to work together. Examples include standard BCI processing, post processing (error potentials), mental state recognition (fatigue), artifact detection, adaptation of classifiers, and surveillance of signal quality (including EEG signals and those from additional input devices).

Abstract:

Brain-Computer Interfaces (BCIs) are no longer only used by healthy volunteers (mostly students) under artificially controlled conditions in laboratory environments, but by patients controlling real applications at their homes, or by healthy users as an alternative input device for playing computer games, but definitely without any BCI experts around. But, are the BCI technologies and the field mature enough for addressing these requirements? In other words, is BCI technology still depending on expert knowledge and monitoring?
In this work, we summarize the experiences gained and the lessons learned while transferring our BCI technology from the lab to the user’s home. In our case we started with naive patients (not healthy users) who first performed BCI training and then used their BCI to evaluate several BCI controlled prototypes (either a writing application for communication or a robotic tele-presence platform). Rather than presenting the technical description about these setups, we will focus on the experiences and challenges that we encountered. As mentioned, our idea was that the users (together with cares) are capable of operating autonomously the BCI, from training to control of the devices, while BCI experts are just available to give trouble shooting advice (if needed at all), like a telephone support hot-line.
Besides the positive experiences and the promising results we have gained with our patients, we have to acknowledge that a lot of work is still needed. The lessons we learned range from (i) pure BCI issues (technical and handling), to (ii) common communication problems between different people involved, and (iii) lessons encountered while controlling the applications. In our case, although we tried to hide the complexity of the BCI and of the prototype applications, our system is not ready to be used alone at the user's place (likewise this is also valid for the whole community). This leads to the following question: How mature does the technology have to be before we can give it away? Furthermore, the difference between the outcome of a successful BCI training (which mostly seeks to develop intentional control) and the needs of the applications/games (which mostly requires non-intentional control) became obvious, which asks for other interaction and analysis approaches.
Nevertheless the points raised are very general and will be faced similarly by other groups, once they move on to bring the BCI technology to the end-user (healthy or disabled).

Abstract:

Brain-Computer Interfaces (BCIs) are no longer only used by healthy subjects under control conditions in laboratory environments, but by patients controlling applications at their homes, without the BCI experts around. But are the technology and the field mature enough for this? In this work, we want to summarize the experiences gained and the lessons we learned while transferring BCI technologies from the lab to the user's home. These lessons range from pure BCI issues (technical and handling), to common communication problems between different people involved, and lessons encountered while controlling the applications. The points raised are very general and will be faced similarly by other groups, if they move on to bring the BCI technology to the end-user.

Abstract:

For people with severe physical disabilities, low resolution input devices, such as buttons, sip and puff switches and brain–computer interfaces provide an opportunity to interact with the world. However, it can be difficult to control assistive technology, such as wheelchairs, tele–presence robots and robotic arms, when you have only a limited number of commands available and/or a lack of temporal precision in issuing such commands. These limitations can be overcome by employing shared control techniques, whereby the system assists the user in performing the desired task. In this study we compare the use of a simple discrete shared control policy with a more dynamic proportional shared control policy. We evaluate both approaches on a wheelchair that is only operated by two temporally–constrained discrete buttons. The experiments were performed in two different realistic indoor scenarios: an open–plan, spacious environment and a smaller, more cluttered office environment. A total of 10 healthy participants took part in this study.

Abstract:

In this paper we present the first results of users
with disabilities in mentally controlling a telepresence robot, a
rather complex task as the robot is continuously moving and
the user must control it for a long period of time (over 6
minutes) to go along the whole path. These two users drove
the telepresence robot from their clinic more than 100 km
away. Remarkably, although the patients had never visited
the location where the telepresence robot was operating, they
achieve similar performances to a group of four healthy users
who were familiar with the environment. In particular, the
experimental results reported in this paper demonstrate the
benefits of shared control for brain-controlled telepresence
robots. It allows all subjects (including novel BMI subjects as
our users with disabilities) to complete a complex task in similar
time and with similar number of commands to those required
by manual control.

Abstract:

This paper discusses a time-dependent classification approach for single trial recognition of spatial covert visual attention for Brain–Computer Interface. Covert visual attention is a natural and intuitive mental task that does not require any external stimulation. The possibility to recognize it from single trials is essential for a future online close-loop BCI. Experimental results indicate the feasibility of the proposed approach and its high performance in an offline study. We achieved an accuracy of 84.1±8.9 % with a rejection of 6.5±6.6 % averaged across subjects.

Abstract:

The aim of this project is a systematic and critical analysis of the studies exploring different interaction modalities in Brain Computer Interfaces (BCIs) for communication.
BCIs measure signals related to specific brain activity and translate them into device control signals. This technology can be used to provide users with locked-in syndrome (e.g. late stage Amyotrophic Lateral Sclerosis, ALS) with an assistive device that does not rely on muscular contraction. Several studies about BCI explored mental tasks and paradigms using visual modality. Considering that in ALS patients the oculomotor control can deteriorate and also the potential users with other diseases could have impaired visual function, in the last years, tactile and auditory modality have been investigated to find alternative BCI independent of vision.
The aim of this review is to survey the cognitive and neural basis of the tasks explored to control the auditory BCI systems to better understand their limits and potentials applications, toward a real user centered approach helping the scientific community to move the BCIs from the laboratory to user's houses.

Abstract:

In this study a P300 based prototype for communication and control was evaluated by three Acquired Brain Injury (ABI) users. The prototype was developed by merging an already existing assistive technology commercial solution (Qualiworld) with a BCI system and it allows the user to access to text entry and internet applications. The three ABI users were challenged with four complex tasks and the results revealed good performance levels in stan-
dard communication and internet tasks (mean=73.79%, SD=9.4). Two end-users completed the four tasks and reached a performance of respectively 69% and 76% on average. One of the end-users did not nish one of the tasks (internet task) and the mean of accuracy for the three completed tasks was 73%.

Abstract:

Brain-computer interfaces based on event-related potentials face a trade-off between the speed and accuracy of the system. This trade-off is generally dealt with by finding a number of stimuli that gave a good result on the calibration data. We show here that this method is sub optimal and only significantly increases the performance in one out of five datasets. Several alternative methods have been described in literature, and we test the generalization of four of them. One method, called rank diff, significantly increased the performance over all datasets. This is important, as it shows that 1) one should be cautious when reporting the potential performance of a BCI based on post-hoc offline performance curves and 2) simple methods are available that do boost performance.

Abstract:

The event related potentials are commonly used to control EEG-based Brain Computer
Interface (BCI) systems. Several evidences seem to converge on the fact that the usage (i.e.
the practice) of BCIs based on P300 paradigm would lead to a overtime stable and even
higher level of subject's performance possibly related to a more robust features (e.g higher
P300 amplitude). In this study we investigate whether practicing a P300-based BCI task
would results "per se" in an increase of subject's performance along with session repetition.
The offline analysis of task accuracy, ERP amplitudes and reaction times was conducted on a
data set obtained from six healthy naive subject exposed to a P300-based BCI usage. Results
showed that both the subjects' R-Square (R2) index related to the amplitude dierences
between target and non-target classes and the users' performances increased across sessions
of P300 Speller BCI application usage.

Abstract:

In the present study, we propose a methodological approach to assess the functional brain
network organization underlying the motor imagery in stroke patients. Functional brain con-
nectivity was estimated from high-density EEG signals in a group of patients (N=20) suffering
from unilateral cortical infarctions during the grasping imagination with both the affected and
unaffected hand. The use of a graph theoretical approach allowed the characterization of the
functional EEG networks and revealed a clear altered connectivity structure in the affected
hemisphere during the motor imagery of the affected hand. These findings indicate that analyses of connectivity may offer new insights into the pathophysiology underlying stroke-induced
neurological symptoms. Such information may help in addressing therapies to enhance motor
recovery in patients, also through BCI-assisted systems.

Abstract:

The application of Brain Computer Interface (BCI) technology in stroke rehabilitation
represents one of the most challenging living matter in the BCI eld. With the aim of
developing a specic BCI system for stroke rehabilitation of the upper limb we monitored the
EEG sensorimotor reactivity to actual and imaged hand grasping in a group of stroke patients
consecutively enrolled from a rehabilitation clinic. A subgroup of patients underwent a one-
month BCI training with a system developed on purpose and installed in the rehabilitation
hospital ward.

Abstract:

This work describes an experiment designed to use continuous feedback in terms of a car-game with additional discrete feedback to record error potentials (ErrPs). The game feedback allowed free movement of a car from the left to the right side of a street while moving forward with constant speed. Randomly appearing coins and barriers were required to be picked up or avoided. In case of successful collections or unwanted collisions visual and acoustic signals were presented as discrete feedback. An offline analysis was conducted to evaluate time periods after these discrete feedback events to investigate ErrPs after collisions with barriers. The found detection rates were above chance level for most of the subjects.

Abstract:

Increasing the independency and quality of life of cervical spinal cord injured persons by restoring grasping function using a BCI-controlled hand neuroprosthesis is one of the recent goals in research. The aim of this study is to evaluate the changes of motor cortical patterns, which could be used for controling such a device, in the course of a long-term BCI training in a 20 year old SCI patient. The patient was regularly trained for 17 months, with different BCI paradigms. The activation patterns of the motor cortex changed over time and got stable after 18 feedback sessions and she reached classification accuracies up to 85% already at the third session. Although such a performance is acceptable, it is still a long way towards a useful and reliable signal for controlling a hand neuroprosthesis to restore grasp.

Abstract:

TiA is a concept to transmit raw bisoignals in a standardized way for BCI purposes. It provides
the possibility for multirate and and block-oriented data transmission. Different kinds of signals,
divided into so called signal types (e.g., EEG, EMG, ECG) can be transmitted at the same time, whereby
a data distinction is always possible. TiA utilizes a client--server principle with one server performing data
acquisition and multiple clients as data consumers. Data is divided into immutable meta information and
raw biosignals. A standardized handshaking protocol and TiA data packets were introduced. Applying
this concept an abstraction layer is evolved facilitating the standardization process for BCI development.

Abstract:

With TiA we introduced a standardized interface to transmit raw biosignals. TiA is able to
deal with multirate and block-oriented data transmission. Data is distinguished by different
signal types (e.g., EEG, EOG, NIRS,\dots), whereby those signals can be acquired at the same time from
different acquisition devices. TiA is built as a client-server model. Multiple clients can connect to one
server. Information is exchanged via a control- and a separated data connection. Control
commands and meta information are transmitted over the control connection. Raw biosignal data is delivered
using the data connection in a unidirectional way. For this purpose a standardized handshaking protocol and
raw data packet have been developed. Thus, an abstraction layer between hardware devices and data processing
was evolved facilitating standardization.

Abstract:

The unsupervised signal decomposition method Slow Feature Analysis (SFA) is applied as a preprocessing tool in the context of EEG based Brain-Computer Interfaces (BCI). Classification results based on a SFA decomposition are compared to classification results obtained on Principal Component Analysis (PCA) decomposition and to those obtained on raw EEG channels. Both PCA and SFA improve classification to a large extend compared to using no signal decomposition and require between one third and half of the maximal number of components to do so. The two methods extract different information from the raw data and therefore lead to different classification results. Choosing between PCA and SFA based on classification of calibration data leads to a larger improvement in classification performance compared to us-
ing one of the two methods alone. Results are based on a large data set (n=31 subjects) of two studies using auditory Event Related Potentials for spelling applications.

Abstract:

Non–Invasive Brain–Computer Interfaces (BCI)
convey a great potential in the field of stroke rehabilitation, where the continuous monitoring of mental tasks execution could support the positive effects of standard therapies. In this paper we combine time-frequency analysis of EEG with the topographic analysis to identify and track task–related patterns of brain activity emerging during a single BCI session.
6 Stroke patients executed Motor Imagery of the affected and unaffected hands: EEG sites were ranked depending on their discriminant power (DP) at different time instants and
the resulting discriminant periods were used as a prior to extract EEG Microstates. Results show that the combination of these two techniques can provide insights about specific motor–related processes happening at a fine grain temporal resolution. Such events, represented by EEG microstates, can be tracked and used both to quantify changes of underlying neural structures and to provide feedback to patients and therapists.

Abstract:

Brain Computer Interface (BCI) is an alternative communication system which allows users to send commands and/or messages toward the outside not crossing the normal output channels of the brain, but conveying from the human brain to a computer. In an EEG-based BCI messages are obtained from brain activity. This study presents a novel P300 based Brain Computer Interface requiring no eye gaze, and so usable in covert attention status, called GeoSpell (Geometric Speller). GeoSpell performances have been compared with those obtained by the subjects with the standard 6 by 6 P3Speller matrix (Farewell & Donchin, 1988) which depends on eye gaze. A NASA Task Load Index (TLX) workload assessment (NASA Human Performance Research Group 1987) was employed to provide a subjective rating about the task’s workload and satisfaction with respect to both the interfaces. Results show comparable workload values for P3Speller and Geospell; this result has an important impact in term of efficiency and satisfaction in the use of the device. Geospell interface has shown an accuracy comparable to the P3Speller but with a lower bit-rate.

Abstract:

We describe an approach to improving the design and development of Brain-Computer Interface (BCI) applications by simulating the error-prone characteristics and subjective feel of electroencephalogram (EEG), motor-imagery based BCIs. BCIs have the potential to enhance the quality of life of people who are severely disabled, but it is often time-consuming to test and develop the systems. Simulation of BCI characteristics allows developers to rapidly test design options, and gain both subjective and quantitative insight into expected behaviour without using an EEG cap. A further motivation for the use of simulation is that `impairing' a person without motor disabilities in a game with a disabled BCI user can create a level playing field and help carers empathise with BCI users. We demonstrate a use of the simulator in controlling a game of Brain Pong.

Abstract:

This "work in progress" presentation will deal with the role of the AT centres in the TOBI project. The paper will describe some of the characteristics of AIAS Ausilioteca and BUK and argue how these have impacted on the work undertaken in the TOBI project. After a short introduction to the project and the consortium, the authors will decribe service delivery features of AIAS and BUK. The presentation will discuss how nearness to the end users of AT has created the conditions for their early involvement in the project and the efforts that are currently undertaken to make the project user centred. Strategies such as Matching Person and Technology model and the "living lab"" approach will be highlighted. Finally the results of the user requirements survey held in 2009 will be summarised. The work is supported by the EC within the 7th Framework Programme. The presentation reflects the point of view of the authors only and the EC cannot be held liable for any use that is made of the content.

Abstract:

In this paper we show how healthy subjects can operate a non-invasive asynchronous BCI for controlling a FES neuroprosthesis and manipulate objects to carry out daily tasks in ecological conditions. Both, experienced and novel subjects proved to be able to deliver mental commands with high accuracy and speed. Our neuroprosthetic approach relies on a natural interaction paradigm, where subjects delivers congruent MI commands (i.e., they imagining a movement of the same hand they control through FES). Furthermore, we have tested our approach in a common daily task such as handwriting, which requires the user to split his/her attention to multitask between BCI control, reaching, and the primary handwriting task itself. Interestingly, the very low number of erroneous trials illustrates how during the experiments subjects were able to deliver commands just when they intended to do so. Similarly, the subjects could perform actions while delivering,
or preparing to deliver, mental commands.

Abstract:

To patients who have lost the functionality of their hands as a result of a severe spinal cord injury or brain stroke, the development of new techniques for grasping is indispensable for reintegration and independency in daily life. Functional Electrical Stimulation (FES) of residual muscles can reproduce the most dominant grasping tasks and can be initialized by brain signals. However, due to the very complex hand anatomy and current limitations in FES-technology with surface electrodes, these grasp patterns cannot be smoothly executed. In this paper, we present an adaptable passive hand orthosis which is capable of producing natural and smooth movements when coupled with FES. It evenly synchronizes the grasping movements and applied forces on all fingers, allowing
for naturalistic gestures and functional grasps of everyday objects. The orthosis is also equipped with a lock, which allows it to remain in the desired position without the need for long-term stimulation. Futhermore, we quantify improvements offered by the orthosis compare them with natural grasps on healthy subjects.

Abstract:

Practical Brain-Computer Interfaces (BCIs) for disabled people should allow them to use all their remaining functionalities as control possibilities. Sometimes these people have residual activity of their muscles, most likely in the morning when they are not exhausted. In this work we fuse electromyographic (EMG) with electroencephalographic (EEG) activity in the framework of a so called “Hybrid-BCI” (hBCI) approach. Thereby, subjects could achieve a good control of their hBCI independently of their level of muscular fatigue. Furthermore, although EMG alone yields good performance, it is outperformed by the hybrid fusing of EEG and EMG. Two different fusion techniques are explored showing graceful performance degradation in the case of signal attenuation. Such a system allows a very reliable control and a smooth handover if the subjects get exhausted or fatigued during the day.

Abstract:

Performance improvement in both humans and artificial systems strongly relies in the ability of recognizing erroneous behavior or decisions. This paper, that builds upon previous studies on EEG error-related signals, presents a hybrid approach for human computer interaction that uses human gestures to send commands to a computer and exploits uses brain activity to provide implicit feedback about the recognition of such commands. Using a simple computer game as a case study, we show that EEG activity evoked by erroneous gesture recognition can be classified in single trials above random levels. Automatic artifact rejection techniques are used, taking into account that subjects are allowed to move during the experiment. Moreover, we present a simple adaptation mechanism, that uses the EEG signal to label newly acquired samples that can be use to re-calibrate the gesture recognition system in a supervised manner. Offline analysis show that, although the achieved EEG decoding accuracy is far from being perfect, these signals convey sufficient information to significantly improve the overall system performance.

Abstract:

Most present-day visual brain computer interfaces (BCIs) suffer from the fact that they rely on eye movements, are slow-paced, or feature a small vocabulary. As a potential remedy, we explored a novel BCI paradigm consisting of a central rapid serial visual presentation (RSVP) of the stimuli. It has a large vocabulary and realizes a BCI system based on covert non-spatial selective visual attention. In an offline study, eight participants were presented sequences of rapid bursts of symbols. Two different speeds and two different colour conditions were investigated. Robust early visual and P300 components were elicited time-locked to the presentation of the target. Offline classification revealed a peak accuracy of up to 90% for selecting the correct symbol out of 30 possibilities. The results suggest that RSVP-BCI is a promising new paradigm, also for patients with oculomotor impairments.

Abstract:

P300-based Brain Computer Interfaces offer communication pathways which are independent of muscle activity. Mostly visual stimuli, e.g. blinking of different letters are used as a paradigm of interaction. Neural degenerative diseases like amyotrophic lateral sclerosis (ALS) also cause a decrease in sight, but the ability of hearing is usually unaffected. Therefore, the use of the auditory modality might be preferable. This work presents a multiclass BCI paradigm using two-dimensional auditory stimuli: cues are varying in pitch (high/medium/low) and location (left/middle/right). The resulting nine different classes are embedded in a predictive text system, enabling to spell a letter with a 9-class decision. Moreover, an unbalanced subtrial selection is investigated and compared to the wellestablished sequence-wise paradigm. Twelve healthy subjects participated in an online study to investigate these approaches.

Abstract:

This paper discusses and evaluates the role of shared control approach in a BCI-based telepresence framework. Driving a mobile device by using human brain signals might improve the quality of life of people suffering from severely physical disabilities. By means of a bidirectional audio/video connection to a robot, the BCI user is able to interact actively with relatives and friends located in different rooms. However, the control of robots through an uncertain channel as a BCI may be complicated and exhaustive. Shared control can facilitate the operation of brain-controlled telepresence robots, as demonstrated by the experimental results reported here. In fact, it allows all subjects to complete a rather complex task, driving the robot in a natural environment along a path with several targets and obstacles, in shorter times and with less number of mental commands.

Abstract:

Neuroprostheses using Functional Electrical Stimulation (FES) have proven their clinical relevance for restoration of grasping in subjects with a cervical spinal cord injury (SCI). Current developments aim at the improvement not only of the grasping but also of the elbow function. In very high-level lesioned patients only very limited residual functions can be utilized for control of a reaching and grasping neuroprosthesis. Therefore a novel control interface based on a hybrid approach is introduced, which is comprised of a shoulder position sensor and a Brain-Computer Interface (BCI). An analog control of the grasp or the elbow position is achieved by the measurement of the shoulder position, whereas the switching between elbow and hand is accomplished by the BCI based on motor imageries. The hardware of the reaching and grasping neuroprosthesis consists of a lower and upper arm orthosis combined with an electrically lockable / delockable elbow joint that prevents excessive muscle fatigue. Additionally, an multichannel FES device using surface electrodes has been integrated into the orthosis, which itself can be individually adapted in size and length and can be upgraded with a wrist orthosis. First tests of the feasibility of the system have been performed in SCI subjects.

Abstract:

The brain computer interface (BCI) technology allows a direct connection between brain and computer without any muscular activity required, and thus it offers a unique opportunity to enhance and/or to restore communication and actions into external word in people with severe motor disability.
Here, we present the framework of the current research progresses regarding noninvasive EEG-based BCI applications specifically devoted to interact with the environment and other software. The P300 potentials recorded from the scalp represent a suitable BCI signal control for applications like environmental control. Here we present a set of findings that confirm the feasibility of a real domotic environmental control operated via P300-based BCI and a novelty interface approach to evoke the P300 signal.

Abstract:

Electroencephalographic signals are known to be
non-stationary and easily affected by artifacts, therefore their
analysis requires methods that can deal with noise. In this
work we present two ways of robustifying common spatial
patterns under a maxmin approach. The worst-case objective
function is optimized within a prefixed set of the covariance
matrices that is defined either very simply as identity matrices
or in a data driven way using PCA. We test common spatial
filters derived with these two approaches with real world
brain-computer interface (BCI) data sets in which we expect
substantial fluctuations caused by day-to-day (session transfer
problem). We compare our results with the classical common
spatial filters and show that both significantly improve the
performance of the latter.

Abstract:

EEG single-trial analysis requires methods that are robust against noise and disturbance. In this contribution, based on the framework of robust statistics, we propose a simple modification of Common Spatial Patterns by robustifying covariance estimators against outlying trials caused, for example, by artifacts. We tested the proposed robust filters with EEG recordings from 80 subjects and obtained, not only a significant improvement in performance, but for some subjects also better neuro-physiologically interpretable filters.

Abstract:

Brain-Computer Interfaces (BCIs) allow a user to control a computer application by brain
activity as acquired, e.g., by EEG. One of the biggest challenges in BCI research is to understand and
solve the problem of ‘BCI Illiteracy’, which is that BCI control does not work for a non-negligible
portion of subjects (estimated 15% to 30%). Here, we investigate the illiteracy problem in BCI systems
which are based on the modulation of sensorimotor rhythms. In this paper, a sophisticated adaptation
scheme is presented which guides the user from an initial subject-independent classifier that operates
on simple features to a subject-optimized state-of-the-art classifier within one session while the user
interacts the whole time with the same feedback application and does not need to care about what is
going on behind the scenes. While initial runs use supervised adaptation methods for robust co-
adaptive learning of user and machine, final runs use unsupervised adaptation and therefore provide an
unbiased measure of BCI performance. Using this approach, which does not involve any offline
calibration measurement, good performance was obtained by good BCI subjects (also one novice) after
3-6 minutes of adaptation. More importantly, the use of machine learning techniques allowed subjects
who were unable to achieve successful feedback before to gain significant control over the BCI system.
In particular, one subject had no peak of the sensory motor idle rhythm in the beginning of the
experiment, but could develop such peak during the course of the session (and use voluntary
modulation of its amplitude to control the feedback application).

Abstract:

Compared to invasive Brain-Computer Interfaces (BCI), non-invasive BCI systems based on Electroencephalogram (EEG) signals have not been applied successfully for precisely timed control tasks.
In the present study, however, we demonstrate and report on the interaction of subjects with a real device: a pinball machine. Results of this study clearly show that fast and well-timed control well beyond chance level is possible, even though the environment is extremely rich and requires precisely timed and complex predictive behavior. Using machine learning methods for mental state decoding, BCI-based pinball control is possible within the first session without the necessity to employ lengthy subject training. The current study shows clearly that very compelling control with excellent timing and dynamics is possible for a non-invasive BCI.

Abstract:

This paper will describe the outcomes of a study into the state-of-the art of user needs and user requirements of assistive applications for people with disabilities. The study was carried out within the framework of the TOBI (Tools for Brain Computer Interaction) project, a project (2008-2011) funded under the 7th Framework Programme of the EC. The project has the aim to develop practical technology for brain-computer interaction that will improve the quality of life of disabled people and the effectiveness of rehabilitation. TOBI includes leading European groups in BCI, human-computer interaction, intelligent robotics, and applied assistive technologies. It also includes rehabilitation clinics and commercial providers of assistive technology.

Non-invasive BCI are based on electroencephalogram (EEG) signals. The EEG is recorded through
electrodes placed on the user’s head. This technology is not invasive and only records the electrical
activity of the brain without interfering with it. TOBI is expected to have an impact by broadening the appropriate use of BCI assistive technology, by incorporating adaptive capabilities that augment those other assistive technologies they are combined with.
After a pre clinical validation the BCI based assistive solutions will be tested and evaluated in real life situations by different populations of end-users.

As a first step the project has identified potential users of BCI as people with severe motor disabilities. General inclusion and exclusion criteria have been identified to access in the future to the test phase, as well as specific inclusion/exclusion criteria for the different application areas Communication and Control, Entertainment, Motor substitution and Motor recovery. To favour the involvement of people with disabilities and to have a sound basis for the definition of user requirements based on user needs and experiences, a questionnaire has been designed to assess different aspects of the current situation of the respondents, their level of satisfaction with current solutions for their independence, their needs and preferences regarding technology based assistive solutions. Areas of independence explored through the questionnaire have included, among other, mobility, communication, computer access, environmental control. The assessment of important aspects in the evaluation of assistive technology was based on aspects such as: functionality, easiness of use, fatigue, design, comfort, impact on environment, etc.

The questionnaire has been delivered to a sample of 50 people across four European countries. The paper will focus specifically on user requirements for the BCI applications areas Communication, Entertainment, Environmental control. It will present the results of the survey, cross analysed with some person related factors, and discuss the impact of these outcomes on the user requirements for BCI applications as formulated within the consortium. Also the review of these outcomes by of teams of AT experts in leading European AT centres will be covered by the paper. Finally some comments will be made on how these user requirements have impacted on the evaluation activities as planned once the prototypes are available.

This work is supported by the European ICT Programme Project FP7-224631. This paper only reflects the authors' views and funding agencies are not liable for any use that may be made of the information contained herein.

Abstract:

EEG single-trial analysis requires methods that are robust with respect to noise, artifacts and nonstationarity among other problems. This work contributes by developing a minimax approach to robustify the common spatial patterns (CSP) algorithm. By optimizing the worst-case objective function within a prefixed set of the covariance matrices , we can transform the respective complex mathematical program into a simple generalized eigenvalue problem and thus obtain robust spatial filters very efficiently. We test our minimax CSP method with real world brain-computer interface (BCI) data sets in which we expect substantial fluctuations caused by day-to-day or paradigm-to-paradigm variability or different forms of stimuli. The results clearly show that the proposed method significantly improves the classical CSP approach in multiple BCI scenarios.

Abstract:

Implementation of Error Detection into the Graz-Brain-Computer Interface, The Interaction Error Potential
Alex Kreilinger, Christa Neuper, Gert Pfurtscheller, Gernot R. Müller-Putz
Graz University of Technology, Institute for Knowledge Discovery

Brain-Computer Interfaces (BCIs) are possible remaining means of communication for people with severe paralyses or who are in a locked-in state. The field of BCI-applications reaches from communication [1] to neuroprostheses [5] and wheelchair control [4]. However, there are still some severe restrictions for BCIs when compared to other assistive devices. Main problems are a low accuracy and an insufficient performance speed. A possible way to improve BCIs is the detection of errors after incorrect events in the EEG. Error correction can inhibit wrong classifications and hence is very useful to increase the accuracy and therewith also the bit rate. After execution or observation of false events characteristic waveforms (error potentials (ErrPs)) of the EEG over the location of the anterior cingulate cortex (ACC) can be recorded and adopted to detect errors. One of the most promising kind of ErrPs is the interaction ErrP [2,3] which can be measured after users observe errors committed by an interface that should translate their commands correctly.
This paper describes the recording of interaction ErrPs as a verification of previous studies dealing with this subject [2,3]. Here, the interaction ErrP is described as a reliable source to evaluate errors and to increase the precision of BCIs. The goal of this study was to retrace these findings and to additionally reduce the complexity of the recording setup, meaning to reduce the number of electrodes and keeping data processing as simple as possible. Under these conditions the error detection was implemented into the Graz-BCI-system based on motor imagery (MI) [6].
In summary, the findings about interaction ErrPs could be confirmed (in terms of the recorded waveforms). Furthermore, the detection rates for single trial error detection exceeded 70%:24% (true positive:false positive) in offline analysis, averaged over 13 subjects. After offline analysis online experiments were conducted that could not show the anticipated error detection rates but could still improve the bit rate significantly. This indicates that even with a limitation of complexity the error correction can improve the accuracy of BCI-systems, especially for people who only achieve a poor performance without error correction.


"This work is supported by the European ICT Programme Project FP7-224631. This paper only reflects the authors' views and funding agencies are not liable for any use that may be made of the information contained herein."

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control of spelling devices and neuroprostheses. Progress in Brain Research, 159:393–409, 2006.

Abstract:

A brain-computer interface (BCI) can be used to control a limb neuroprosthesis with motor imaginations (MI) to restore limb functionality in paralyzed persons. However, existing BCIs lack a natural control and need a considerable amount of training time or may use invasively recorded brain signals. A new approach is the direct decoding of movements which has already been shown non-invasively for executed movements. In this work we show indirectly that algorithm principles used in decoding executed movements can also be applied when decoding imagined movements. Healthy subjects performed rhythmic arm movement imaginations in the transverse and sagittal plane. We were able to classify the correct movement plane with an average classification accuracy of 69 % considering only significant results. This shows that the classification of movement imaginations with the same hand in two different planes is possible.

Abstract:

For an auditory ERP paradigm, randomized stimulus presentation sequences were compared to fixed predictable stimulation sequences. In a study with N=10 healthy subjects, a standard offline analysis of the collected data epochs resulted in comparable classification performance and ERP responses. Making explicit use of the repetitive structure, the classification result could be improved only for the fixed sequence condition.

Abstract:

Traditional BCIs rely primarily on EEG signals. Due to the non-stationary and non-linear characteristics of these signals, BCIs often suffer from limited accuracy in psycho-cognitive tasks. Also, some users are unable to produce distinct EEG features for different mental tasks and as such are unable to achieve reasonable control of such a BCI system. In order to address these limitations, this paper introduces a hybrid-BCI (hBCI) based on a combination of ECG and EEG signals. The hBCI uses a power spectrum based technique for feature extraction, and Fisher's linear discriminant analysis for classification. Compared with a traditional BCI, the hBCI provides higher performance during offline analysis. The hBCI is also successfully being used in an online task. We report on both the offline and online performance of the hBCI system.

Abstract:

In this study the usabilty of a hybrid-P300 BCI communication application was evaluated by four severely motor restricted possible BCI end-users. The P300 BCI was combined with EMG for error correction (see also abstract Riccio et al.). The prototype was evaluated in terms of effectiveness (accuracy), efficieny (time needed to complete task) and end-user’s satisfaction. In two copy-spelling tasks accuracy was high (M=92.5% and M=98.75%), but lower in the free-spelling sentence (M=85.02%) and email task (M=75.34%). The hybrid letter correction could be used by all end-users and improved efficiency. Overall, end-users were moderately to highly satisfied with the BCI, but least satisfied with the adjustment (M=3.25 of 5), effectiveness (M=3.25 of 5) and aesthetic design (M=3 of 5) of the BCI, as assessed with the Extended Quest 2.0. One end-user could imagine using the BCI in daily life.

Abstract:

In the current study the BCI controlled application Brain Painting was installed at a locked-in ALS-patient’s home. Family and caregivers were trained to set-up the EEG-cap and amplifier and to start an easy-to-use interface for the brain painting application. BCI data, duration of painting time, and evaluation were saved automatically on a server. The Brain Painting was evaluated in terms of satisfaction, frustration and enjoyment using a visual analogue scale. In over 8 months the end-user painted in 86 BCI sessions (and ongoing). Overall, satisfaction was moderate to high (M=6.2 of 10, SD=3.65). The study demonstrates that expert-independent BCI use is possible. Nevertheless, independent BCI use is challenged by technical problems and variable BCI control.

Abstract:

We present a novel selection mechanism for binary input channels such as motor imagery EEG, which is robust to very high noise levels and biased inputs. This technique makes otherwise weak channels usable for interaction, and closely approaches theoretical optimal performance, while retaining a simple, clear interface.

Abstract:

A hybrid (electromyographic, EMG) control devoted to the correction of spelling errors was introduced in a previously implemented P300-based BCI system designed to control an assistive technology software (Riccio et al., 20111; Zickler et al., 2011). The hybrid version of such system would provide severly disabled end-users with a way to exploit not otherwise functionally reliable residual muscular activity. Six healthy subjects and one severly motor impaired end-user participated to the system testing. Preliminary findings are in favour of the superiority in efficiency of the hybrid control with respect to the no-hybrid (only BCI-based) as indicated by the observed improvement of the performance (expressed as time for selction and number of errors) that was associated with a decrease of the system usage frustration perceived by the users.

Abstract:

In the recent past, Brain-Computer Interface (BCI) have been proposed as a potential mean to maximize the output of standard motor therapy after stroke, providing access to the damaged motor network of the brain. Also, Functional Electrical Stimulation (FES) is often applied during motor therapy to directly engage muscles of the affected side of the body. In this paper, we describe a BCI system for stroke rehabilitation that decodes the engagement of motor areas of the brain and activates FES of a target muscle on the affected arm, accordingly. The system includes visualization for physical therapists about the state of the brain and the current muscular activity. Preliminary results on 4 patients show consistency in the EEG features selected for further training. Two of the patients completed the testing, and both show recovery of target muscle function. Our results support the idea that BCI can be used to promote beneficial brain plasticity, and justify further testing on a larger population.

Abstract:

Previously we have shown that motor-disabled end users were able to drive a telepresence robot using a Brain-Computer Interface (BCI). However, to facilitate the interaction part of telepresence, users must be able to voluntarily and reliably stop the robot at any moment, not just drive from point to point. We propose to exploit the user’s residual muscular activity to provide a fast and reliable EMG channel, which can toggle start/stop the telepresence robot. Our preliminary results show that not only does this hybrid approach increase accuracy, but it also helps to reduce workload and was the preferred control paradigm of all 4 participants in this study.

Abstract:

Controlling a device by the brain requires the user to pay visual attention to it, which is partly in conflict with the visual BCI feedback. Therefore, a tactile stimulator is developed, which provides a tactile illusion as BCI feedback. The stimulation system consists of six coin motors and a single-board microcontroller. Several psychophysical experiments are conducted to optimize the parameters that generate the illusion. Two protocols that convert the BCI feedback into spatiotemporal patterns of the stimulator are tested online.

Abstract:

The main aim of this study is to demonstrate the reliability of an online EEG based BCI using covert visuospatial attention. For this purpose the BCI system has been tested across different days and in two different conditions: with neutral background and with natural images. The achieved classification performances achieved (70.6±4.3% in average, in case of neutral background) makes this mental signal as a promising candidate for BCI control. Although the performances dropped (61.2±3.3% in average) with natural images, this was the first attempt of a comparison between these two conditions in the case of a covert visuospatial attention task.

Abstract:

The effects of continuous classifier adaptation in Motor Imagery (MI)-based Brain-Computer Interaction (BCI) on a subject's ability to improve and stabilize his BCI performance through feedback learning have been largely neglected in favor of gains in online classification accuracy. In this work, we investigate the influence that adaptation intensity may have on the subject's ability to learn and consolidate a MI strategy. Preliminary results with one disabled and two healthy subjects show that there exists a natural trade-off between online accuracy maximization and subject learning which needs to be carefully accommodated by supervised BCI adaptation methods.

Abstract:

Decoding of limb kinematics from the scalp electroencephalogram (EEG) is receiving some empirical support, but feasibility of on-line application in a brain-machine interface (BMI) is yet to be resolved. Presumably, however, any real-time operation would be imperfect, i.e., prone to incorrect recognition of user's intent and erroneous movement of any limb avatar. It is therefore necessary to understand EEG correlates of the user's perception of such errors in order to: (i) account for their confounding influence on the signal, and more interestingly (ii) tap into any additional information they may provide about the user's intent. Here we investigate the feasibility of single-trial recognition of error-related potentials induced in subjects operating today's most ubiquitous upper limb avatar: the computer mouse, while we distort the mapping between the cursor and the hand (mouse), simulating imperfect operation of a kinematics-decoding BMI.

Abstract:

Modeling electroencephalographic (EEG) correlates of affective or cognitive processes of brain-computer interface (BCI) users’ can be used to adapt BCI’s operation. It can also serve to develop BCIs not targeted at providing a control signal to operate a device, but rather whose main goal is only to monitor users' mental states, e.g. attention levels. In this off-line study we test the feasibility of real-time recognition of subject’s sustained attention to the visual feedback used in our BCI applications. Cross-validation results indicate usable accuracy.

Abstract:

Recently published results of a center-out experiment showed the basic feasibility of the offline-decoding of hand movements from continuous Electroencephalogram (EEG) recordings. However, the decoding accuracy is limited and it is still unclear which conditions may lead to an improved estimation of movement parameter. Thus, in the presented work the influence of velocity on the decoding accuracy was investigated. The analysis of center-out tasks with different execution velocities performed in 5 healthy subjects shows that velocities of the hand in the transversal plane are better decodeable, if the movements were executed with higher velocities. It may be concluded that users of Brain-Computer Interfaces (BCIs) for control of upper extremity neuroprostheses should be instructed to use fast movements to improve the movement decoding accuracy.

Abstract:

Three BCI-controlled AT devices namely a Functional Electrical Stimulation (FES)-hybrid orthosis, a telepresence robot and a music player were tested and evaluated in a highly paralyzed subject (C3 Tetraplegic since 2010, 42 years old, BCI-naïve). He went through an extensive Motor-Imagery-Brain-Computer Interface (MI-BCI) training of 102 runs and achieved an average performance >80%. He successfully passed all three testing protocols, stated a low to medium workload and was satisfied with their use and could imagine using improved versions in his daily life.

Abstract:

Highly paralyzed subjects have only a few residual motor functions that can be used for control of conventional assistive devices (ADs). These devices can be extended to accept input from a Motor-Imagery-Brain-Computer Interface (MI-BCI) to make them accessible for such individuals. However, it is still unclear to what extend highly disabled individuals are able to control a MI-BCI. In this study, the outcomes of MI-BCI training sessions with ten spinal cord injured (SCI) subjects are presented. Only one subject’s average performance is greater than 70%, three subjects have a performance around 70%.

Abstract:

Although Brain Computer Interfaces have been proposed as communication devices for those with severest motor impairment, research is rarely performed with this target population, i.e. people in the locked-in state. Usually, developments are tested in healthy samples and assumptions are made regarding generalization of results to patient samples. Herein we report a case study with a user in the locked-in state. Different paradigms on different modalities were applied and far best performance was achieved in the tactile modality, usually regarded as inferior to the visual and auditory modality. Although she displayed distinct ERPs in a visual oddball, the visual channel could not be utilized for communication – even a so-called gaze independent speller failed. Our results thus highly encourage BCI development in the frame of a user-centered approach. Generalization from healthy participant data to patient samples should be treated with great caution and cannot replace actual end-user testings. BCIs that may be regarded less effective or less practical, may be the only possibility for a specific end-user. Adjusting BCI development specifically to end-users’ needs and requirements is mandatory and will thereby potentially allow for a transfer of BCI technology out of the lab into end-users’ daily lifes.

Abstract:

Recent advances in brain computer interfaces (BCI) based on event related potentials (ERP) proved that face stimuli can increase spelling performance due to an improved signal-to-noise ratio of the recorded ERPs. This study investigated its effect on BCI inefficiency in users with neurodegenerative disease who often display decreased spelling performance as compared to healthy participants. Performance achieved with the commonly used BCI (P300-BCI) was compared to BCIs using face stimuli in several online sessions with different number of stimulation sequences. Herein we report on data from N=9 participants with neurodegenerative disease. Online performance was significantly increased when using face stimuli as compared to classic stimulation. Importantly, two users who were highly inefficient with the commonly used BCI (performance ≤ 40%) spelled with high accuracy levels when using face stimuli. Our results thus display particular benefits of face stimuli in the target user group.

Abstract:

BCI systems may be employed in stroke rehabilitation to monitor and reinforce EEG patterns generated by motor imagery (MI). In the rehabilitative path of a stroke patient, therapists would encourage and reinforce any residual (or recovered) execution of the MI trained hand movements. For this reason, a hybrid BCI-driven rehabilitative device was proposed in order to boost motor recovery of the upper limb in stroke patients. This system would employ brain signals generated from motor attempt and reinforce voluntary contraction reflecting correct muscles activation as recorded by surface electromyography (EMG). The aim of the present work is to provide an EMG classification method that would be compliant with the current rehabilitation principles.

Abstract:

Recently several researchers proposed different P300-based Brain Computer Interfaces which can be controlled even with impaired eye movements (covert attention). However, in all the comparative studies, authors detected lower accuracy for the covert attention modality with respect to the overt one. This study aims to investigate if this decrement correlates with lower stability of the P300 potential evoked during the task. We evaluated the latency jitter of the P300 evoked potential with two BCI spellers exploiting overt and covert attention. We found that the P300 latency jitter is significantly higher and Written Simbol Rate is significanly lower for the covert-attention BCI speller. We conclude that the reduced performance of BCIs based on covert attention is only partially explained by the absence of discriminant short-latency Visual Evoked Potentials.

Abstract:

The feasibility of using a time-coded motor imagery-based brain-computer interface (BCI) for the control of an elbow and hand neuroprosthesis was investigated in a study with nine healthy subjects and one person with spinal cord injury (SCI). Context-based BCI commands, which resulted from motor imageries with different activation lengths, were used to open/close the hand or to flex/extend the elbow. All participants had to follow a predefined activation sequence simulating a self-feeding procedure. On average 5.5 out of 10 sequences were successfully completed by the study participants. The SCI end-user was among the best subjects. The system was found to be feasible for persons with severely limited muscular functions by providing a control method purely based on mental activity.

Abstract:

FES is used in various neuroprostheses to substitute for non-recovered motor functioning, including improving hand function in tetraplegia patients. Furthermore BCI has been proposed to control FES of paralyzed hand muscles to allow functional grasping. The objective of the study is to assess the validity of FES training for improving hand\wrist movements to obtain the functionality necessary for the BCI controlled motor substitution approach to grasping.

Abstract:

Over the last decade the improvement of a missing hand function by application of neuroprostheses in particular the implantable Freehand system has been successfully shown in high spinal cord injured individuals. The clinically proven advantages of the Freehand system is its ease of use, the reproducible generation of two distinct functional grasp patterns and an analog control scheme based on
movements of the contralateral shoulder. However, after the Freehand system is not commercially available for more than
ten years, alternative grasp neuroprosthesis with a comparable functionality are still missing. Therefore, the aim of this study
was to develop a non-invasive neuroprosthesis and to show that a degree of functional restoration can be provided to end users
comparable to implanted devices. By introduction of an easy to handle forearm electrode sleeve the reproducible generation of
two grasp patterns has been achieved. Generated grasp forces of the palmar grasp are in the range of the implanted system.
Though pinch force of the lateral grasp is significantly lower, it can effectively used by a tetraplegic subject to perform
functional tasks. The non-invasive grasp neuroprosthesis developed in this work may serve as an easy to apply and inexpensive way to restore a missing hand and finger function at any time after spinal cord injury.

Abstract:

Introduction
Motor Imagery (MI) was proposed to enhance motor recovery after stroke. EEG-based Brain Computer Interfaces (BCI) operated by MI can provide monitoring and reinforcement of such task-specific training. A BCI rehabilitation device was specifically developed for recovery of hand function after stroke. Here we report the validation of this device, conducted in accordance with the guidelines to demonstrate the efficacy of novel rehabilitation interventions.
Methods
Sixteen stroke patients were consecutively recruited upon their admission to the clinic for post-stroke rehabilitation treatment, and randomly assigned to the BCI-supported MI group (BCI) or MI control group (CTRL). The primary outcome measure was the arm section of the Fugl-Meyer scale. A minimal clinically important difference (MCID) for this scale was described to 7 points. Secondary outcome measures were European Stroke Scale and the arm MRC scale for muscle strenght.
Results
No significant group differences at baseline were found on primary and secondary outcome measures. Regarding the primary outcome measure, a mean change of 9.3 (+45%) was observed in th BCI group, exceeding the MCID of 7, with respect to an improvement of 5.62 (+9%) observed in CTRL control group.
Conclusion
To our knowledge this is the first randomized controlled trial to evaluate the efficacy of BCI-supported MI for motor recovery after stroke. Our findings support the efficacy of this approach. Two possible biases were found in the randomization procedure: side of the brain lesion and time from the event.
Acknowledgements: This work is supported by the European ICT Programme Project FP7-224631.

Abstract:

The article presents an overview of the results from a semi-structured interview study conducted with 17 participants in BCI research. The results show that respondants confronted for the first time with (non-invasive) BCI technology do not report feelings of risk or danger, but rather talk about their experiences of trivial distress and practical problems with this technology. Depending on their motivations, some of the research subjects expressed frustration at the end of the studies. This shows that research ethics should still be the core of BCI ethics.

Abstract:

Brain-computer interfaces bridge parts of the body and allow the environment to be manipulated without motor activities. This sheds light on some anthropological issues triggered by the current visionary movement of transhumanism aiming at overcoming the human being’s vulnerable biological constitution and finiteness. This paper alludes to some patterns of European philosophical anthropology and speculates on the principle feasibility of transhuman visions.

Abstract:

Within this work we propose a new interface to transmit events within BCI systems. This has been done to facilitate compatibility within different BCI systems. The distribution system, called TiD is based on a bus design and transmits events in an asynchronous way. TiD messages are based on XML. A cross-platform library has been developed and is available for download.

Abstract:

Brain-computer interface (BCI) systems can be used to control limb neuroprostheses in order to restore limb functionality of paralyzed persons. Traditionally, only invasive BMI (brain machine interfaces) are thought to provide an adequate signal-to-noise ratio and bandwidth to control an upper limb neuroprosthesis accurately in a continuous manner with sufficient degrees-of-freedom. This paper supports work which already showed that it is possible to decode the velocity of executed arm/hand movements from electroencephalographic signals but using a different movement paradigm and suppressing eye movements. It was possible to decode movement velocities with a high correlation with actual executed movements. This could pave the way to a new type of prostheses control using a direct link betweeen natural hand movement imagination and prostheses movement.

Abstract:

In this study we investigated if brain computer interfaces (BCI) based on event-related potentials (ERP) can be handled independently by laymen without expert interference, which is inevitable for establishing ERP-BCIs in end-user’s daily life situations. We implemented a straightforward, user-centered software tool which reduces handling of an ERP-BCI application to a few button presses. The software individually adjusts classifier weights and control parameters in the background, invisible for the end-user (auto-calibration). Also, a predictive text entry system directly integrated into the matrix allows for fast selection of words. In this study, N=16 BCI novices used the software on their own without expert interference. All participants were able to operate the software and to correctly spell a sentence with the auto-calibrated classifier. Furthermore, participants particularly benefited from the predictive text entry in terms of spelling speed and accuracy was not reduced, as previously reported elsewhere with another predictive text entry paradigm. Altogether this study demonstrates feasibility of BCI use independently by laymen and the strong benefit of integrating text entry into the matrix.

Abstract:

Recently, we proposed a new stimulation paradigm for ERP-based brain-computer interfaces, i.e. flashing characters with transparently superimposed pictures of famous faces. In an offline study with N=20 participants, this paradigm elicited face specific ERPs which strongly contributed to offline classification accuracy due to an improved signal-to-noise ratio. Consequently, this new face flashing (FF) paradigm significantly outperformed the classic character flashing (CF) paradigm, i.e. simply highlighting characters. Herein we present first results from an online test of the FF paradigm. We gradually decreased the number of sequences used for flashing characters to estimate the potential of FF for improving bit rate. Average online performance of the FF paradigm was above 80% with one sequence only. With CF this level was reached only after six sequences. Furthermore, with the FF paradigm and 3 sequences all participants performed with 100% accuracy whereas with the CF paradigm not all subjects achieved 100% accuracy. Our results confirm the previously reported benefits, with even stronger effects in a typical online setting. This indicates great potential for improving communication with the FF paradigm under online conditions. Also, faces can be easily integrated in other applications such as internet browser.

Abstract:

In this paper, first evaluation results of a BCI controlled modular hybrid-neuroprosthesis for functional restoration of grasping and reaching function in an individual with a high spinal cord injury are presented. The user has been able to perform a functional task, i.e. eating, with the use of a hybrid FES elbow orthosis, which he is not able to do without the help of the system. An essential component of the successful application of the neuroprosthesis was the introduction of the concept of the hybrid BCI, where the BCI was used for switching between elbow and hand control and with a shoulder position sensor for hand opening/closing and for flexion/extension of the elbow.

Abstract:

This study describes our experiences with motor imagery (MI) Brain-Computer Interface (BCI) control of a patient who is very close to the locked-in state. Typical event-related (de-)synchronization (ERD/ERS) pattern were found that could maintain a promising BCI control (up to 90% binary accuracy) in a copy-task within six sessions. Nevertheless, the patient was not yet able to reliably use this control in a free gaming mode of the application (connect-4 game).

Abstract:

This single-case study describes findings of a study with a long-term patient with amyotrophic
lateral sclerosis (ALS) in locked-in state since 13 years. During 11 sessions of attempted motor
execution tasks, EEG recordings were analyzed in real-time for the online control of a Brain-Computer
Interface and post-hoc. Basic EEG rhythmic activity of the patient's motor system seems unaffected by
the neurodegenerative disease. However, the class-discriminative information contained in event-related
(de-)synchronization data and slow motor-related potentials varies strongly between sessions.

Abstract:

This study introduces the photobrowser, a BCI application specifically designed for severely handicapped end-users. It allows its user to be involved in manipulating and exploring a photo collection that is shared between the user and participating relatives. The end-user can thus participate in a socially interacting group of friends, even if those friends are geographically separated. The first results with one tetraplegic end-user are promising, with 100% accuracy on all but one session.

Abstract:

The present study aimed to evaluate the efficacy of mental training of motor skills (motor imagery, MI) supported by an EEG-based BCI device, whose developement and implementation was based on nurorehabilitation principles, in promoting hand function recovery after stroke. The validation procedure of such BCI application was designed as a rehabilitation stage II pilot trial with two endpoints: primarily, to demonstated the efficacy this BCI-based MI training as an adjuntive intervention in post-stroke rehabilitation and, secondarily to forster the transfer of BCI technology into clinical practice. Sixteen stroke patients were enrolled and randomized in two programme groups: i) a BCI-based MI training programme group and a MI training programme control group. Both groups of patients received a similar usual care treatment. The results showed a significant improvement of some of the outcome measures in the intervention group (MI-based BCI training) with respect to the control group. Yet, two important biases in the randomization process were found and corrective actions were discussed.

Abstract:

Within the evaluation framework of an innovative BCI system for post-stroke rehabilitation [Pichiorri et al., 2011], we analyzed the influence of psychological variables and the workload on patient’s performances that have been already described to have relevance in BCI training for communication purposes [Kleih et al., 2010; Nijober et al., 2010]. Mood, motivation and workload assessment was performed before, after and across training sessions on a sample of stroke patients who undertook one month Motor Imagery (MI) based BCI-training with the aim of promoting recovery of the hand function. Results showed a good level of acceptability of BCI technology for rehabilitation; motivation may contribute to variance in BCI performance and should be monitored in the settings. Workload scores were similar between intervention groups. These preliminary findings are of relevance when considering the translation of BCI technology into real clinical setting for post-stroke rehabilitation intervention.

Abstract:

The objective of this work is to provide a comprehensive BCI-driven rehabilitative device, which incorporates Functional Electrical Stimulation (FES), for the rehabilitation of the upper limb in stroke patients. The proposed system is designed in aggreement with the neurorehabilitation experts and is meant to comply with the current rehabilitation principles. The main innovative characteristic of the device would be the “hybrid” control of the FES system, meant to reinforce both motor intent (as recorded by EEG) and the associated residual motor ability (electromyograpy, EMG). Furthermore, FES would provide for an enriched sensorimotor feedback with the aim of boosting motor scheme re-learning by allowing voluntary access to the stroke affected hand.

Abstract:

A Brain-Computer Interface (BCI)-controlled photo browser software has been developed to enable severely disabled people to participate in a 2-way sharing of photos with a group of friends, family members and carers (FFCs). Here we present the initial results from an ongoing study involving such an end-user who has been able to successfully share photos with 3 friends and family over a period of 3 months. Both quantitative and qualitative evaluation metrics collected thus far reveal positive and active experiences shared among the BCI user and her friend and 2 family members.

Abstract:

Abstract. A photo browser software controlled by a P300 based Brain Computer Interface (BCI) was tested with a severely disabled end-user lacking any productive communication. The photo browser was provided with features allowing the end-user to share photos with one friend and two family members. The results show the end-user’s high satisfaction with the application, and an increasing self-assurance in using it is indicated by a decrease of reported “incompetence fear” and “effort”.

Abstract:

In this work, we present a framework for exploiting contextual knowledge derived from a brain-actuated device to improve the classifier adaptation of a Brain-Computer Interface (BCI). Our method provides a modified expectation step of the conventional Expectation-Maximization (EM) algorithm for Gaussian models to assign more accurate probabilistic labels to the extracted brain patterns, thus implementing a semi-supervised learning approach. The algorithm is tested in an offline simulation with artificial datasets where the simulated BCI is coupled with a binary text-entry system.

Abstract:

This work presents mid-term clinical evaluation results of a hybrid Brain Computer Interface (BCI) text-entry prototype based on Motor Imagery. This novel prototype takes advantage of a simplified graphical interface, tight coupling of inference mechanisms and the Human-Computer Interface (HCI) and an auxiliary hybrid control modality which exploits potential residual muscular activity, in order to improve end-user experience compared to earlier text-entry system designs. The results of clinical testing with three disabled and one healthy user in a word-typing task demonstrate the effectiveness and efficiency of this prototype.

Abstract:

Recently there was a growing interest for gaze independent Brain Computer Interface. Also researchers are engaged to improve usability and reliability of these systems for a more users’ independent use. This study evaluate an asynchronous classifier with a gaze independent P300-based Speller. Results, achieved by 9 healthy subjects, allowed to conclude that an asynchronous classifier could be a valuable solution to improve reliability and usability of P300 based BCI systems also when the user’s voluntary control of eye movements is impaired.

Abstract:

In order to bring Brain Computer Interface systems from research laboratories to end users’ homes several factors should be considered and improved. This work aims to assess performances during repeated P300-based BCI sessions in the same day mimicking the use of BCI system as communication aid. Preliminary findings highlighted a daily variability of BCI performances.

Abstract:

Our brain-actuated wheelchair uses shared control to couple the user input with the contextual information about the surroundings in order to perform natural manoeuvres both safely and efficiently. In this study, we investigate the feasibility of using our brain–controlled wheelchair with patients in a rehabilitation clinic. Both user and system performance metrics are analysed. We find that the driving performance of a motor-disabled patient at the clinic is comparable with the performance of four healthy subjects. All five participants were able to complete the driving task successfully.

Abstract:

In this paper we present the results of six motor-disabled patients manoeuvring a telepresence robot via a BCI. Remarkably, although five of the patients had never visited the location where the telepresence robot was operating, they achieved similar performances to a group of four healthy users who were familiar with the environment. In particular, the experimental results confirm the benefits of using shared control for brain-controlled telepresence robots. Shared control empowered all subjects (including the less experienced motor-disabled BCI subjects) to complete a complex BCI task in a comparable time and with a similar number of commands to those required for a manual condition.

Abstract:

The use of Brain-Computer Interface (BCI) systems as stroke rehabilitation tools has fostered the need to define novel indexes to monitor brain functions during specific tasks. In this study, we analyze the EEG of six stroke patients performing Motor Imagery (MI) of the affected limb or resting, identifying the associated Microstates and representing the EEG as a sequence of these stable topographies. Then, we define an index of activity on top of this information and characterize task-specific Microstates appearance across time. The results we report on one manually selected topography per subject show that the index quantifies motor-specific changes of brain activity at the single trial level as demonstrated by the average changes between resting and MI trials in different time windows, and could be used to track and quantify user engagement in the mental task.

Abstract:

Successfully operating applications–like telepresence robots or text entry systems–just with your mind requires a good level of Brain-Computer Interface (BCI) control. How much training is needed to achieve such a level? Is it possible to train a naïve end-user subjects in 10 days to sucessfully control such applications? In this work we report our mid-term experiences based on the training of 12 severely motor disabled participants at a rehabiliation clinic, without BCI experts present.

Abstract:

Due to its intuitive nature, the decoding of covert visuospatial attention has recently been proposed to be useful for Brain–Computer Interface (BCI) applications. In order to identify neural correlates of covert spatial visual attention, state of the art approaches usually rely on the whole α–band over fixed time intervals. In this work, we propose a discriminative model that exploits spectro-temporal evolution of covert visuospatial attention to improve classification performances. Results with 10 healthy subjects demonstrate that our approach reaches, on average, 0.74±0.03 of AUC value with an increase (+11.5%) with respect to the state of the art method. In addition, the proposed method allows faster classification (<1 second on average) without compromising classification performances.

Abstract:

It has been argued that BCI technology faces special problems regarding moral and/or legal
responsibility. The article shows that the arguments behind this assumption are based on descriptive
and causal analyses of BCI use while judgements on agency and responsibility are actually
interpretative in character. The ascription of agency and responsibility does not, even in simple cases,
require that people are in causal control of every individual detail involved in an event. From a
pragmatic point of view, responsibility in BCI use can be dealt with under the rules and regulations
currently followed in dealing with established technology.

Abstract:

One of the challenges of modern research is to enable severely disabled persons to communicate with others and to perform these tasks commonly taken for granted.

Brain-computer interfaces (BCIs) have been designed to fulfill such a requirement and are now ready to be deployed at homes and clinics.
Under this perspective, the precise detection of the user's intent to deliver (intentional control) or not to deliver (intentional non-control) mental commands while acting in natural environments plays a crucial role.

In this work we present results supporting that non-invasive BCIs can be operated while multitasking, a condition where subjects split attention between BCI control and external tasks.

Abstract:

This poster compares classification accuracies of feature extraction methods (FEMs) as used in sensory motor rhythm (SMR) based Brain-Computer Interfaces (BCIs). Features were extracted offline from 9 subjects and classified with linear discriminant analysis. The following FEMs were compared: adaptive autoregressive parameters, band power, phase locking value, time domain parameters, and Hjorth parameters. FEM parameters were optimized individually with a genetic algorithm in advance. In summary, time domain parameters combined with a bipolar spatial filter yielded the best classification accuracies.

Abstract:

This contribution investigates the effects of applying an unsupervised adaptation mechanism to linear classiers for Brain-Computer Interfaces (BCI). Specifically, we track changes in the first two moments of the unlabeled data distribution. Changes are adaptively compensated by recalculating the classifier based on short, consecutive data segments. The approach is validated on three auditory oddball data sets containing a total of N = 37 subjects, of which 6 were used for model selection and the remaining 31 for validation. We find a significant performance increase (up to 14%) for the adaptive scheme compared to a fixed classier. The increase is largest for subjects with low performance.

Abstract:

Objectives:
TOBI (Tools for Brain-Computer Interaction) is an European Objectives: TOBI (Tools for Brain-Computer Interaction) is an European multicentric project supported by the European ICT Program Project FP7-224631 aiming at developing practical technology for brain-computer interaction that will improve the quality of life of disabled people and the effectiveness of rehabilitation. EEG signals are used to control a binary system.

Patients and methods : The subjects included have a motor deficit predominantly in the upper limbs (myopathy, multiple sclerosis, tetraplegia, amputation, etc.). A 16 channel EEG is used to record the signals evoked by motor imagery (closure of either hand, dorsiflexion of feet). The signals are analysed so as to select the two best reproducible. After training on BCI, the subject will choose one or more prototype to train on: 1) Communication and environment control, 2) Motor substitution, 3) Motor recovery and 4) Entertainment.
Results: Since September 2010 six subjects have participated to the study. Two with myopathy trained on mentally controlling a robot (Robotino®) and a text entry program QualiWORLD®. The performances were not identical with one or the other prototypes. Two other subjects dropped-out after few training sessions because BCI signals were altered by either bruxism or involuntary head movements. Two others subjects are undergoing BCI training. Results of the first two subjects are detailed in another communication.

Discussion: All subjects are satisfied from their participation to the project. They expressed the feeling that BCI can offer much in the future for people with severe motor deficiency. The close collaboration between the 12 participating centres to the project allowed so far the improvement of the hard- and softwares rendering the usage of BCI easier.

References
Millán JD, Rupp R, Müller-Putz GR et al. Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges. Front Neurosci. 2010;4: 1-15
Kubler, K-R. Muller. An Introduction to Brain-Computer Interfacing. In : G. Dornhege, J. d. R. Millan, T. Hinterberger, D. McFarland & K.-R. Müller (Eds.), Toward Brain-Computer Interfacing. Cambridge, MA: MIT press

Abstract:

Introduction: Our institution is one of the 12 members of the European TOBI (Tools For Brain-Computer Interaction) and one of the 4 clinics applying the technology with patients. We collaborate closely with the Ecole Polytechnique Fédérale de Lausanne. After giving their consent, subjects with severe upper limb deficiency train on brain-computer interface. Once they succeed, they can choose either to drive a robot (Robotino®) or to use a text entry program (QualiWORLD®). Presently 6 subjects have been enrolled. We present the results obtained with 2 patients suffering from myopathy; S1, a 28-years-old man and S2, a 33-years-old female.
Observations: Before each session, questionnaires evaluating motivation (VAS), mood and depression (CES-D, QCMBCI2000, VAS) are introduced. After each session, the NASA Task Load Index provides an overall workload score based on a weighted average of ratings on six subscales: Mental demand, physical demand, temporal demand, performance, effort and frustration. After the whole protocol is over, the patient’s and therapist’s satisfactions regarding the prototype is evaluated by VAS and TUEBS 1.0.
Robotino: Both had to drive the Robotono along 3 paths, from a starting point to 4 targets and back using a) the interface, b) manual switch. The time needed to perform pathway 1 was shorter with the mental command for S1 (323.66 versus 345.37 sec) while S2 had a quicker mental command for pathways 1 and 3.
QualiWorld : S2 had to write mentally numbers with 1-5 figures, and words with 1-6 letters. She did 73 mistakes to write 135 characters. She needed in average 47.83 seconds to write down one character. In spite of some disappointing results, S2 was satisfied with her performance.
Discussion: Both subjects were satisfied to discover the possibilities to mentally (Brain Computer Interaction) control assistive technology and are eager to use the technology at home. However, a third person is still necessary for using both hard- and softwares, and the latency between the command and response of the prototypes is still long. BCI leads to interesting perspectives. The concept needs improvement in performance and easiness to use.
Bibliographie :
QualiWORLD :http://www.qualilife.com/fr/accessibilite-produits/166-qualiword.html
Robotino : http://www.festo.com/cms/fr-be_be/11614.htm

Abstract:

Introduction :
Tools for Brain-Computer Interaction (TOBI) est un projet soutenu par le programme européen des technologies de l’information et de la communication (European ICT Program Project FP7-224631). Il cherche à développer des applications incorporant l’interaction cerveau-ordinateur et permettant d'améliorer la qualité de vie des personnes handicapées et l’efficacité de la réadaptation.

Méthodes : Les sujets ont une atteinte motrice majeure des membres supérieurs (myopathie, SEP, tétraplégie, amputations, etc.). Cette technologie utilise les signaux EEG pour commander un système binaire. En imaginant la sensation de trois mouvements (serrement de la main gauche, serrement de la main droite et élévation des pieds) les signaux EEG enregistrés par 16 électrodes sont analysés et traités pour sélectionner les deux meilleurs. Après une période d’entraînement, le sujet peut choisir de s’exercer sur une application parmi : 1) la communication & le contrôle de l’environnement, 2) la substitution motrice, 3) la récupération motrice, 4) les loisirs.
Résultats : Depuis septembre 2010, 8 sujets ont participé à l’étude. Deux myopathes se sont entraînés à contrôler mentalement un robot (Robotino®) et le logiciel de traitement de texte QualiWORLD®. Les performances n’ont pas été identiques avec l’un ou l’autre prototype. Quatre autres sujets n’ont pas terminé l’entraînement initial : 2 myopathes à cause de la mauvaise qualité des signaux qu’ils ont généré, 1 ataxique présentant des myoclonies cervicales et un tétraplégique pour bruxisme parasitant les signaux. Les deux derniers sujets sont en cours d’entraînement. Les résultats des 2 premiers cas font l’objet d’une seconde communication.
Conclusion: Les huit sujets sont satisfaits de leur expérience et voient en cette technologie un potentiel de développement d’applications. Grâce à une étroite collaboration entre les 12 centres partenaires et au feed-back des participants, des modifications sont apportées régulièrement pour améliorer l’interaction cerveau-ordinateur.
Références :
• Millán JD, Rupp R et al. Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges. Front Neurosci. 2010;4: 1-15
• Kubler, K-R. Muller. An Introduction to Brain-Computer Interfacing. In : G. Dornhege, J. d. R. Millan, T. Hinterberger, D. McFarland & K.-R. Müller (Eds.), Toward Brain-Computer Interfacing. Cambridge, MA: MIT press

Abstract:

Introduction : dans le cadre du consortium européen TOBI, notre clinique est un des 4 centres d’application clinique, en collaboration avec l’Ecole Polytechnique Fédérale de Lausanne. Après consentement libre et éclairé, les sujets présentant un déficit moteur important aux membres supérieurs s’entrainent à contrôler l’interface cerveau-ordinateur. Lorsqu’ils réussissent, nous proposons aux participants de s’entraîner sur les prototypes disponibles. A ce jour, 8 sujets ont participé. Quatre n’ont pas expérimenté les applications et 2 sont en cours d’étude. Nous présentons les résultats de 2 myopathes, un homme de 28 ans (S1) et une femme de 33 ans (S2) qui ont réussi à conduire un robot mobile (Robotino®) et, pour l’un d’entre eux (S2), écrire un texte simple avec l’interface QualiWORLD®.

Méthodes: avant chaque séance, des questionnaires évaluent la motivation (EVA), l’humeur et l’état psychique (CES-D, QCMBCI2000, EVA). A la fin de chaque séance, sont mesurés les charges physique, mentale, temporelle, la performance, l’effort et la frustration (NASA Task Load Index). Au terme du protocole nous vérifions la satisfaction du sujet à propos du prototype (EVA, TUEBS 1.0) et celle du thérapeute (TUEBS 1.0).

Résultats : le sujet guide le robot sur 3 parcours prédéfinis en utilisant l’interface comparé à l’utilisation d’un interrupteur. S1 a réalisé plus rapidement le parcours 1 par contrôle mental (323.66 versus 345.37 sec) tandis que S2 a une meilleure performance mentale pour les parcours 2 et 3. En utilisant QualiWORLD, S2 a écrit par la pensée des nombres de 1 à 5 chiffres et des mots de 1 à 6 lettres ; elle a fait 73 erreurs pour écrire 135 caractères. Le temps nécessaire pour écrire un caractère était de 47.83 sec. Malgré les résultats un peu décevants, S2 était satisfaite de sa performance.

Conclusion : les 2 sujets sont satisfaits de découvrir les possibilités de contrôler l’environnement par la pensée (interaction cerveau-ordinateur), et se voient utiliser le prototype testé à domicile. Le consortium s’efforce de faciliter l’utilisation du hard-/software et des prototypes notamment en diminuant le temps de latence entre l’ordre et la réponse de l’appareil. L’interface cerveau-ordinateur ouvre des perspectives intéressantes.

Références :
QualiWORLD :http://www.qualilife.com/fr/accessibilite-produits/166-qualiword.html
Robotino : http://www.festo.com/cms/fr-be_be/11614.htm

Abstract:

Brain-Computer Interfaces (BCIs) allow for muscle independent communication and control. However, individuals differ in their ability to use a BCI for which psychological variables may account. To investigate the relevance of psychological influencing variables such as emotion and motivation we examined the relation between these variables and the ability to learn using a BCI based on sensorimotor rhythms (SMR). A sample of N=20 participants was instructed to steer a moving cursor from the left to the right edge of a computer screen into a marked area either on top or bottom of the screen by imagining movement of the left or right hand or feet (=one SMR-BCI trial). We trained participants for three sessions on three different days, each containing 9 blocks of 25 trials per block. Motivation was manipulated by rewarding participants with an extra 5 Eurocents per successful trial or not rewarding them at all (each group N=10). Out of each of the two groups (motivated and unmotivated) five participants were emotionally manipulated by either showing them a sad or neutral video and afterwards playing either sad or neutral music during the BCI task. Preliminary results reveal no effect of emotion manipulation but of motivation manipulation. We found a trend for rewarded participants to be more successful (=percentage of successful trials) in SMR-BCI use compared to not rewarded participants (Wilcoxon, p=.13). The results indicate that motivation may explain some of the variance in SMR-BCI performance and should be monitored in BCI settings.

This work is supported by the European ICT Programme Project FP7-224631. This paper only reflects the authors' views and funding agencies are not liable for any use that may be made of the information contained herein.

Abstract:

Introduction - Graph theory has been introduced as a novel method to study the functional brain
networks in a wide range of neurological disorders.
Objectives - We propose a methodological approach to assess the functional brain network
organization underlying motor imagery (MI) after stroke and to evaluate the effects of MI-based
brain computer interfaces (BCIs) as adjunctive rehabilitation strategy.
Methods - High-density EEG data were collected from 15 stroke patients (band-pass 0.1-70 Hz;
frequency sample 200Hz), who were asked to imagine grasping with unaffected (UH) and affected
hand (AH). Functional brain networks were estimated by computing the imaginary coherence; the
values obtained were stored in a channel-channel matrix, in which only those links that were
significantly different between rest and task were considered. The EEG functional connectivity
matrices were characterized by means of a graph theoretical approach by computing node degree,
global (Eg) and local efficiency (El).
Results - The EEG network patterns estimated for the Beta band (14-29 Hz) showed that MI of the
UH was associated with an unbalance of the overall connectivity in favor of the unaffected
hemisphere. This asymmetry was absent during MI of the AH. The Eg values were similar during
the UH and AH MI, while El was smaller during MI of AH, suggesting that information transfer is
more clustered in the UH condition.
Discussion - Our findings indicate that MI after stroke is associated with abnormalities of the
functional brain network in the Beta band, which is known to be involved in motor acts. We
propose this methodology as valuable to assess functional reorganization of the motor cortical
system after stroke promoted via a MI–based BCI training.
Acknowledgements - This work is supported by the European ICT Programme Project FP7-224631
(TOBI). This paper only reflects the authors' views and funding agencies are not liable for any use
that may be made of the information contained herein.

Abstract:

Introduction - Electroencephalographic (EEG)-based BCI (Brain Computer Interface)
technology operated via motor imagery (MI) appears a unique option to promote
motor recovery after stroke. The rationale beyond this application relies on the
evidence that mental rehearsal of motor actions elicits a certain degree of
sensorimotor cortical activity.
Objectives - To address the issue of which are the most appropriate EEG patterns to
be reinforced via a BCI training and to determine how we can optimize the detection
of MI-related EEG signals generated from the damaged motor cortical regions.
Methods - High-density EEG signals (band-pass 0.1-70 Hz; frequency sample 200Hz)
were collected from 15 patients with first ever monolateral stroke. Patients were asked
to either image (MI) or execute (ME) grasping with unaffected (UH) and affected
hand (AH), being instructed by a visual cue. Power spectral density was computed for
each channel and frequency bin (1-60Hz, 2Hz resolution). The obtained values were
compiled in a channel-frequency matrix and averaged to highlight the most common
EEG pattern for each motor task.
Results - ME and MI of UH grasping generated EEG patterns consistent with the
physiological responsiveness to such motor tasks. ME and MI of AH were associated
with a significant desynchronization (lower R-square values than UH) of the
sensorimotor rhythms localized over the scalp contralesional centro-parietal areas,
with a concomitant desynchronization over the midline frontal, central and parietal
electrodes for MI.
Discussion - Our findings indicate that ME and MI-related desynchronization of the
alpha and beta rhythms occurs with a broader spatial pattern and less magnitude when
the AH is engaged in the motor task.
Acknowledgements - This work is supported by the European ICT Programme
Project FP7-224631 (TOBI). This paper only reflects the authors' views and funding.
agencies are not liable for any use that may be made of the information contained
herein.

Abstract:

With Brain-Computer Interfaces (BCI), it is possible to establish a communication pathway that solely relies on neural activity. Since visual BCI paradigms are not suitable for users with vision impairments (e.g. late-stage ALS patients), auditory speller paradigms have recently been investigated.
We propose two auditory ERP speller paradigms that were recently developed at the BBCI group in Berlin. Both paradigms utilize spatial auditory stimuli and were successfully tested in online studies with healthy participants.
The AMUSE paradigm (Schreuder et al., 2010) consists of a ring with 6 audio speakers around the subject which produces stimuli in a pseudo-random order. In the PASS2D paradigm (Höhne et al., 2010), nine auditory stimuli are presented using standard headphones in a pseudo-random order. Stimuli vary in pitch (high/medium/low) and direction (left/middle/right).
For both paradigms it was found that focusing attention to any of the stimuli leads to reliable elicitations of class-discriminative N200 and P300 signals in the EEG, that are strong enough to drive a speller application. For the AMUSE paradigm, the spelling procedure is based on six control signals and follows an adapted version of the Hex-o-Spell text entry system. In the PASS2D paradigm, the spelling procedure is based on nine control signals, being supported by a predictive text system that is intuitive to understand and well known from mobile phones.
On average, a spelling speed of more than 0.8 char/min and more than 3 bits/min was achieved. Thus, both spellers are competitive paradigms even though they are independent of visual input. These paradigms are promising to provide a new communication channel for BCI end-users such as late-stage ALS patients that are unable to use visual spellers.

Abstract:

A Brain-Computer Interface (BCI) strives to provide a channel for communication and control for patients lacking motor control. In a BCI system a user's intention is interpreted based on changes of his/her brain activity, which are assessed e.g. by recordings of the electroencephalogram (EEG).
The most widely used BCI paradigms utilize the event related potential (ERP) response of the EEG upon a series of target/non-target visual stimuli in an oddball setting. However, users with remaining sight and gaze control can use eye tracking systems for communication. BCI paradigms can at most offer an alternative communication channel for them. If the ability to observe visual stimuli and receive visual feedback for actions is decreased (e.g. for late-stage ALS patients lacking a stable control of gaze direction), the range of available BCI paradigms is restricted to imagery tasks or the auditory modality.
We propose two new multiclass (6 and 9 classes) auditory ERP approaches for spelling with a BCI that offer alternative solutions to target patients with sight deterioration. They have successfully been evaluated in two online studies with 31 healthy users and one blind user. In both paradigms, the coding of control commands for text entry utilizes differences in pitch and spatial direction. Strong discriminatory auditory evoked potentials (AEP) were observed for target stimuli even during rapid stimulation sequences (≤200ms SOA). Utilizing machine learning principles to classify the ongoing brain activity, the two approaches realize competitive information transfer rates (2.8 to 7.5 bits/min) during both, copy spelling and free spelling tasks.

Abstract:

A common strategy in Brain-Computer Interface (BCI) research is to first investigate paradigms and data analysis methods in the lab with healthy subjects, before applying them to target users. While research prototypes might neglect user centered design principles, they become important in end user tests. Thus we investigated design parameters of visual and auditory stimuli for fast event-related potential (ERP) BCIs.

In the visual domain, six highlighting schemes were compared in a row-column paradigm: brightness, scaling, rotation, color inversion, grid overlay and a combination thereof. Calibrating a media application, the effects were used to enhance rows and columns of photos. EEG data of healthy users (n=6) was collected together with subjective ratings of the effects. ERP analysis revealed that different effects lead to changed distributions of discriminative information. In the light of individual preferences, the careful selection of an effect per subject is advisable, as it improves the estimated classification accuracy.

In the auditory domain, samples of spoken phonemes were compared to artificial stimulus tones. Brisk phonemes ('ta', 'to' or 'it') of 3 human speakers resulted in 9 different stimuli. Despite of a more diffuse temporal structure, phonemes represent over-trained stimuli and should thus be easy to perceive. They were used to drive a 9-class BCI with spatial cues.
In contrast to a setup with artificial tones, all participants (N=5) judged the phonemes as pleasant and easy to concentrate on. Observed discriminative N200 and P300 ERPs of the EEG were very similar to those of tones, and as the classification performance was almost equal for both types of stimuli, phonemes are considered a good choice for future patient tests.

Abstract:

Brain-Computer Interfaces (BCI) aim at providing a means of communication and control, mediated via the detection and decoding of specific brain states. In order to reach practical applicability, the classifiers of brain activity employed in a BCI must work reliably and robustly over extensive periods of time.

In this contribution, we investigate fluctuations of classifier performance that occurred during the online feedback phase of a BCI spelling application (Hoehne et al. 2010), which was based on the auditory evoked potential (AEP). For this purpose, the experimental data of 10 participating subjects is re-analyzed offline.

First a continuous measure for classifier performance is defined. The measure is based on classification rates of individual stimulus presentations. Then the linear interactions between the performance measure and band power features of various frequency bands are determined.

We find positive correlations between the performance measure and parietal-occipital alpha power (8 to 14 Hz). Furthermore, we observe negative correlations between the performance measure and the power of central (centro-parietal) low (high) range gamma oscillations. The mentioned findings reach significance on the group level analysis as well as in the majority of individual subjects.

The observed negative correlations in central and parietal gamma oscillations are in line with recent findings on the relation between gamma power and performance in an entirely different BCI paradigm, namely motor imagery (Grosse-Wentrup et al. 2010). Thus, our results stress the importance of neural oscillations for information processing
in the brain.

Abstract:

Abstract. We investigated the accuracy, user satisfaction and user evaluation the of the P300 BCI-Brain Painting application. End users with severe disabilities (N=4) showed comparable high accuracies in copy spelling, copy painting and a free painting session. Users reported the wish to use the application in daily life but the hardware (EEG-cap, electrodes, gel) was felt to impose the greatest obstacle.
Keywords: Brain Computer Interface (BCI), Brain Painting, user evaluation, user satisfaction

Abstract:

Using Brain Computer Interfaces (BCIs), one can establish a communication pathway which does not rely on muscular activity. This is a promising tool for patients suffering from neurodegenerative diseases as well as for patients with locked-in syndrome. Neural degenerative diseases such as Amyotrophic Lateral Sclerosis (ALS) also cause a deterioration of vision, in particular a loss of gaze fixation. Since visual BCI paradigms might not be suitable for these patients [1], auditory speller paradigms were recently investigated [2-3]. Spatial hearing has been introduced as informative cue in a BCI paradigm [2], revealing that subjects are able to focus attention to a target direction. Two online studies were performed to investigate the usability of speller paradigms with spatial auditory cues.

Abstract:

INTRODUCTION
Reasons for large inter-individual differences in people’s ability to use a brain computer interface (BCI) are not yet understood and predictors for BCI performance would be advantageous for the selection of users and EEG parameters. For BCI-based communication by spelling, paradigms making use of the P300 evoked potential are widely used. Success in a P300 based BCI requires the ability to cognitively modify the focus of attention and to sustain attention. Such attentional control has been closely linked to peripheral physiological parameters, such as the heart rate variability (HRV). The present study investigated the association between resting HRV and performance in the P300 BCI and furthermore investigated the robustness of the resulting predictive regression model.

METHODS
Electrocardiogram (ECG) of 25 healthy participants was recorded and a P300 based BCI spelling task performed. HRV resting baseline was determined for five minutes before BCI performance. The BCI task consisted of a calibration run and subsequently runs with given feedback. Participants were required to spell 12 words of five letters each (12 runs). The runs were interspersed with 30 second breaks (recovery times). Time and frequency domain of the resting HRV was analysed.


RESULTS
Performance was determined as the percentage of correctly spelled letters (Min=73,0%, Max=100%, Mean=94,9%, SD=6,25). Autonomic balance (ratio of low frequency (LF, mainly sympathetic) and high frequency (HF, parasympathetic) band) and other HRV measures were associated with BCI-performance such that subjects with higher HRV performed better (r=-.52, n=25, p<.01). Robustness of the regression between HRV and performance was validated to guarantee a stable prediction of BCI performance based on HRV recordings. Given the moderate sample size, an iterative check of robustness was performed to proove robustness of the linear regression model. Participants with success rates of 100% were excluded to compensate for ceiling effect related biases. Applying an iterative loop, data overly (>3x SD) affecting the regression model were identified. After exclusion of these data, a new regression model resulted (r=-.51, n=16, p<.05) confirming the previously found effect size in the remaining sub-sample.

DISKUSSION
This work shows that HRV, which is easy to measure, might predict successful performance with the P300 BCI system used here, across an respectalbe number of subjects. These results contribute to a better understanding of interindividual differences in BCI performance in healthy individuals and potentially also in clinical samples.
Giving the proven robustness to individual outliers and ceiling effects, autonomic balance proved to be a stable and reliable predictor of BCI performance in the P300 BCI paradigm, explaining approx. 25% of performance variance.

Support
This work is supported by the European ICT Programme Project FP7-224631. This paper only reflects the authors’ views and funding agencies are not liable for any use that may be made of the information contained herein.

Abstract:

BACKGROUND AND OBJECTIVE
The Brain-Computer Interface (BCI) systems of today have primarily been developed to replace the lost abilities of patients diagnosed with motor-neuron diseases such as amyotrophic lateral sclerosis (ALS). Of these lost abilities, the most important seems to be that of communication [1], represented by the increasing volume worldwide of research and development into such applications. Another valuable element of human life however is that of creative expression. Modification to the P300-BCI communication system has yielded an application which provides the ability for such expression, Brain Painting.

METHODS
Brain Painting [2] works by replacing individual fields in a P300-BCI based control matrix with painting functions, such as cursor control, shape, size and colour selection to produce images of an abstract nature (see Fig. 1). Brain Painting is now used regularly by several ALS patients (n=4) throughout Germany as a form of entertainment and as a way to satisfy the desire for creative articulation in their own homes. Furthermore, prominent German artists (n=6) have been invited to use Brain Painting at their ateliers. Constructive feedback has been collected from these various users in an effort to improve the application. Important metrics being assessed are ensuring the application is intuitive to use, robust in reliability and practical for unsupervised use in daily life.

RESULTS
Quantitative results initially suggest minor performance variations between ALS-patients and healthy subjects. Healthy participants display prominent P300 responses thus requiring 20% less repetitions (12) in comparison with patients (15). A higher repetition of flashes is used to ensure a higher selection accuracy which is valued over speed. Qualitatively are the results outstandingly positive, enthusiastic comments from both patients and artists confirm that importantly they experience satisfaction and are entertained when using the application, with a repeated strong desire to re-use the system. To date, patients using the system have produced numerous images from independent sittings lasting upwards of 1.5 - 2 hours (Figure 1). This is evidence for a BCI solution that serves to satisfy some basic human needs by providing a positive, useful difference and a better Quality of Life to ALS-patients

DISCUSSION AND CONCLUSIONS
Brain Painting satisfies some basic human needs and it is hoped that by providing an ability to be productive again this will assist with improving the Quality of Life for patients with severe motor disabilities. Although the Information Transfer Rate appears low in the Brain Painting application, the high number of stimulus repetitions is a result of a need for a high selection accuracy. During the repetitions the user can concurrently reflect on the next stages and their paintings development. Modifications to the application and stimulus presentation are being initiated to improve the reliability and prominence of the P300 response amongst patients where life sustaining apparatus presents a challenge to the mounting or operation of BCI devices. Thus acceptance of Brain Painting within the artist community and most importantly the patient community is a key factor of the project to ensure its long term success.

REFERENCES
[1] Kübler A. et al. Brain-Computer Communication: Unlocking the Locked In. In Psychol. Bull 2001, pp.358-375

[2] Kübler A. et al. A. Brain Painting - BCI Meets Art. In 4th International BCI Workshop and Training Course, Graz, 2008.

This work is supported by the European ICT Programme Project FP7-224631. This paper only reflects the authors’ views and funding agencies are not liable for any use that may be made of the information contained herein.

Abstract:

Background and Objective
Experiments with animals and humans starting in the late sixties demonstrated that physiological functions that were believed to be autonomous, such as glandular responses, blood pressure, and the electrical activity of the brain (EEG) could be braught under voluntary control via operant conditioning (1,2). After it has been repeatedly shown that subjects could produce clearly distinguishable brain responses on command the idea was at hand to use this ability for communication in people who are in the so-called locked-in state with only residual muscular movement left for communication (3,4). In contrast to the neurofeedback approach which involves learning to control a component of the EEG, the machine learning approach aims at detecting patterns of activation in the brain that can be readily produced by the individual (5). Several authors claim that BCI control consitutes a skill (6,7), but a skill requires learning and improvement with practice (8). With the here presented review of BCI studies we were aiming at answering the following questions: (1) how much evidence for learning is provided in BCI studies, (2) how is the course of learning with a BCI, and (3) how stable is performance over time.

Methods
We performed an exhaustive literature review beginning in the late sixties up to now. Search terms were e.g., brain-computer interfaces, neurofeedback, self-regulation, slow cortical potentials, P300, SMR, SSVEP, SSSEP in various combinations. We restricted our review to non-invasive and ECoG studies in humans. EEG, fMRI, MEG, and NIRS were included as a measure of brain activity.

Results
First analysis of a subset of studies (N=137) revealed that in most studies which report online results motor imagery is used for BCI control. Most studies that involved long term training with severely impaired patients relied on slow cortical potentials. The vast majority of studies comprised healthy subjects who typically participated in 1-4 BCI sessions. Although it is often stated that „individuals learned to control the BCI“ and „performance improved with time“ learning curves are hardly ever presented. Only few studies conducted trials long enough for learning to occur. In such studies it can be seen that learning to control a BCI either follows the typical power or a linear trend.

Discussion and Conclusions
We conclude that most BCI studies or BCI approaches do not involve human learning. Those which do, rely on neurofeedback to achieve self-regulation of the SMR- or SCP amplitude. In some of those studies performance followed a power trend indicating strong improvement of performanc at the beginning of training followed by asymptotic performance with practice. The linear trend indicated constant, slow learning more often seen in patients. The ability to control a BCI appeared to be stable over time even when motor impairment increased. In further studies it could be investigated whether control of such BCIs that rely mainly on pattern recognition could be improved with practice.

References
(1)Miller, NE (1969). Science 163(866):434-45
(2)Nowlis DP, Kamiya J. (1970). Psychophysiology 6(4):476-84
(3)Kübler A et al. (1999). Exp Brain Res 124(2):223-32
(4)Neuper C et al. (2003). Clin Neurophys 114(3):399-409.
(5)Krauledat, M et al. (2008). PLoS One 3(8):e2967.
(6)Neumann N et al. (2004). Clin Neurophys 115(3):628-35
(7)Wolpaw JR, McFarland DJ (2004). PNAS 101(51):17849-54.
(8)Logan GD (2002). Psychol Rev. 109(2):376-400.

Support
This work is supported by the European ICT Programme Project FP7-224631. This paper only reflects the authors’ views and funding agencies are not liable for any use that may be made of the information contained herein.

Keywords
BCI, learning, performance, skill

Abstract:

Background and Objective
Brain-Computer Interfaces (BCI) may provide muscle-independent communication for severely paralyzed patients. However, individuals differ in their ability to use a BCI. To elucidate how psychological variables such as motivation influence BCI performance in healthy subjects and ALS (amyotrophic lateral sclerosis) patients, we investigated the relation between motivation and performance in a P300 based BCI. Furthermore, we were interested whether motivation would affect the P300 amplitude.

Methods
A group of N=33 healthy subjects was included. Participants were instructed to copy spell a sentence with 29 characters using visual ERP-BCI. With stepwise linear discriminant analysis the target character was determined and fed back to the participant. Motivation was manipulated with monetary reward. In two experimental groups participants received 25 (N=11) or 50 (N=11) Euro Cent for each correctly selected character; the control group (N=11) was not rewarded. Motivation was assessed with the BCI adapted FAM (FAM-BCI) questionnaire and a visual analogue scale (VAS).
In a group of N=15 ALS patients motivation was manipulated with a 20 Euro gift certificate for an internet store. In a first run participants spelled a 14 character sentence without receiving a reward. In the second run they were promised a gift certificate for achieving a better performance compared to the first run. Motivation was assessed with the FAM-BCI and the VAS.
BCI performance was in both groups defined as the overall percentage of correctly selected characters (correct response rate=CRR).

Results
In the healthy subjects group monetary reward did not affect motivation and BCI performance. All participants performed at almost 100 % CRR (M=99%). However, at Cz the P300 amplitude was positively related to self-rated motivation (ρ=.50; Spearman). The P300 amplitude of the most motivated participants (measured by VAS) was significantly higher than that of the least motivated participants. Offline analysis revealed that, highly motivated participants would have needed fewer trials for a discriminable ERP and thus, would have been able to communicate faster with the ERP-BCI.
In the ALS patients group we found a high negative correlation between the number of sequences needed to spell 70% correctly and the motivation (measured with VAS, r=-.78, p<.05). CRR comparison before and after motivation manipulation strengthened this result (Z= -1.86, p= .06).

Discussion and Conclusion
Results indicate that motivation may explain some of the variance in BCI performance and P300 amplitudes. Therefore, motivation should be monitored in BCI settings. If these results can be confirmed in further studies motivation could become an integral part of BCI training protocols.


Support
This work is supported by the European ICT Programme Project FP7-224631. This paper only reflects the authors’ views and funding agencies are not liable for any use that may be made of the information contained herein.

Keywords
BCI, MOTIVATION, ERP, ALS

Abstract:

Background and Objective:
Many Brain-Computer Interfaces (BCI) rely on the regulation of sensorimotor rhythms (SMR) of the EEG by means of neurofeedback. According to Lacroix’s Two-Process Theory of neurofeedback learning, subjects either identify efferent programmes already in their behavioural repertoire which allow them to achieve control over the targeted physiological signal. If so, subjects use the provided feedback to refine these strategies. If no strategy is available afferent processes underlie the acquisition of autonomous control to construct a new behavioural programme (1). The second process equals the operant conditioning approach in which physiological responses are altered and if this manipulation leads to the desired result these responses are positively reinforced and subsequently more often applied. In the current study, we aimed at investigating how learning to control an SMR-based BCI differs as a function of the process – efferent vs. afferent - by which such control is instantiated. We hypothesized (1) that subjects who are instructed to imagine a movement present with a higher initial performance and that for this reason (2) the course of learning would be less pronounced. Further, we predicted that (3) the end performance would not differ between the groups.

Methods:
Nineteen healthy BCI novices were randomly assigned to an “efferent” group (EG) and an “afferent” group (AG). EG subjects were instructed to kinesthetically imagine the movement of their right hand. The AG was instructed to focus their attention on the feedback signal and observe how its trajectory changes in relation to different thoughts. In both groups subjects were required to move a cursor into one of two targets. A decrease of the power in the alpha band over EEG channel C3 moved the cursor to the bottom target, and an increase of the power to the top target. The EEG was recorded with a 16 channel EEG amplifier. Correct response rate (CRR) in % served as measure of performance.


Results:
Initial and final performances were 61.9/67.36% in the EG and 50.9/62.8% in the AG. Performance was significantly better in the last session when compared with the first session (F(1,17) = 8.7, p < .01), but the group differences and interaction were not significant (2 (session) x 2 (group) repeated measures ANOVA). Both groups learned significantly during the course of 10 sessions (linear trend EG: F(1,8) = 9.7, p < .05; AG: F(1,8) = 139.5; p < .001; power trends also significant). Learning was more pronounced in the afferent group (F(1,17) = 397.3, p < .001).

Discussion and conclusion:
As expected, both groups learned and the increase in learning rate was greater in the afferent group. Although, on a descriptive level, the efferent group performed better in the first session compared to the afferent group, this difference failed to reach significance. End performances were not significantly different between the groups. The high variance in performance and the low overall performance which is atypical for SMR-BCIs may have been due to not determining the best motor imagery and the best electrode for each subject. However, a screening session to identify which motor imagery works best for each subject at which electrode could not be performed in this study, as it would have revealed the motor imagery strategy to all subjects. We conclude that motor imagery is not necessary to regulate the SMR amplitude and cautiously, that providing a strategy only improves the initial performance, which might be important to sustain motivation for BCI training.

Abstract:

Using BCI as a tool for feedback training requires a fast and easy acquisition of a reliable classifier to detect the appropriate activation patterns. The usual practice for training a classifier is to record EEG during MI without giving feedback and use this data to calculate a classifier. For a stroke BCI feedback training it would be advantageous if we could give appropriate feedback from the beginning. Hence, new strategies for setting up a classifier are needed. As is known from previous work the activation patterns (event related desynchronization ERD) of the sensorimotor cortex during active and passive movement and during MI are quite similar. Therefore, it should be possible to use data from active or passive movement to set up a classifier for the detection of MI. We already know that it is possible to use data from foot motor execution to set up a reliable classifier for the detection of foot MI. In this study we explore, whether a similar strategy could be applied to data from active and passive hand movements in a group of elderly persons.

EEG was recorded from three Laplacian channels over the sensorimotor cortex (C3, Cz, C4) in a sample of 20 healthy elderly volunteers. Participants performed three different tasks, passive hand movement (performed by a hand robot, Tyromotion, Graz Austria), active hand movement and hand MI. Classifiers (task against rest) were calculated with data from every task by means of a linear discriminant analysis. Relevant features were selected with distinction sensitive learning vector quantization. In the next step the calculated classifiers were used to detect ERD in offline MI data.

The mean performance of the classifiers in detecting MI against rest was above random. The performance of classifiers calculated from passive and active hand movement data did not differ significantly regarding the classification accuracy for detecting MI.

In this study, we have shown that it is possible to use classifiers calculated with data from passive and active hand movement to detect MI. The advantage of this approach is that passive and active movements are part of the normal stroke rehabilitation. For working with stroke patients, a physiotherapy session would be used to obtain data for classifier setup and the BCI rehabilitation training could start immediately. In a next step, we want to test this approach in stroke patients. First sessions with patients will start shortly.

"This work is supported by the European ICT Program Project FP7-224631. This paper only reflects the authors' views and funding agencies are not liable for any use that may be made of the information contained herein."

Abstract:

Background and Objective
Applying Brain Computer Interface (BCI) technology in real-world applications demands identification of brain patterns in an asynchronous manner in order to achieve a natural way of humancomputer interaction. So far, most of the existing BCI systems are operated under a synchronized, cue-based paradigm, imposing time and speed constraints on the subject, as well as forcing him/her to be continuously under BCI control with no possibility to voluntarily relax or shift his/her attention to other activities. Identifying those non-intentional control (NIC) intervals is an open challenge attracting growing interest in BCI [2]. Our BCI system is based on an asynchronous control paradigm, where the user learns to voluntary modulate EEG oscillatory rhythms by executing different mental tasks. This abstract refers to the statistical machine learning techniques applied under this framework to enable accurate classification of the mental tasks and identification of NIC.

Methods
Three blocks in our mental imagery-based BCI are responsible for enabling NIC and multitasking capabilities to the user: a) stable subjectdependent feature selection b) sample rejection on the classifier output and c) evidence accumulation over time. Feature selection is based on Canonical Discriminant Spatial Patterns (CDSP) [1] aiming at selecting those features that are the most discriminant and stable across recording sessions for a specific subject. While on-line, a rejection threshold is set on the probability distribution over the mental classes emitted by the classifier, thus filtering out decisions with low confidence. Last but not least, surviving decisions are not immediately translated into commands, but rather treated as temporary evidence on the executed task and accumulated using an exponential smoothing probability integration framework. A command is finally delivered by thresholding the integrated probability distribution. The desired effect of the described approach is that although the “unknown” state is not explicitly encoded in the classifier and the system is always monitoring and classifying the subject’s brain patterns, he/she is generally able to avoid delivering unintentional commands while occupied with tasks other than controlling the system, such as talking to fellows, without degrading delivery speed and accuracy of intentional commands.

Results
A preliminary study with 3 subjects supports the above mentioned claims. The subjects were instructed to operate the BCI during an online experiment including 2 NIC trials (20 seconds each) and 4 intentional control (IC) trials (10 seconds each) per run. For each run we measured the average number of intentionally delivered commands, both correct (9.8) and erroneous (0.30), as well as the average number of non-intentional decisions during NIC (4.1). During NIC trials, subjects had not to perform any activity. We can see that, on average, people can deliver almost double mental commands during IC than during NIC. Furthermore, the number of errors during IC is negligible. It is also worth noting that one of the subjects was novel who run a BCIexperiment for the first time. He had an excellent IC, but was rather poor during NIC, most probably due to the lack of training. It should be mentioned that the parameterization of the system (decision and rejection thresholds and integration smoothing factor) has been fixed to conventional values for all the subjects.

Discussion and Conclusions
The difficulty of separating specific mental-task related brain patterns in multi-class problems has motivated the use of powerful discriminant classifiers for asynchronous BCI systems. Hence, conventional systems are designed to separate a known number of mental tasks and not to handle arbitrary patterns as those generated during non-intentional control. Encoding such a state as an additional class in the classifier is known to degrade IC performance. However, the presented approach provides the means to achieve a satisfying degree of NIC on top of such a discriminant function, thus not sacrificing robustness and accuracy. Besides the results reported here we have strong evidence in the same subjects, that NIC improves when the subject is engaged in other mental tasks or when he is multitasking, such as speaking with other people. Moreover multitasking did not affect the IC performances. Results with 10 subjects will be reported at the time of the conference.

References
[1] F. Galan et al. Feature extraction for multi-class BCI using canonical variates analysis. In IEEE Int Symp Intelligent Signal Processing, 2007.
[2] R. Leeb et al. Self paced (asynchronous) BCI control of a wheelchair in virtual environments: A case study with a tetraplegic. Computational Intelligence and Neuroscience, 1–12, 2007.
Acknowledgments:
This work is supported by the European ICT Programme Project FP7-224631.

Abstract:

BACKGROUND and OBJECTIVE
The Defitech Foundation Chair in Non-Invasive Brain-Machine Interaction carries out research on the direct use of human brain signals to control devices and interact with our environment. In this multidisciplinary research, we are bringing together our previous work on the two fields of BCI and adaptive intelligent robotics. The goal is to develop intelligent brain-actuated devices that people can efficiently operate them in a natural and intuitive manner over long periods of time. Such neuroprosthetic devices will allow interaction by exploiting brain signals associated to different aspects of voluntary behavior.

METHODS
Current EEG-based BCIs are limited by a low information transfer rate and are considered too slow for controlling complex devices. However, we have shown that online asynchronous analysis of spontaneous EEG signals, if used in combination with statistical machine learning techniques and smart interaction designs, is sufficient for humans to do so. Furthermore, thanks to the principle of mutual learning —where the user and the BCI are coupled together and adapt to each other— humans learn to operate the brain-actuated device very rapidly, in a few hours normally split between a few days.

Accordingly, our research is based on the following principles for brain-computer interaction [1], namely (1) asynchronous protocols, (2) mutual learning, (3) shared control, where the user conveys high level mental commands that the devices interpret and execute in the most appropriate way to achieve the goal. This is particularly effective for the control of robots and neuroprostheses. Still a fourth principle is to exploit the fact that EEG not only conveys information about the subject’s intent (the mental commands), but also about cognitive states that are crucial for a purposeful interaction. All this is done in parallel. An example of such a cognitive state is the user’s awareness to errors made by the BCI. Recently we have demonstrated its online use embedded in a BCI, which yields enormous increases in performance [2]. In summary, the last principle is (4) cognitive states.

Our approach relies on the extensive use of statistical machine learning techniques at three levels. First, the rapid identification of individual stable discriminant features the user can naturally modulate. Second, the design of powerful statistical classifiers (a Gaussian classifier) to discriminate each EEG sample. Third, the probabilistic smooth integration of the classifier outputs to accumulate evidence about the user’s intent [3]. Only when this evidence is high enough, a mental command is delivered to the brain-actuated device. The major point of our framework is the generalization and stability of the selected features to be invariant against fluctuations in the EEG over time.


RESULTS
Over the last years we have developed a wide range of prototypes for mental control (via motor imagery) of a variety of devices: keyboards, games, robots, hand orthoses, and wheelchairs [3]. In addition, we have demonstrated that the recognition of mental states can be successfully used to implement adaptive capabilities in BCI. Most of these devices are relevant for paralyzed humans, but they also open up new possibilities for able-bodied people suffering from “situational disability” such as in space applications —as shown by our recent feasibility study of non-invasive BCI during parabolic flights [4].

In all cases, our framework yields stable long-term performance (several months) and allows subjects to operate different brain-actuated devices, whose control paradigms and associated workloads are quite different, using the same EEG features and classifier. Furthermore, some subjects have demonstrated they can deliver appropriate mental commands only when they wish to do so and while performing other tasks such as speaking.

DISCUSSION and CONCLUSIONS
In summary, we are trying to develop principled methods to design intelligent brain-actuated devices so as to achieve an effective brain-computer interaction and reduce the user’s cognitive workload. A recent extension of our framework is the combination of BCIs with existing assistive technologies in order to have a real impact in improving the quality of life of disabled people. In such a hybrid BCI users can merge brain signals with other physiological signals or can switch between different channels naturally (based on monitoring of physiological parameters or mental states). A key element is the design of principled methods for multimodal fusion.

ACKNOWLEDGMENTS
This work is supported by the European ICT Programme Project FP7-224631 and ICT-2007-225938.

REFERENCES
[1] J. del R. Millán, IEEE Int Syst, 2008.
[2] P.W. Ferrez, et al., IEEE Trans Biomed Eng. 2008.
[3] F. Galán, et al., Clin Neurophysiol, 2008.
[4] J. del R. Millán, et al., Int Rev Neurobiol, 2009.

Abstract:

BACKGROUND and OBJECTIVE
The development of practical BCIs for disabled people should allow them to use all their remaining functionalities as control possibilities. Sometimes these people have residual activity of their muscles, most likely in the morning when they are not exhausted. One of the goals of the TOBI project (www.tobi-project.org) is to use and fuse muscle and brain activity (or even additional channels) in the so called “Hybrid-BCI” (hBCI) approach, depending on their availability and reliability. In this experiment we are combing electromyographic (EMG) with electroencephalographic (EEG) activity for a control task.

METHODS
Up to now four healthy subjects participated in an experiment where the task was to control a synchronous BCI with either EEG or EMG. The brain activity was acquired via 16 EEG channels over the motor cortex. From the Laplacian filtered EEG the power spectral density was calculated and the selected features were classified with a Gaussian classifier [1]. Four EMG channels were recorded over the flexor and extensor of both forearms. The prehensile EMG activities were rectified, averaged (0.3 s), thresholded, normalized and classified based on the maximum distance. The task of the participant was to drive a liquid cursor feedback either to the left or right depending on a cue during 5 seconds (60 trials each, 64 decisions/samples per trial). The feedback can be controlled either by one modality alone (EEG or EMG) or by the fused activity of both. Furthermore, to simulate fatigue of exhausted muscles, the amplitudes of the EMG channel were degraded over the run time (attenuation from 10% up to 100%), so that the EEG activity became more and more important in the fusion.

RESULTS
The number of correctly classified samples over the trial time of all subjects in case of EEG or EMG activity alone were 77% and 83%, respectively. For the fused activity an increase to 91% could be achieved. Remarkably, thanks to the fusion of EEG and EMG, increasing muscular fatigue (from 10% to 50% to 90% attenuation) led to a moderate and graceful degradation of performance: 90%, 84% and 77% accuracy, respectively.

DISCUSSION and CONCLUSIONS
This first simple experiment demonstrates multimodal fusion of muscular and brain activity for a Hybrid BCI. Thereby, subjects could achieve a good control of their hBCI independently of their level of muscular fatigue. Furthermore, although EMG alone yielded good performance, its combination with EEG improved it. Therefore such a system allows a very reliable control and a smooth handover if the subjects gets exhausted during the day.
The results of ten subjects will be reported at the workshop.

REFERENCES
[1] Galán, F., Nuttin, M., Lew, E., Ferrez, P. W., Vanacker, G., Philips, J. & Millán, J.d.R. A brain-actuated wheelchair: Asynchronous and non-invasive Brain-computer interfaces for continuous control of robots. Clin Neurophysiol, 119: 2159-2169, 2008.

KEYWORDS
BCI, EEG, EMG, multimodal, fusion

Abstract:

BACKGROUND AND OBJECTIVE

The EU-project “Tools for Brain-Computer Interaction” (TOBI) aims at developing practical technology for non-invasive brain-computer interfaces (BCI) combined with other assistive technologies (AT) in the domains of communication, environmental control, entertainment and grasping (orthosis). An important concern of TOBI is the close integration of people with disabilities (end-users) and professional users (AT experts and caregivers) in the project. For assessment of the most urgent needs and requirements regarding new ATs, users from different European countries were investigated.

METHODS

TOBI Short Questionnaire:
Disabled end-users were asked
• to rate their satisfaction with current AT solutions in the domains of manipulation, communication, computer access, and environmental control on a four-point Likert scale (4= “very satisfied”, 1= “not at all satisfied”),
• to indicate whether they had or wanted independent access to devices for communication and entertainment.
Professional users and disabled end-users were asked
• to rate the importance of various aspects of AT on a four-point Likert scale (4= “very important”, 1= “not at all important”).

Participants: The investigated group of 77 disabled AT end-users was very heterogeneous in the etiology of their disability. Diagnosis dated back 15 years on average and participants showed a high degree of impairment (69% were almost or completely tetraplegics) that is, participants represented potential BCI-users. Professional users (n=48) showed a high degree of experience in their professions (years of experience: AT experts: M=9.92, SD±7.45; carers: M=12.50, SD±8.08).

RESULTS

Satisfaction in the different domains of AT was rather high. However, 30 % of those who used aids for manipulation, were dissatisfied with their current AT solutions. In the domain of computer access 23%, in the area of environmental control 17% and, for communications aids 16% were not satisfied.
The majority of the participants had independent access to different devices for communication and entertainment. However, participants who were impaired most severely (tetraplegia, with minimum possibilities to control AT) were in a worse situation. Depending on the device 16%-63% of these participants (n=19) had no independent access. For the less impaired participants (n=58) only 5%-16% did not have access.

Professional users and disabled end-users chose the four aspects of “functionality”, “ease of use”, “possibility of independent use”, and “adaptability to the specific situation” as being most important for new ATs. Both, end-users and AT experts rated “functionality” as the most important aspect, while caregivers rated “ease of use” as most important.

DISCUSSION AND CONCLUSION

There is the need for improved AT solutions in the domains where BCI can contribute with applications for manipulation, communication, entertainment and environmental control. The main conclusions for TOBI were to develop BCI applications which will be effective (functional/robust) and simple (ease of use); to train not only end-users in using the BCI-applications but also caregivers in supporting end-users with BCI; to provide AT solutions with which users will be as independent as possible from the support of others and which will be adaptable to the specific situation of the end-user.

SUPPORT
This work is supported by the European ICT Programme Project FP7-224631. This paper only reflects the authors' views and funding agencies are not liable for any use that may be made of the information contained herein.

KEYWORDS: BCI-applications, assistive technology, communication, entertainment, environmental control, orthosis

Abstract:

OBJECTIVES
Little is known about how the learning to operate a Brain Computer Interface (BCI) device would affect brain plasticity occurring during the learning process. The aim of this study was to investigate if and how a motor imagery (MI)-based BCI training would induce plastic changes at motor cortical level, as assessed by means of hr-EEG and Transcranial Magnetic Stimulation (TMS).
METHODS
61-channels-EEG and TMS data were collected from ten healthy volunteers who underwent TMS mapping sessions prior and after a motor imagery (MI)-based BCI training. For TMS Motor Evoked Potentials (MEPs) were recorded from Opponens Pollicis (OPP) and Extensor Digitorum Communis (EDC) muscles.
RESULTS
Offline EEG analysis revealed patterns correlating with successful BCI control localized over the scalp central areas, within a range of frequency typical for sensorimotor EEG rhythms (12-14Hz). TMS mapping showed that the OPP muscle representation estimated before and after training changed according to the type of MI tasks that subjects generated to achieve BCI control. A significant increase of the post-training OPP map parameters was found in subjects who adopted a first-person MI of grasping as control strategy with respect to subjects performing a first-person MI of fist-clenching. In the former group, analysis of the functional connections between the scalp EEG signals revealed a significant reduction of "global efficiency" index (i.e. lower degree of randomness) of the network in the high β range of frequency (20-29 Hz) throughout the BCI training sessions.
DISCUSSION
Our EEG and TMS findings indicate that MI-based BCI training induce changes in motor cortical system in terms of cortical excitability and functional networks.
SUPPORT
This work is supported by the European ICT Programme Project FP7-224631 (TOBI). This paper only reflects the authors' views and funding agencies are not liable for any use that may be made of the information contained herein.

Abstract:

OBJECTIVE
Modulation of EEG oscillations within alpha and beta frequency ranges occurs during execution and imagery of motor tasks; the same tasks induce changes in cortical excitability as tested by transcranial magnetic stimulation (TMS). The aim of this study is to investigate if and how EEG rhythm modulation correlate with changes in cortical excitability during hand motor tasks.
METHODS
14 subjects were instructed to execute (ME) or imagine (MI) a simple hand motor task; EEG data were acquired through a 64 channels EEG cap. TMS pulses were delivered through a figure-of-eight coil and Motor Evoked Potentials (MEPs) in contralateral <I>Opponens Pollicis</I> and Extensor Digitorum Communis were obtained. EEG power spectral density computed for each channel and frequency bin and and MEP amplitudes were correlated on a trial-by-trial basis.
RESULTS
The performance of ME and MI tasks induced a significant increase in MEP amplitude recorded from the OPP muscle concomitantly with a significant reduction in EEG power spectrum over the scalp sensorimotor areas. During ME, the increase in MEP amplitude significantly correlated with desynchronization in EEG power spectrum in 12 subjects. A similar tendency was observed in all subjects for MEP amplitude and EEG features during MI; this tendency reached significance in 3 subjects who were previously exposed to a MI-based Brain Computer Interface (BCI) training.
DISCUSSION
Our findings indicate that MI-induced effects on cortical excitability vary among subjects, and become robust in subjects previously trained to operate a MI-based BCI. This evidence suggests a promising role of BCI technology as a novel post-stroke &quot;rehabilitation intervention&quot; to practice MI.
SUPPORT
This work is supported by the European ICT Programme Project FP7-224631
(TOBI). This paper only reflects the authors' views and funding agencies are
not liable for any use that may be made of the information contained herein.

Abstract:

A major problem in high spinal cord injured patients (lesion above C4) is that they lose control over their grasp and elbow functions. With the help of neuroprosthetic systems, it is possible to restore simple hand and arm movements. It seems that for those cases, a non-invasive Brain-Computer Interface (BCI) based on EEG signals provides a good option to control such devices. After first attempts at using a BCI for hand control are already made [1, 2], the aim of the following study is to investigate whether the imagination of hand and elbow movements can be used for neuroprosthetic control. To start with, here we describe a first investigation of the separability of executed as well as imagined hand and elbow movements.

Abstract:

BACKGROUND AND OBJECTIVE

Advancing in BCI technology towards practical applications in technology based assistive solutions for people with disabilities requires coping with problems of accessibility and usability, in order to increase user acceptance and satisfaction.
Here, we propose an initial approach in the assessment of BCI technology development in terms of usability that is focused on the reduction of user’s mental workload in operating a mainstream software controlled via a P300-based BCI. In this study, we compare workload under two conditions: a) using separate screens/windows to display the control interface (i.e. P300 stimulator) and the application [1]; b) using a prototype with an overlaid interface.
This latter condition should require the user not to switch attention as in the case of two separate screens/windows, and thus would reduce her/his workload in mastering the BCI application.


METHODS
User interface. The prototype (described in [2]) is based on a brain transducer based on P300, implemented in the BCI2000 platform [3], and on the QualiWorld accessibility software (QualiLife SA, Paradiso, Switzerland). No dedicated BCI window is visible to the user [3] in condition (b).
Experimental procedure. In a preliminary testing phase, 2 subjects were challenged with 3 tasks (internet browsing; word processing configuration of the software), each requiring 7-9 selections with both overlaid and split interface. In the latter case, a second screen prompted the same icons available in the application interface, arranged in a matrix.
Behavioral assessment. Rating of mental workload was performed by means of the multidimensional NASA TLX questionnaire [4] that assesses the workload by considering six different factors: Mental, Physical and Temporal Demands, Frustration, Effort and Performance. These factors have a direct bearing on the usability of a software interface. If fewer mental resources are used, then the efficiency, effectiveness and satisfaction associated with the interface can be increased. The questionnaire was self-administered to both users at the end of each performed task to capture the potential differences in workload level relative to the 2 conditions.

RESULTS
Using the overlaid interface, the average time for each selection (14sec) was remarkably lower with respect to the split interface (23sec). The workload assessment indicated that the overlaid interface was associated with a lower overall mental workload (average score for overlaid interface=33.3, for split interface=74.7). The score of each single NASA TLX dimension was always lower for the overlaid interface except for the temporal demand, in both subjects.

DISCUSSION and CONCLUSION
The proposed prototype extends the concept of P300 selection from letters or menu items from an own interface to the more flexible concept of integrating with the external software interface. We expect this approach to improve the interface usability; the NASA TLX is a promising tool in the assessment of BCI technology within the framework of a user centred evaluation methodology.


References
[1] Mugler E., Benschc M., Halder S., Rosenstiel W., Bogdan M., Birbaumer N., Kübler A. (2008). Control of an Internet Browser Using the P300 Event- Related Potential, International Journal of Bioelectromagnetism, 10(1):56-63

[2] Cincotti F., Bianchi L., Aloise F., Schettini F., Riccio A., Babiloni F., Mattia D. BCI control of a mainstream communication software using overlaid P300 stimulation. Proceedings of the TOBI Workshop. Graz, 3-4 Feb. 2010.

[3] Schalk G., McFarland D.J., Hinterberger T., Birbaumer N., Wolpaw J.R. (2004). BCI2000: a general-purpose brain–computer interface (BCI) system. IEEE Trans Biomed Eng, 51(6):1034–43.

[4] NASA Human Performance Research Group (1987). Task Load Index (NASA-TLX). NASA Ames Research Centre: NASA Human Performance Research Group. http://humansystems.arc.nasa.gov/groups/TLX/


Support
This work is supported by the European ICT Programme Project FP7-224631
(TOBI). This paper only reflects the authors' views and funding agencies are
not liable for any use that may be made of the information contained here in.

Keyword
Mental Workload, BCI, Usability, NASA TLX, P300.

Abstract:

Steady-state evoked potentials, in general, have already been researched in larger scale and are well known. In this work vibro-tactile stimulation of five fingers of the right hand is used for generating steady-state somatosensory evoked potentials (SSSEP) with a self-made stimulation-device. The cortical responses are recorded during stimulation by three bipolar EEG-channels and analyzed afterwards by FFT- and bandpower-computations.
Nine subjects participated at the study and have been measured two times with a delay of at least two weeks to research a possible change in their neuronal responses on vibro-tactile stimulation over time and finger. Another issue was to investigate the emergence of a person independent so called tuning curve based on SSSEPs or a generally equal resonance frequency. Six subjects showed a finger-independent significant relative bandpower increase, the three others did not respond on the vibro-tactile stimulation in a significant way.
The emergence of a tuning curve, similar at all five fingers, could be shown, resonance-frequencies were person-dependent.

"This work is supported by the European ICT Programme Project FP7-224631. This paper only reflects the authors' views and funding agencies are not liable for any use that may be made of the information contained herein."

Abstract:

Background and Objective

Present-day non-invasive Brain Computer Interfaces (BCI) determines the intent of the user from a variety of different electrophysiological signals [1]. The P300 potentials recorded from the scalp are one example of signals that can be used to determine the subject’s intent [2]. Indeed, P300-based BCIs have the advantage of requiring no initial user training, since the P300 is a typical, or naive, response to the presentation of a desired choice.
Previous study indicated that a domotic environment can be operated by a P300-based BCI [3,4].

One of the main open issues relevant to this BCI application, concerns the low bit rate of command execution. Another interesting issue is the possibility for the user to disengage his attention from the interface without generates a wrong classification. In this study, we also addressed both issue by proposing asynchronous P300-Based BCI, we conducted experiments with four subject. The offline results show that the BCI is able to achieve an averaged information transfer rate of approximately 8 selection/min at a low false positive rate 5%.

Index terms- Brain Computer Interface, Domotic appliance, Assistive Technology, Asynchronous control

Materials and method

Four volunteers were challenged in this experimentation. Scalp EEG data (8 channel EEG system; g.USBAmp, gTec, Austria ; sampling rate 256Hz) were acquired from each subject during BCI sessions operated by a BCI2000 software [5], with P3Speller application. Data were stored for offline analysis. The data acquired is composed by 6 run. In each trial we ask to the subject to pay attention during the odd trial at stimulation, counting mentally the number of occurrences relative at particular character (Control task) suggest by the system and to ignore the stimulation during the even trial (No-Control task).

The data were processed in Matlab (MathWorks). The first step was extracting the control feature using the SWLDA, to discriminate two different classes, Target versus No-Target and No-Control. After we plotted a ROC curves for find the threshold to impose in the classification algorithms [6], the threshold is put out from an opportune choice between the True Positive Rate (TPR) and False Positive Rate (FPR), TPR > 0,5 and FPR < 0.05. The threshold value depend on the number of sequences accumulated. For improve the classification we impose another constrain, a hit is valid if the threshold is overtake two consecutive time, by the same stimulus.

Results

The results of this analysis show less than 4% of wrong classification and less than 15% of missed classification during control. During the no control task we obtain around 98% of abstentions. These preliminary findings shows that is possible to decrease the flash number for achieve the selection, and in the same time the threshold impose with this algorithms allow to the classifier to abstain when is a trial of no control.

Discussion and Conclusions

The next step will be implements this classification rule in a online signal processing, and testing it in a domotics contest. If the result in online utilization is confirmed we decrease the gap present in the BCI interface respect to the tradition input interface. And this will allow a direct comparison between it.

Abstract:

Background and Objective
The aim of this study is to provide some initial guidelines for relevant parameters selection to set up P300-based BCI systems. The ultimate goal will be to optimize system performances. Data were analyzed with 7 classifiers and 4 decimation factors (DFs); these latter were taken into account to consider the influence of the dimensionality of the training dataset. Classification results were compared in term of mean Expected Selection Cost (ESC) [1], that is the number of logical trials (flashes) needed to generate a correct symbol, taking into account the errors and abstentions and the strategy to recover them. The ESC of a system should be as low as possible to maximize the efficiency of the system.

Methods
Ten subjects participated to a 6x6 speller based P300 experiment (61 EEG electrodes), such as in [2-3]. Three artifact-free sessions (6 characters each) were used for classifier training and 3 for the classification.
Five electrodes and 600 ms duration time interval, after each stimulation, were selected. 10 flashes for each of the rows and columns of the matrix were used for the classification.
The 7 classifiers were: Stepwise, Fisher, Bayesian and Regularized Linear Discriminant Analysis (SWLDA, FLDA, BLDA, RLDA), Support Vector Machine with linear and RBF kernels (L-SVM and RBF-SVM) and Artificial Neural Networks (ANN). The selected DFs were 1 (no decimation), 2, 4, 8 and 16; decimation was preceded by a moving average filtering of the data.
All the classifiers were fed with the same dataset in order to attain a reliable comparison. The P3Classifier from the NPXLab suite (www.brainterface.com) was used for the analysis.

Results
Classifications results obtained from all subjects were averaged and showed that RLDA and BLDA achieved the lowest ESCs. In fact, when they are compared to SWLDA (ESC=1.48), the most used classifier for P300, they required respectively on average 23% (ESC=1.25) and 15% (ESC=1.33) less trials to generate a correct character. Non linear classifiers, RBF-SVM and ANN, are the most “selections-demanding”, while L-SVM and FLDA achieved ESCs in middle of the range.
DF considerably affected the performances of FLDA and ANN: with no decimation, the FLDA’s ESC did not converge (too many errors!) and for the ANN it was equal to 1.82. The increase of the DF yielded to an ESCs decrease. SVM-RBF’s ESC increased with the increasing of the DF.
BLDA and RLDA both had a minimum ESC when the DF was equal to 8.
Finally, in the best condition the SWLDA (DF=16) was characterized by an ESC=1.35 while RLDA (DF=8) had an ESC=1.19, thus indicating that RLDA provides for a 15% faster communication rate.

Discussion and Conclusions
Optimization of P300-based BCI systems performances requires different parameters to be considered. According to the present findings, obtained by comparing different classifiers and decimation factors in terms of number of selections needed to generate correct symbols, the combination that maximizes system efficiency is RLDA with DF=8. Other parameters, such as channels sets, filtering settings, time intervals and number of trials, are currently under investigation.


References
[1] Bianchi, L. et al., 2007. Performances evaluation and optimization of brain computer interface systems in a copy spelling task. IEEE T Neur Sys Reh, 15(2), 207-16.
[2] Krusienski, D.J. et al., 2006. A comparison of classification techniques for the P300 Speller. J Neural Eng, 3(4), 299-305.
[3] Krusienski, D.J. et al., 2008. Toward enhanced P300 speller performance. J Neurosci Meth, 167(1), 15-21.


Support
This work is partially supported by the European ICT Programme Project FP7-224631 and by the DCMC Project of the Italian Space Agency. This paper only reflects the authors’ views and funding agencies are not liable for any use that may be made of the information contained herein.

Keywords
BCI, P300, Optimization, P3Classifier.

Abstract:

Background and Objective
The aim of this study was to exploit BCI classifiers in order to define the topography (i.e. brain areas) and components of the event-related potentials generated in a typical P300 spelling application. In this widely used BCI application an alphabet of symbols, disposed on a squared matrix, is displayed on a PC screen and the subject is asked to gaze upon one of the symbols. Afterwards all the rows and columns of the matrix begin to flash in a random order.
Because evoked responses to target (flashing) and non-target (non flashing) stimuli are expected to have different spatial distribution, it could be deduced, by means of a classifier, which symbol the subjects are fixating. On the other side, it could also be assumed that a classifier performs better when the difference of the target and non-target responses is higher. By deduction, classification accuracy could represent an indirect measure of brain processing workload.

Methods
Six subjects participated to a 6x6 speller P300 experiment such as in [1]. Data were acquired by means of either an EEG system (61 sensors; 3 subjects) or a MEG system (274 sensors; 3 subjects). Inter Trial Interval (ITI) was set to 1000 ms to minimize the effect of responses overlapping. Subjects were asked to fixate one symbols communicated by an operator (15 flashes for each row and column, 36 symbols for the EEG and 18 for the MEG). Responses evoked during the fixation of six symbols randomly chosen were then to train the classifier (SWLDA) while the remaining were used for classification. This procedure was repeated in order to have 1080 classifications performed independently for each sensor and for 8 overlapping time intervals of 200 ms duration that started from trigger onset and were increased by 100 ms step. A total of 527040 and 2367360 classifications were performed for each of the EEG and MEG data respectively. The classification accuracy was computed for each of these configurations, leading to 61 (EEG) and 274 (MEG) values for each of the 8 time intervals. These values were mapped on 2-D topographic maps (single sensors classification maps).

Results
Both EEG and MEG maps consistently showed an occipital activation (accuracy greater than 50%) corresponding to the first time interval (0-200 ms) which presumably contained the N100 component; a similar EEG-MEG consistency was found for an activation located over the central areas which corresponded to the third and forth intervals containing the P300 component, recognizable from the EEG.

Discussion and Conclusions
The consistency between the topography and components of the waveform expected to be generated by the performance of this task, and the accuracy of the classifier indicates that classification accuracy can function as an indirect measure of the brain processing workload. Although this approach still requires to be improved (for instance, by adopting different strategies to select the time intervals to be analyzed), we suggests that a BCI paradigm can be of value beyond classical application of a control channel.


References
[1] Krusienski DJ, Sellers EW, McFarland DJ, Vaughan TM, Wolpaw JR. Toward enhanced P300 speller performance. J. Neurosci. Methods. 2008 Gen 15;167(1):15-21.


Support
This work is partially supported by the European ICT Programme Project FP7-224631 and by the DCMC Project of the Italian Space Agency. This paper only reflects the authors’ views and funding agencies are not liable for any use that may be made of the information contained herein.

Keywords
BCI, P300, Classification Maps

Abstract:

BACKGROUND AND OBJECTIVES
The main purpose of Electroencephalographic (EEG)-based Brain Computer Interface (BCI) technology is to provide for an alternative channel to support communication and control when motor pathways are interrupted [1]. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how the learning to operate a BCI device would affect brain plasticity which occurs during the learning process. Using Transcranial Magnetic Stimulation (TMS) we investigated if and how a sensorimotor rhythm-based BCI training would induce plastic changes at the motor cortical level. TMS constitutes a non-invasive objective measure of motor cortical excitability modulation occurring under different behavioral settings which involve the motor system [2].

METHODS
Scalp EEG and TMS data were collected from ten healthy volunteers who underwent TMS mapping sessions performed prior and after a motor imagery (MI)-based BCI training, consisting of 6-8 training sessions. EEG data were acquired by means of a 64 channel-cap, filtered (band-pass 0.1-70 Hz; sample frequency 200 sample/s) and stored for offline analysis. Data acquisition, on-line EEG processing and feedback to the subject were performed by using BCI2000 software system [3]. Focal single-pulse TMS was delivered to the scalp using a figure-of-eight coil, placed over the optimal position to elicit Motor Evoked Potentials (MEPs) in the contralateral Opponens Pollicis (OPP) and Extensor Digitorum Communis (EDC) muscles. TMS-derived maps were obtained by stimulating (suprathreshold intensity) multiple scalp sites marked on a 49-point grid mounted on an elastic cap. Volume of the TMS-derived maps (expressed as μV*cm2) was obtained by contrasting amplitude of MEPs recorded under MI and rest conditions, before and after BCI training.
RESULTS
All 10 subjects acquired BCI control with accuracy ranges from 53% to 96% in the first training session (R2 values from 0.04 to 0.6 respectively) and from 81% to 97% in the last session (R2 from 0.2 to 0.6). The spatial (location) and spectral analysis (frequency) of R2 values revealed EEG patterns correlating with successful control, localized over the scalp central areas, mainly bilaterally (n=7 subjects), within a range of frequency typical for sensorimotor EEG rhythms (SMR; 12-14 Hz). TMS mapping showed that the EDC and OPP muscle representation estimated before and after training were changed as function of the type of MI tasks that subjects generated to achieve BCI control. A significant increase of the post-training OPP muscle’s volume was found in those subjects who adopted a first-person MI of grasping as control strategy (n=5) with respect to subjects performing a first-person MI of fist-clenching (n=5). The EDC muscle representation did not show significant difference between pre- and post- training condition.
DISCUSSION
The main finding of the present study was that motor cortical excitability is dynamically modulated following MI-based BCI training. Since MI appears to be a promising intervention in motor rehabilitation after stroke [4], our finding corroborates the recent idea of exploiting BCI technology to restore motor function by “guiding” activity-dependent brain plasticity to improve post-stroke rehabilitation outcome.
Keywords: BCI, TMS, Motor Imagery

SUPPORT
This work is supported by the European ICT Programme Project FP7-224631 (TOBI). This paper only reflects the authors' views and funding agencies are not liable for any use that may be made of the information contained herein.

REFERENCES
[1] Daly JJ, Wolpaw JR. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 2008 Nov;7(11):1032-43.
[2] Mark Hallett. Transcranial Magnetic Stimulation: A Primer. Neuron. 2007 Jul 19;55(2):187-99.
[3] Cincotti et al. High-resolution EEG techniques for brain-computer interface applications. J Neurosci Methods, J Neurosci Methods. 2008 Jan 15;167(1):31-42.
[4] Sharma N, Simmons LH, Jones PS, Day DJ, Carpenter TA, Pomeroy VM, Warburton EA, Baron JC. Motor imagery after subcortical stroke: a functional magnetic resonance imaging study. Stroke. 2009 Apr; 40(4):1315-24.

Abstract:

BACKGROUND AND OBJECTIVE
The practice of motor imagery (MI) has been suggested to improve motor recovery after stroke, by inducing plastic changes in the lesioned hemisphere [1]. The Transcranial Magnetic Stimulation (TMS) technique has become a valuable tool to map motor cortex excitability during MI [2]. A modulation of the EEG oscillatory activity within the alpha and beta ranges of frequency (i.e sensorimotor EEG rhythms) occurs during voluntary execution as well as imagination of simple motor tasks[3]. This phenomenon has been exploited to operate some types of EEG-based brain computer interfaces (BCI). In the perspective of developing a BCI-based rehabilitation tool grounded on EEG monitoring of MI, we investigated by means of TMS, if and how the EEG sensorimotor rhythms modulation functionally correlates with the changes in motor cortical excitability during covert hand motor tasks.
METHODS
The scalp EEG and TMS data were collected from 13 healthy volunteers (mean age 25±10 years) who were verbally instructed to either execute (ME) or image (MI) a simple motor task with their non-dominant hand. EEG data were acquired through a 64 channels EEG cap, filtered (band-pass 0.1-70 Hz; sample frequency 200 sample/s) and stored for offline analysis. Single TMS pulses were delivered (at 120% of motor threshold) through a figure-of-eight coil over the right hemisphere in the optimal position to elicit Motor Evoked Potentials (MEPs) in the contralateral Opponens Pollicis (OPP) and Extensor Digitorum Communis (EDC) muscle. EEG epochs (1 sec duration) preceding each TMS shock were analyzed; power spectral density for each channel and each frequency bin (2 Hz resolution) was computed. EEG spectral features and MEP amplitudes were aligned and time series were correlated on a trial by trial basis. The ME and MI tasks were contrasted with baseline condition (Rest).
RESULTS
The performance of ME and MI task induced, in all subject, a significant increase in MEP amplitude recorded from the OPP muscle (ME: p=.00003; MI: p=.05) concomitantly with a significant EEG desynchronization (frequency range 12-26 Hz; ME: r2 values from 0.15 to 0.5; MI: r2 values from 0.1 to 0.5) localized over the scalp sensorimotor areas. During ME, the increase in MEP amplitude significantly correlated with the decrease in the EEG power spectrum (p<.01), in 12 out of 14 subjects. A similar tendency to contra-vary was observed, in all subjects, for the MEP amplitude and EEG features under MI condition; this tendency reached significance (p<.03) only in those subject who were previously exposed to a MI-based BCI training.
DISCUSSION AND CONCLUSIONS
The present EEG and TMS findings mainly indicate that MI facilitation effect on motor cortical excitability is variable among subjects, but it becomes very robust in subjects previously trained to perform this cognitive motor task to operate a BCI system. Thus, we suggest that BCI technology and TMS can be successfully adopted to tailor the development of a novel post-stroke “rehabilitation intervention” based on the practice of MI.
KEYWORDS: motor imagery, TMS, sensorimotor BCI.
SUPPORT
This work is supported by the European ICT Programme Project FP7-224631
(TOBI). This paper only reflects the authors' views and funding agencies are
not liable for any use that may be made of the information contained herein.
REFERENCES
[1] Langhorne et al. Motor recovery after stroke: a systematic review. Lancet Neurol, 2009.
[2] Fadiga et al. Corticospinal excitability is specifically modulated by motor imagery: a magnetic stimulation study. Neuropsychologia 1999.
[3] Pfurtscheller G and Lopes da Silva, FH, Event-related EEG/MEG synchronization and desynchronization: basic principle. Clin Neurophysiol, 1999.

Abstract:

In the last decade, the number of groups involved in BCI research has increased tremendously. They have often tackled similar problems in different ways, generating a multitude of BCI systems that are incompatible with each other, even if they solve the same classes of problems.

Abstract:

Late ALS stages result in unreliable eye control and increased secondary vision problems. In order to provide a remedy for these patients, a new spatial auditory BCI design was recently introduced [1] which is based on ERP components. As ERP effects are known to be susceptible to changes of the experimental design, this offline study explores the influence of the variables loudness, stimulus duration and inter stimulus onset (ISO) to optimize them under real-world conditions for an online study with patients.

Abstract:

Although the P300 spelling grid has been modestly successful even for patients, the visual flashes that are needed are not accessible for those with vision deterioration. For these potential users, tactile or auditory stimulation are the modalities of choice, but their implementations are mostly binary in nature. In [1,2] we presented the offline results for a new auditory multi-class paradigm to overcome some of these difficulties. Here we give the initial online results from an ongoing trial with healthy subjects.

Abstract:

INTRODUCTION

People with impaired motor abilities suffer from significant deterioration of their quality of life due to malfunction of the conventional human communication and control channels. BCI technology provides a promising option for recovering basic communication capabilities and operating devices that allow users to gain and maintain control of their environment. One of the middle-term goals of TOBI (Tools for Brain-Computer Interaction) Project is to integrate BCI control into currently existing, popular among the disabled community AT systems and perform preclinical validation on healthy users and users with different levels of motor impairments.

METHODS

The AT software selected for our BCI experimentation is QualiWORLD (QualiLife). QualiWORLD replaces standard mouse and keyboard by a variety of computer access solutions. A two-command Mental Imagery (MI) based BCI system has been integrated with QualiWORLD based on a novel API allowing bidirectional communication between the two environments. Two prototypes (text editor, web browser) have been tested with healthy subjects. In the text-entry prototype the BCI has been used to control the selection of the scanned element in a virtual keyboard and the inversion of the scanning order, while QualiWORLD sequentially highlights the keyboard elements hierarchically (by group, row and item). In the web browser prototype , the task was to scan through and select the links of a web-page and reveal a hidden keyword in the loaded documents by zooming in/out, scrolling up/down.

RESULTS

A novel subject tested the text entry prototype achieving a performance of 33 seconds/character in writing 5 words, compared to 23 seconds/character using manual control (key press) in the same context (virtual keyboard with scanning mouse). In a second experiment with the web browser prototype, two naïve subjects were able to complete the tasks in 30-96 seconds, reporting 11 seconds/command and only 3 erroneous decisions in a total of 16 trials. These results are to be compared with a performance of 17-31 seconds and 4.7 seconds/command for manual control.

DISCUSSION

The results demonstrate the suitability of BCI for controlling AT software. Ongoing work focuses on coupling BCI with more complex applications, improving human-computer interaction and adapting AT software to the needs of BCI (e.g. embedding neurofeedback) towards eventually establishing BCI as a key player in the AT market.

This work is supported by the European ICT Programme Project FP7-224631. This paper only reflects the authors' views and funding agencies are not liable for any use that may be made of the information contained herein.

Abstract:

INTRODUCTION
In the present electroencephalographic (EEG) study different types of hand movements, active,
passive and imagery have been compared within a group of right and left handers. The passive
hand movement was realized by functional electrical stimulation (FES) of the lower arm. For all
3 conditions left and right hand movements were performed. Significant EEG changes were
analyzed by calculating time­frequency maps of event­related (de­)synchronization (ERD/ERS)
for 32 EEG channels recorded from sensorimotor and premotor areas. Several EEG [1] and
fMRI [2] studies reported the impact of handedness on motor cortical areas during simple hand
movements. The results of the study might contribute to the development of handedness specific
training paradigms for stroke patients, leading to faster rehabilitation success.
METHODS
Seven right handed and seven left handed healthy participants (six women and eight men, 27,2 ±
3,4 years old) performed three movement conditions with their right and left hand: (i) voluntary
active hand grasping movement (ii) imagery of the same hand movement (iii) Functional
electrical stimulation (FES) producing a similar grasping movement. EEG was recorded from 32
positions placed over prominent motor areas, and additionally 2 EMG (electromyogram)
channels were applied as a control during the motor imagery task.
RESULTS
Immediately after the beginning of the FES movement, a prominent ERD was found, followed
by a beta ERS similar to that observed after active movements. Stronger ERD was observed for
passive movements compared to movement imagery for the dominant hand.
DISCUSSION
Based on the results we suggested that future rehabilitation programs of stroke patients could be
individually developed under consideration of the handedness of the patients which might fasten
the rehabilitation progress.

Abstract:

INTRODUCTION
Brain computer interfaces (BCI) can serve as communication tools for people with severe impairment in speech and motor function due to neurodegenerative disease. Reasons for large inter-individual differences in people’s ability to use a BCI are not yet understood and predictors for BCI performance would be advantageous for the selection of users and EEG parameters. For BCI-based communication by spelling, paradigms making use of the P300 evoked potential are widely used. Success in a P300 based BCI requires the ability to cognitively modify the focus of attention and to sustain attention. Such attentional control has been closely linked to peripheral physiological parameters, such as the heart rate variability (HRV). The present study investigated the association between resting HRV and performance in the P300 BCI.

METHODS
Electrocardiogram (ECG) of 29 healthy participants was recorded and a P300 based BCI spelling task performed. HRV resting baseline was determined for five minutes before and after BCI performance. The BCI task consisted of a calibration session and afterwards a monitored trial during which participants were required to spell 12 words of five letters each (12 blocks). The blocks were interspersed with 30 second breaks (recovery times). Time
and frequency domain of the resting HRV was analysed.

RESULTS
Autonomic balance (LF/HF) and other HRV measures were associated with BCI-performance such that subjects with higher HRV performed better. Autonomic balance explained 23,1% of the variance in BCI-performance. This association (r=-.53, n=18, p<.05) is illustrated in Figure 1.

DISCUSSION
How can we identify whether people could use a BCI, and which BCI might be best for each user? This abstract shows that HRV, which is easy to measure, might predict successful performance with (at least) the P300 BCI system used here, across an admirable number of subjects. These results contribute to a better understanding of interindividual differences in BCI performance in healthy individuals and potentially also in clinical samples.

Abstract:

INTRODUCTION
This study aimed at testing to what degree of motor impairment and of altered states of consciousness severely handicapped patients could be provided with a P300 based BCI.

METHODS
Twelve patients with severe motor impairments or altered states of consciousness were included in the study: eleven patients were in the minimally conscious state (MCS) and one patient in the locked in state (LIS). The state of consciousness and impairment was evaluated using the Coma Recovery Scale Revised form (CRS-R). As a baseline for the proof of principle 16 age-matched healthy subjects were included. An auditory P300 four choice speller paradigm was used (Sellers and Donchin, Clinical Neurophysiology 2006; 117: 538–548). After a training session of 4 sequences (67 seconds of auditory presentation of words), patients and healthy subjects were required to answer 10 or 12 questions, respectively. A stepwise linear discriminant analysis based on the training session was used to classify the data and to provide online feedback. Data were also analyzed offline for correct responses and amplitude variance (r2).

RESULTS
Healthy subjects presented a P300 signal and significant communication with the P300 speller (offline analysis correct responses >90%). Likewise, the LIS patient had a P300 and significant communication (73% correct responses). MCS patients presented with a P300, but the response was fluctuating and communication was not above chance level (21 – 43% correctness).

DISCUSSION
A P300-based BCI is a communication aid for people in the locked-in state. For patients with fluctuating levels of attention such as in MCS, the P300 BCI needs to be more robust and sensitive for EEG changes that indicate states of unconsciousness. In the future, BCI may be developed to serve as a tool to distinguish between different clinical states of consciousness.

Abstract:

INTRODUCTION
The P300 event-related potential is used as a control signal for Brain Computer Interface (BCI) systems [2]. The generation of P300 brain after salient or attended potential can vary according to the type of stimulation utilized to evoke it [3]. This is of relevance in the development of BCI technology whose usability must accommodate different user’s needs (e.g. less invasive hardware) and physical conditions (e.g. visual impairment). In this study, different modalities of stimulation to induce P300 were explored within the context of a P300-based BCI system; the impact on the on-line classification performance and the possibility to use the same feature for different stimulation were also investigated.

METHODS
Eight subjects with no previous experience of BCIs, participated to the study. Scalp EEG data (61 channel EEG system; Brain Products, Germany) were acquired from each subject during BCI sessions operated by a BCI2000 software [4]. Data were stored for offline analysis. Each participant underwent 5 sessions (4 run each; 30 min duration) which included one type of stimulation at time with the following order: visual, auditory and tactile stimuli.

RESULTS
The P300 brain potentials displayed significant variation in latency and amplitude of the principal P300 component elicited with the 3 modalities of stimulation. Online classification performance reached the highest level in all subjects (93 % in average) with respect to the auditory and tactile stimulation modalities which yielded to a correct classification percentages of 70% and 68% respectively.

DISCUSSION
These preliminary findings suggest that multimodal stimulation can be of considered in a P300-based BCI application. The finding of P300 characteristic modification as function of the stimulus modality indicates that it is required to tune control feature according to the stimulus modality for a successful P300-based BCI applications.

Abstract:

INTRODUCTION
Today's Brain-Computer Interface (BCI) systems have been primarily developed to assist in replacing the abilities lost by patients diagnosed with motor-neuron diseases such as amyotrophic lateral sclerosis (ALS). Of which, the most important ability appears to be communication [1], represented by the volume of research into such applications currently under development worldwide. Another valuable element of human life however is that of creative expression. Modification to a P300-BCI communication system has yielded an application which provides the ability for such expression, Brain Painting.

METHODS
Brain Painting [2] works by replacing individual fields in a P300-BCI based control matrix with painting functions, such as cursor control and shape/colour selection to produce images of an abstract nature (see Fig. 1). Brain Painting is now used regularly by several ALS patients (n=4) throughout Germany as a form of entertainment and as a way to satisfy the desire for creative articulation in their own homes. Furthermore, prominent German artists (n=6) have been invited to use Brain Painting at their ateliers. Constructive feedback has been collected from these various users in an effort to improve the application. Important metrics being assessed are ensuring the application is intuitive to use, robust in reliability and practical for unsupervised use in daily life.

RESULTS
Quantitative results initial suggest information transfer rate variations amongst subjects, healthy participants display prominent P300 responses thus requiring 20% less repetitions in comparison with patients. Qualitatively are the results outstandingly positive, enthusiastic comments from both patients and artists confirm that importantly they experience satisfaction and are entertained when using the application, with a repeated strong desire to re-use the system. To date, patients using the system have produced numerous images from independent sittings lasting upwards of 1.5 - 2 hours. This is unquestionable evidence for an BCI solution which is providing a positive and useful difference to the lives of ALS patients.

DISCUSSION
Brain Painting satisfies some basic human needs and it is hoped that the possibilities offered assist in improving the Quality of Life for patients with severe motor disability. Modifications to the application and stimulus presentation are being initiated to improve the reliability and prominence of the P300 response amongst patients where life sustaining apparatus presents a challenge to the mounting or operation of BCI devices. Thus acceptance of Brain Painting within the artist community and most importantly the patient community is a key factor of the project to ensure its long term success.

REFERENCES
[1] Kübler A. et al. Brain-Computer Communication: Unlocking the Locked In. In Psychol. Bull 2001, pp.358-375
[2] Kübler A. et al. A. Brain Painting - BCI Meets Art. In 4th International BCI Workshop and Training Course, Graz, 2008

Abstract:

INTRODUCTION
BCI-controlled software applications are usually either embedded in the BCI system (e.g. a simple speller) or they are specifically developed with BCI control in mind. A third option is that mainstream software (e.g. a web browser or a commercial assistive software) with no or little customization is operated by a BCI. Applications like Mozilla Firefox were previously controlled by a P300 based BCI [1], using separate screens (or windows) for the control interface (i.e. the P300 stimulation) and the application itself. In this study, we propose a system in which the BCI stimulation overlays the application, thus requiring no switch of the user’s attention between two separate screens/windows.

METHODS
The prototype is based on a brain transducer implemented in the BCI2000 platform, and on the QualiWorld accessibility software (QualiLife SA, Paradiso, Switzerland). No dedicated BCI window is visible to the user. Brief visual stimuli (120 ms color reversal, 80 ms ISI) are randomly overlaid onto each active control (e.g. buttons, whose number ranged between 4 and 42 depending on the context) of the application’s main window.
In a preliminary testing phase, 2 users were challenged with tasks requiring 7-9 selections using either the proposed overlaid interface or a split interface. In the latter case, a second screen prompted the same icons available in the application interface, arranged in a matrix. Users were interviewed at the end of each task, and filled an evaluation questionnaire for the assessment of workload (NASA TLX).

RESULTS and DISCUSSION
Using the overlaid interface, the average time for each selection was 14 s, significantly lower than the split interface, presumably as an effect of a reduced need of attentional resources. ERP patterns of the two modalities show marked differences, apparently due to the different stimulation scheme. As expected, the workload assessed by the questionnaire was lower for the overlaid interface.
The proposed prototype extends the concept of P300 selection from letters or menu items to the more general target of buttons on the interface of a computer program. To achieve this, no new functionalities were incorporated into the BCI software, but rather we tried as much as possible to separate the ”BCI transducer” from the target computer application. This separation promotes the use of a BCI as a more general device for Human-Computer Interaction.

Abstract:

INTRODUCTION
People with amyotrophic lateral sclerosis (ALS) in the course of their disease lose their motor functions and thus, their ability to speak. Brain-Computer Interfaces (BCIs) provide an alternative communication channel because they rely on brain signals and are therefore, muscle independent. However, individuals differ in their ability to use a BCI. To elucidate how psychological variables such as motivation influence BCI performance in patients with ALS, this study investigated the relation between motivation and performance in a P300 based BCI. Furthermore, we were interested whether motivation would affect the P300 amplitude.

METHODS
Motivation was manipulated with a 20 Euro gift certificate for an internet store. In a first run 15 ALS patients spelled a 14 character sentence without receiving a reward. In the second run they were promised a gift certificate for achieving a better performance compared to the first run. Motivation was assessed with a BCI-adapted questionnaire (FAM-BCI2000) and a visual analogue scale (VAS). BCI performance was defined as the overall percentage of correctly selected characters (correct response rate=CRR).

RESULTS
We found a high negative correlation between the number of sequences needed to spell 70% correctly and the motivation (measured with VAS, r=-.78, p<.05). CRR comparison before and after motivation manipulation strengthened this result (Z= -1.86, p= .06).

DISCUSSION
The results indicate that motivation may explain some of the variance in BCI performance in patients with severe disability and should be monitored in BCI settings. If these results can be confirmed in further studies motivation could become an integral part of BCI training protocols.

Abstract:

INTRODUCTION
The application workpackage 2 “motor substitution” of the European project TOBI is aiming at the restoration of the hand grasp and elbow movements in spinal cord injured individuals and in stroke survivors. Today the only possibility of significantly improving a restricted or lost grasp function is the application of Functional Electrical Stimulation (FES). However, current FES methods are only applicable if shoulder and elbow functions are preserved to a large extent. Secondly, due to the non-physiologic synchronous activation of the muscles through external electrical pulses, muscles fatigue rapidly. This is a major limitation for the use of FES systems not only for the restoration of the grasp but also of the elbow or shoulder function.
Moreover, in high lesioned tetraplegic subjects only a few motor functions are preserved that can be used for control of assistive devices. In those cases, a Brain-Computer Interface (BCI) can serve as an additional control channel.

FEATURES OF THE ORTHOSIS
The limitations of the current FES systems may be overcome by a combination of FES and a mechanical orthosis to maintain a stable posture without causing fatigue. Therefore a first prototype of a modular, adjustable elbow orthosis has been developed. Its main components consist of an in flexion direction self-locking, electrically delockable elbow joint and double sided self-adhesive FES electrodes in combination with a multichannel electrical stimulation device (“Motionstim”). Its dedicated firmware allows for autonomous use and shared control. The FES-hybrid orthosis offers mainly three degrees of control (analog control of the degree of hand opening / closing, selection between predefined grasp patterns, analog control of the elbow angle).
To fully support the functionality of the hardware of the orthosis, several software tools for its central processing unit “Motionstim” have been developed. These include an application program to control the orthosis’ actuators and the implementation of a standardized serial communication protocol as a basis for integration of the whole system into the hybrid-BCI concept.
In the simplest scenario, several input signals – e.g. from a shoulder position sensor, a myoelectric sensor or a BCI – are connected independently to the input channels of the “Motionstim” for autonomous control. In a more sophisticated control concept, these signals are fused by the hybrid-BCI, which generates high-level control commands and sends them to the “Motionstim” device via a serial interface.

CONCLUSION
A novel FES-hybrid orthosis has been developed for restoration of reaching and grasping function in high-level lesioned spinal cord injured subjects with limited finger, hand and elbow function. Its core component consists of a lockable / delockable elbow joint that prevents excessive muscle fatigue apparent in traditional neuroprostheses. Two prototypes of the FES-hybrid orthosis are now ready for combination with the hybrid-BCI approach to perform first tests with patients.

Abstract:

INTRODUCTION

The EU-project “Tools for Brain-Computer Interaction” (TOBI) aims at developing practical technology for non-invasive brain-computer interfaces combined with other assistive technologies (AT). An important concern of TOBI is the close integration of people with disabilities (end-users) and professional users (AT experts and carers) in the project from the very beginning. For assessment of the most urgent requirements regarding new ATs users from different European countries were investigated.

METHODS

TOBI Short Questionnaire: Participants were asked to rate the importance of various aspects of AT on a four-point Likert scale from (4) “very important” to (1) “not at all important”. Professional users were additionally asked to rank the three most important aspects.
Participants: The investigated group of 77 AT end-users was very heterogeneous in diagnosis. Diagnosis dated back on average 15 years and participants showed a high degree of impairment that is, participants represented potential BCI-users. Professional users (n=48) showed a high degree of experience in their professions (years of experience: AT experts: M=9.92, SD±7.45; carers: M=12.50, SD±8.08).

RESULTS

All three groups of participants chose the following four aspects as most important for new ATs: “functionality”, “ease of use”, “possibility of independent use” and “adaptability to the specific situation”. Both, end-users and AT experts rated “functionality” as the most important aspect, while carers rated “ease of use” as most important. These findings were supported by the results of the ranking of the three most important aspects: 50% of the AT experts rated “functionality” in first place while 59% of the carers rated “ease of use” in first place.

CONCLUSION

The main conclusions for TOBI were to develop BCI applications which will be effective (functional/ robust) and simple (ease of use); to train not only end-users in using the BCI-applications but also caregivers in supporting end-users with BCI; to provide AT solutions with which users will be as independent as possible from the support of others and which will be adaptable to the specific situation of the end-user.

Abstract:

INTRODUCTION

Recent attention has been given to the use of Brain Computer Interface (BCI) technology to restore motor function by “guiding” activity-dependent brain plasticity to improve rehabilitation outcome after brain disorders such as stroke [1]. In this prospective, we investigate if and how a BCI training based on motor imagery (MI) paradigm would induce plastic changes at the motor cortical level by means of Transcranial Magnetic Stimulation (TMS) technique; this latter provides for a non-invasive objective measure of motor cortical excitability modulation occurring under different behavioral motor settings [2].

METHODS

Scalp EEG and TMS data were collected from ten healthy volunteers (mean age 30±7 years) who underwent TMS mapping sessions performed prior and after a MI–based BCI training consisting of 6-8 training sessions for each subject. Single TMS pulses were delivered through a figure-of-eight coil in the optimal position to elicit Motor Evoked Potentials (MEPs) in the contralateral Opponens Pollicis (OPP) and Extensor Digitorum Communis (EDC) muscles. TMS map volume (expressed as uV*cm2) were obtained by contrasting MEP amplitudes obtained during MI and rest conditions.

RESULTS

The spatial and spectral analysis revealed homogeneous EEG reactivity patterns of successful control (>80%), localized over the scalp central areas, within a range of frequency typical for sensorimotor EEG rhythms (SMR; 12-14 Hz). TMS functional mapping showed that the ECD and OPP excitability areas estimated before and after BCI training varied significantly according to the type of MI tasks that subjects generated to achieve BCI application control. Indeed, only those subjects who achieve control by performing MI of hand grasping displayed a significant increase in the map volume relative to the OPP muscle, after training.

DISCUSSION

The main finding of the present TMS study is that motor cortical excitability can be dynamically modulated by MI performed and actually practiced via a BCI training. Moreover, this modulation maintains a muscular pattern specificity. These neurophysiological evidences corroborate the idea of exploiting BCI technology to train and practice those MI tasks that can have a facilitating effect on the excitability of the hand motor area.

References
[1] Daly JJ, Wolpaw JR. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 2008 Nov;7(11):1032-43. Epub 2008 Oct 2.
[2] Mark Hallett. Transcranial Magnetic Stimulation: A Primer. Neuron. 2007 Jul 19;55(2):187-99.

Abstract:

INTRODUCTION

Motor imagery (MI) has been suggested to improve motor recovery after stroke by inducing plastic changes in the lesioned hemisphere [1]. Transcranial Magnetic Stimulation (TMS) is a valuable tool to map motor cortex excitability during MI [2]. Modulation of EEG activity within α and β frequency ranges occurs during execution as well as imagery of simple motor tasks [3]. In the perspective of developing a BCI-based rehabilitation tool grounded on EEG monitoring of MI, we investigated by means of TMS if and how EEG rhythms functionally correlate with changes in motor cortical excitability during hand motor tasks.

METHODS

EEG and TMS data were collected from 14 subjects who were instructed to either execute (ME) or imagine (MI) a simple motor task with their non-dominant hand. EEG data were acquired through a 64 channels EEG cap, filtered and stored for offline analysis. Single TMS pulses were delivered through a figure-of-eight coil over the non-dominant hemisphere and Motor Evoked Potentials (MEPs) in contralateral Opponens Pollicis (OPP) and Extensor Digitorum Communis (EDC) were obtained. EEG power spectral density for each channel and frequency bin was computed. EEG spectral features and MEP amplitudes were aligned and time series were correlated on a trial by trial basis.

RESULTS

During ME, the increase in MEP amplitude significantly correlated with desynchronization in EEG power spectrum, in 12 out of 14 subjects. A similar tendency to counter-vary was observed in all subjects for MEP amplitude and EEG features under MI condition; this tendency reached significance only in subjects who were previously exposed to a MI-based BCI training.

DISCUSSION

Our findings indicate that MI-induced effects on motor cortical excitability are variable among subjects, but become robust in subjects previously trained to operate a BCI system. We suggest that BCI technology and TMS can be successfully adopted to tailor the development of a novel post-stroke “rehabilitation intervention” based on the practice of MI.

References
[1] Langhorne et al. Motor recovery after stroke: a systematic review. Lancet Neurol 2009.
[2] Fadiga et al. Corticospinal excitability is specifically modulated by motor imagery: a magnetic stimulation study. Neuropsychologia 1999.
[3] Pfurtscheller et al. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 1999.

Abstract:

In order to optimize the performances of P300-based BCI systems, a study on the best number of flashes to elicit an evoked potential suitable for BCI, has been conducted. The performances of such systems have been evaluated in terms of Expected Selection Cost (ESC) [1], which measures the number of logical trials (flashes) needed to generate a correct symbol, also taking into account the errors and abstentions and the strategy to recover them, and the time needed for that (T). The Efficiency of a BCI system is inversely proportional to , so, minimizing this product provides a method for selecting the best number of flashes for each subject.

METHODS
Ten subjects participated to a 6x6 speller based P300 experiment (61 EEG electrodes). In six different sessions, subjects were asked to focus on six of the symbols of the matrix. Data were band-pass filtered (0.5-30Hz) and divided into three sets: two sessions were used for training the classifier, two for the optimization and tuning (finding the best number of flashes), and the last two for the validation. Two classifiers were compared, Stepwise and Bayesian LDA. Classifications were performed with a progressive number of flashes (from 1 to 15) of the rows/columns of the matrix and the accuracy and the indicators computed at each step.

RESULTS
While the accuracy of the system increases with the number of flashes, the Efficiency does not increases accordingly; this because errors and abstentions have different distributions and must be recovered by adding new selections for deleting the error or/and reselecting the correct symbol; this means further flashes and additional time. In general, for almost all the subjects and for both the classifiers, from the optimization phase it resulted that it is preferable to classify with less than 15 flashes, admitting a loss of accuracy but maximizing system efficiency. The optimal number of flashes, found in the optimization phase as the number of flashes that minimizes the , was verified in the validation phase.

DISCUSSION
The optimal number of flashes cannot be derived from accuracy, because errors, abstentions and strategies to recover them are not taken into account; so, in order to maximize P300-based systems efficiency, it can be computed by means of the indicator and verified in successive experimental sessions, giving a realistic view on system performances and allowing to implement new strategies for optimizing them.

[1] L. Bianchi, L.R. Quitadamo, G. Garreffa, G.C. Cardarilli, and M.G. Marciani, “Performances evaluation and optimization of brain computer interface systems in a copy spelling task” IEEE Trans Neural Sys Rehabil Eng. 15(2), Jun. 2007, pp. 207-16.

Abstract:

The transition of BCI systems from laboratories to users’ homes makes it necessary to have highly versatile and easy to use tools that can be integrated with the systems and practiced without the assistance of technical staff.
The P3Classifier module is a component from the NPXLab suite (www.brainterface.com) that is responsible for the offline analysis of the brain signals and their classification. It acts as an offline Transducer and allows the optimization of several aspects concerning the classification of event-related potentials. It is intuitive, it can be set up by everyone and can be integrated with different systems, as it supports different file formats (BCI2000, GDF, EDF, Brain Vision Analyzer, etc.).

METHODS
There are some main actions which can be performed in the P3Classifier to train and test the classifiers. The most important ones are listed below.
• Preprocessing: data can be temporal or spatially filtered (e.g. ICA).
• Channels selection: different subsets of sensors, among those acquired, can be used for the classification in order to find those that are highly responsive and guarantee high performances for the whole BCI system.
• Segmentation: different time intervals of the evoked responses can be selected (e.g. in P300 protocols 600ms after the stimulations). Data can be also decimated and trials can be discarded depending on special events (e.g. Artefacts).
• Classifiers: different classifiers, that can be simultaneously tested on the same set of data, can be selected. SWLDA, BLDA, FLDA, RLDA, SVM, ANN, kNN and all their possible configurations are available: for example, different p-in and p-out values for SWLDA can be selected; linear, polynomial or radial basis kernels can be chosen for SVM; different activations functions, number of layers and number of neurons in a layer for neural networks can be set, etc....
• Validation Mode: it is possible to select a n-folds automatic, random cross-validation or a manual validation of data; to select the number of flashes to perform classifications; to classify by using different combinations of the selected channels or temporal sliding windows, and so on. Obviously the results of the classifications can be stored into external files for further processing (e.g. 2D and 3D topographic maps representation, performance optimization, etc…).

DISCUSSION
The friendly and intuitive user interface of the P3Classifier allows testing a virtually unlimited number of different BCI configurations with just few mouse clicks. In this way it is possible to tune a BCI system to every single user with very little effort and in a very convenient way. The next release of the P3Classifier will support the TOBI interfaces and will also be evaluated in terms of results and usability by the side of patients.

Abstract:

Because the cost of setting up and using a Brain Computer Interface (BCI) is high, it is important to develop tools and techniques to aid development of such systems as far as possible before user testing. Simulation techniques can be used to optimize user interfaces and predict performance for Assistive Technologies (ATs) [1]. This poster describes a simulator that models classifier output for an EEG-based 2-class motor imagery BCI, discussing its potential applications. BCI researchers were able to adjust parameters to realize a good subjective feel of a game of Brain Pong using the simulator input, both for expert and novice subjects.

Abstract:

Late ALS stages result in unreliable eye control and increased secondary vision problems. In order to provide a remedy for these patients, a new spatial auditory BCI design was introduced in Schreuder et al., BioEMag 2009, which is based on ERP components of the EEG. As ERP effects are known to be susceptible for changes of the experimental design, this study explores the influence of the variables loudness, stimulus duration and inter stimulus onset (ISO) in this spatial auditory design.

Abstract:

Although the P300 spelling grid has been modestly successful even for patients, the visual flashes that are needed are not accessible for those with vision deterioration. For these potential users, tactile or auditory stimulation are the modalities of choice, but they are mostly binary in nature. In [1,2] we presented the offline results for a new auditory multi-class paradigm to overcome some of these difficulties. This paradigm is now ready to be used online and was successfully operated by two subjects to write short words.

Abstract:

In the context of brain-actuated device control [1], the role of feedback is crucial for informing the
user on his mental performance, so that his/her mental strategy can be continuously adjusted during
real-world interaction with a device. Consequently, the feedback is functionally binded with the
device control paradigm, introducing parameters that need to be configured to accommodate the
particular user preferences, experience in brain control, fatigue etc. This work is aimed at building
a feedback controller able to automatically and continuously reconfigure the system online.

Abstract:

If we want to use Brain-Computer Interfaces (BCI) as feedback training for motor rehabilitation after stroke a fast and easy acquisition of a reliable classifier is indispensible. In order to find new strategies for setting up a classifier to correctly detect activation patterns of motor imagery two approaches, active movement and passive movement, were examined and tested in a sample of 20 healthy elderly participants. For this purpose we recorded data from passive movement (hands were moved by a handrobot), active movement (opening and closing of hands) and motor imagery. In this paper we set up a classifier of data from passive movement and a classifier of data from active movement and use these classifiers to detect motor imagery. Subsequently the classification accuracies of the two classifiers are compared to check which strategy is more successful in detecting motor imagery.
"This work is supported by the European ICT Program Project FP7-224631. This paper only reflects the authors' views and funding agencies are not liable for any use that may be made of the information contained herein."

Abstract:

Assistive and rehabilitative devices ought to continuously adapt to changes such as different users, sensor characteristics, user expectations, or user motor patterns due to learning or ageing; moreover, system performance inherently relates to the user's perception of the system behaviour. Thus, the user should be guiding the adaptation process. This should be automatic, transparent, and unconscious.
We define an Hybrid-BCI system (hBCI) detecting Error Potentials (ErrPs) on single trial basis [1]. ErrPs are emitted when a human observes an unexpected behaviour in a system: we propose and evaluate the detection of such signals as a “teacher” for the on-line adaptation of a user centered classifier.

Abstract:

For spinal cord injury and brain stroke patients the ability of performing grasps is
important for their reintegration and independent daily life. Functional Electrical
Stimulation (FES) of residual muscles can achieve the most dominant grasping tasks [1].
However, due to the very complex hand anatomy and current limitations in FEStechnology
with surface electrodes, these functions are not smoothly executed yet. The
middle finger responds strongest to the stimulation, leads the movement and generates
higher local forces compared to the other fingers. The idea is to support and synchronize
evenly the grasping movement of all fingers with a passive hand orthosis in order to
increase the acceptance and help in real world applications.

Abstract:

The TOBI project focuses on the development of practical brain-computer interfaces (BCIs) for disabled people. Sometimes these people have residual activity of their muscles, most likely in the morning when they are not exhausted. One of the goals of the project is to therefore use, switch and fuse muscle and brain activity (or even additional channels) in the so called “Hybrid-BCI” approach, depending on their availability and reliability (www.tobi-project.org). In this first experiment we are combing electromyographic (EMG) with electroencephalographic (EEG) activity.

Abstract:

This abstract reports on a study about time coded motor imagery (MI) to control an artificial arm. One of the goals was to allow a fast setup without needing a lot of training. Hence, a simple strategy had to be applied. It was decided to use motor imagery of the right hand, since the artificial arm was placed on the right side of the subject. The second objective was to check for error potentials (ErrPs). Therefore, a possibility is demonstrated how a discrete feedback can be provided on top of a continuous one, since ErrPs are time- and phase-locked signals.

Abstract:

Hybrid BCI (hBCI), meaning a combination of more BCIs and/or control signals, is a fairly unexplored topic of research. However, it promises more convenient and
reliable means of communication for severely disabled persons. The definition for the TOBI hBCI is a combination of different signals including at least one BCI channel.
Thus, it could be a combination of two BCI channels but also a combination of BCI and assistive devices, for example. One very important goal of the hBCI approach is
to develop a general, modular platform, capable to deal with several different requirements, to allow a maximum of distribution. For this reason, implementing a
uniform data acquisition and distribution system is an important part.

One of the main strengths of the hBCI will be flexibility, whereby different laboratories will be able to exchange their own programs or modules with other users.

Here we describe details of the Data Acquisition Module for the TOBI hBCI concerning its structure and interface for data transmission.

Abstract:

A major problem in high SCI patients (lesion above C4) is that they lose control over their grasp and elbow functions. It seems that for those cases, a non-invasive BCI based on EEG signals provides a good option to control such devices. After first attempts for using a BCI for hand control are already made [1], the aim of the following study is to investigate whether the imagination of hand and elbow movements can be used for neuroprosthetic control. To start with, here we describe a first investigation of the separability of executed hand and elbow movements.

Abstract:

INTRODUCTION
This poster demonstrates a first approach towards a hybrid brain-computer interface (hBCI)
which combines BCI channels with inputs from other sensors (environmental or manually
controlled). The advantage of this hBCI is that one does not have to rely on BCI solely, but can
use it as an additional control strategy which does not cause fatigue of any muscles. A study
was conducted that applied a simple brain switch to trigger an elementary task like elbow
flexion/extension and a joystick for controlling a precise movement (grasping).

METHODS
Three participants attended the experiment. Before conducting online measurements, a
classifier for a brain switch was realized by detecting the beta rebound [1, 2] after imagination
of brisk foot motor imagery (MI) over channel Cz during two offline cue-based runs. Here, 40
trials foot MI versus 40 trials rest were recorded. For the online experiment, the subjects were
asked to grasp objects of varying sizes by the means of a joystick and then to move the arm
up/down via brain switch-induced commands. These movements were carried out by an
artificial limb. During 8 runs, the participants had to complete a certain sequence: 60s of rest
were followed by a natural lifting movement (open/close gripper move arm up/down and
open/close gripper again), another break of 30s and another lifting movement followed by a
final resting period of 60s. The long breaks were necessary to check for false positives (FPs).

RESULTS
For evaluation, three measures are given: the percentage of true positives (TPs) and FPs
depending on the context and the rate of FPs during the breaks. On average, the subjects
achieved TP = 75.6 %, FP = 24.4 %, and 0.43 FP/min.

DISCUSSION
To conclude, this poster will show a feasible combination of a BCI channel with another
control option. This first, simple approach can also be seen as one basic example on which
more advanced hBCI models can be built on later.

References
[1] G.R. Müller-Putz, D. Zimmermann, B. Graimann, K. Nestinger, G. Korisek, and G. Pfurtscheller.
Event-related beta EEG-changes during passive and attempted foot movements in paraplegic patients.
Brain Research, 1137:84–91, 2007.
[2] G. Pfurtscheller and T. Solis-Escalante. Could the beta rebound in the EEG be suitable to realize a
'brain switch'? Clinical Neurophysiology, 120, 24–29, 2009.


This work is supported by the European ICT Programme Project FP7-224631. This paper only reflects
the authors' views and funding agencies are not liable for any use that may be made of the
information contained herein.

Abstract:

P300 Brainpainting: Evaluation of a novel BCI application with ALS patients and healthy controls

J Münßinger1, S Halder1, S Kleih1, A Furdea1, V Raco1, A Hösle2, A Kübler1,3
1 Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, Tübingen, Germany
2 Babenhausen, Germany
3 Department of Psychology I, Biological Psychology, Clinical Psychology, and Psychotherapy , University of Würzburg, Würzburg, Germany

To date, brain-computer interfaces (BCIs) are primarily used to enable (completely) paralyzed patients to communicate. However, these applications do not allow them to communicate in a creative manner; which many amyotrophic lateral sclerosis (ALS) patients would consider an increase of their quality of life. The current P300-Brainpainting application is intended to enable the patients to express themselves creatively by means of painting pictures only using their brain activity.

Abstract:

Validation of SMR BCI Performance Categorization Using fMRI

S Halder 1,4, D Agorastos 1, R Veit 1, B Blankertz 2,5, T Dickhaus 2, E Hammer 1, S Kleih 1, S Lee 1, C Sannelli 2, B Varkuti 1, A Kübler 1,3
1 Institute of Medical Psychology and Behavioural Neurobiology, University of Tübingen, 72074Tübingen, Germany
2 Machine Learning Laboratory, Berlin Institute of Technology, Franklinstr. 28/29 10587 Berlin, Germany
3 Department of Psychology I, University of Würzburg, Marcusstr. 9-11, 97072 Würzburg, Germany
4 Wilhelm-Schickard Institute for Computer Engineering, University of Tübingen, Sand 13, 72076 Tübingen, Germany
5 Fraunhofer FIRST (IDA), Kekuléstr. 7, 12489 Berlin, Germany.

Brain-Computer Interfaces (BCIs) enable paralyzed people to communicate with their environment. Differences in performance between users and sessions remain largely unexplained, as does the question as to why communication in the complete locked-in-state (CLIS) has not been possible. A reliable performance indicator would allow an analysis of subject-to-subject and session-to-session performance differences and serve as and indicator of the capacity to use a BCI during the progression of the disease. BCIs based on modulation of the sensory motor rhythms (SMR) require several training sessions or at least a 30 minute calibration period, depending on the design of the system. Additionally the preparation of a high-density electroencephalography (EEG) cap takes a comparable amount of time. Therefore, indicators allowing us a fast screening of healthy participants or the selection of suitable training programs are particularly important.

Abstract:

Introduction
The EU-project “Tools for Brain-Computer Interaction” (TOBI) aims at developing practical technology for brain-computer interaction, i.e., non-invasive brain-computer interfaces (BCI) combined with other assistive technologies (AT) that will improve the quality of life of disabled people. An important concern of TOBI is the integration and participation of people with disabilities from the very beginning of the project.
The aim of the study was to define firstly, the needs (a person’s wants and necessities with respect to different aspects of independence) and secondly, the requirements (instrumental needs demanding specific functions/characteristics from the product or solution) of potential users with regard to assistive applications based on BCI.

Material
The TOBI members developed a questionnaire to assess i) satisfaction with current AT solutions in the areas of manipulation, mobility, communication, computer access, and environmental adaptation/control, ii) the three most important areas where participants wanted to improve their independence, iii) whether participants had independent access and used AT for access to devices for communication and entertainment as well as their desire to gain access, iv) the overall satisfaction with the current AT solutions. Variables were rated on a ten-point Likert scale (1=“not at all satisfied”, 10=“absolutely satisfied”). The importance of various aspects of AT was assessed on a four-point Likert scale from “very important” (4) to “not at all important” (1).

Participants
Participants (N = 77, 23 female) were from Austria (12%), Italy (41%), and Germany (47%) and diagnosed with spinal cord injury (37%), neurological/neuromuscular diseases (47%), or cerebrovascular disorders (16%). The majority of the participants (69%) was almost or completely tetraplegic.

Results
Overall satisfaction (M=7.12) and satisfaction in the different areas of independence was high. However, 16% (communication) to 30% (manipulation) were dissatisfied with their current solutions. Lowest dissatisfaction ratings were found for mobility aids (8%).
The majority of the participants had independent access to different devices for communication and entertainment. However, depending on the device 10% to 22% would have liked to have access to e-media and even more would have liked to use AT for access to the different devices. Participants who were impaired most severely (tetraplegic and only one channel to control AT´s) were in a worse situation. Sixteen to 63% of these participants had no independent access and wished to use AT to get access to the different devices.
“Mobility” (52%) was the aspect of life in which the majority of the participants wanted to improve their independence followed by “activities of daily living” (46%) and “occupation/ employment” (33%). Participants who used communication aids had needs, which were partially different from those of the rest of the participants. They wanted to improve their independence in personal expression (32%) and social interaction (24%).
Considering the adoption of a new AT solution, participants rated “functionality” (M=3.74) as the most important aspect followed by “possibility of independent use” (M=3.67) and “easiness of use” (M=3.60).

Conclusions
There is the need for better or/and alternative AT solutions in the areas where BCI can contribute with applications for manipulation, communication, and environmental control/entertainment.
The main lesson for TOBI is: (1) To develop simple (easiness of use) and effective (functional/ robust) BCI applications. (2) If communication aids are needed, to provide devices which enable people to communicate their thoughts and wishes and support their interaction with significant others. (3) To provide AT solutions with which users are as independent as possible from external support.

Abstract:

1 Introduction
A non-invasive brain-computer interface (BCI) is a system that translates user's intent, coded by spatiotemporal neural activity (usually EEG), into a control signal without using activity of any muscles or peripheral nerves. The user's involvement in current BCI systems is highly demanding in terms of cognitive attention and effort, since he/she needs to continuously generate mental commands for the brain-actuated device. In contrast, we discuss a new brain interaction approach where the user only monitors the performance of a semi-autonomous system. In this approach, the system carries out its task automatically (e.g., a wheelchair navigating autonomously) and occasionally receives corrective signals derived from the user's EEG whenever the operator wishes to deliver key decisions or improve the system's performance. In particular, we are interested in exploring the use of event-related potentials related to error detection (ErrP) and slow cortical potentials related to the anticipation of future events (i.e., contingent negative variation, CNV). These signals can replace or complement asynchronous control commands issued by traditional BCI [Gallan et al., 2008, for example]. The representation of this approach is illustrated in Figure 1.

2 Cognitive Monitoring
Several studies have shown error related EEG patterns elicited by subject-generated errors , erroneous feedback or errors generated during Brain-machine interaction. We propose to use these signals to provide a more natural mean of interaction for BCIs. Under this framework ErrPs are used to confirm or reject decisions taken by an artificial autonomous system.


To test this hypothesis we recorded EEG signals while subject monitors 1-D movement of a cursor towards a target . Figure 2(a) shows the ERP difference of signals elicited by erroneous and correct cursor movements. We have shown that it is possible to recognize these potentials in single trial. Moreover, system performance can possibly improved based on the recognition of these signals at least in two ways; on the one hand erroneous decisions can be corrected [Ferrez and Millan, 2008], on the other hand, the autonomous system can be updated so as to decrease the likelihood of actions that elicit error potentials, e.g. using ErrPs as negative reinforcers [Chavarriaga et al., 2007].
We have also explored the use of other cognitive signals for interaction. In particular, we have studied EEG signals related to the anticipation of future events, namely the Contingent-negative variation.
Gangadhar et al, [Gangadhar et al., 2009] have shown recognition of relevant/irrelevant situation based on Go-NoGo CNV paradigms both on-line and off-line analysis (c.f. Fig 2(b)). This signal may thus be used by the user to trigger specific behaviors in an asynchronous manner (e.g. in the scenario of wheelchair control to perform docking in front of a desk as opposed to avoid it).

3 Extended multimodal feedback
Efficient Brain-machine interaction requires the system to provide the user with information about its internal states. For instance, a moving robot may inform what action it is about to perform asking the user to confirm or reject such action via ErrPs. To this end, we have studied the use of different feedback modalities (i.e. visual, auditory and tactile) in realistic navigation tasks using virtual reality environments. This study shows that it is feasible to use ErrPs elicited by different feedback modalities in Brain Machine interactive systems [Perrin et al., 2008]. Moreover, simulated experiments in ErrP-based Human-robot interaction have shown that current ErrP classification performance is enough to successfully perform navigational tasks (c.f. Figure 2(c)). Experiments using real robots and online recognition of error potentials are currently undergoing.

Abstract:

People with impaired motor abilities suffer from significant deterioration of their quality of life due to the numerous physical and psychological barriers imposed by their situation on the conventional human communication and control channels. Despite common thinking, physically impaired people strive to partially restore or replace their communication and control pathways, in order to escape isolation and marginalization, and reach a satisfactory level of independence, social integration and eventually, an improved quality of life. Brain-Computer Interface (BCI) technology provides a promising additional option for recovering basic communication capabilities and operating devices that allow users to gain and maintain control of their environment.
TOBI (Tools for Brain-Computer Interaction) is an European project that aims to develop practical BCI Assistive Technology (AT), namely, to build non-invasive BCI prototypes based on electroencephalographic (EEG) signals, thus providing disabled users with a novel means of interaction with the outside world. Communication and control enhancement is specifically addressed within the TOBI framework, targeting a measurable middle-term impact in terms of preclinical validation on users with different levels of motor impairments.
State of the art BCI systems demonstrate the potential of using BCI as an additional communication and control channel. An increasing number of BCI applications has been reported (e.g., virtual keyboards, web browsers, brain-actuated mobile robots, neuroprostheses), yet, current BCI prototypes suffer
certain limitations. Interaction aspects of relevant applications, such as “ease of use” and “user friendliness” have been neglected, as research has been mainly focused on the machine learning and signal processing challenges of BCI technology. As a result, interested parties (clinics or patients) cannot benefit without constant expert guidance. TOBI aims at alleviating such limitations, by creating standalone BCI applications along with novel user training protocols, thus bringing BCI technology out of the lab and into the real world. The main short-term goal of TOBI is the integration of BCI control into currently existing, popular among the disabled community AT systems, that have proven to be useful. Embedding the additional BCI communication option is believed to augment the range of applications that users can currently access with AT products.
The AT software selected for our BCI experimentation is QualiWORLD (QualiLife SA, Lugano, Switzerland). QualiWORLD is a comprehensive platform allowing disabled users to gain access to many applications (text editor, web browser, email, etc.), while replacing standard mouse and keyboard by a variety of computer access solutions (Auto-Scan Mode, mouse alternatives, gesture recognition). It can be personalized to the specific user disabilities and preferences. QualiWORLD helps disabled persons to achieve greater independence by allowing them to communicate, have more control over their living environment, and participate in state of the art rehabilitation and educational programs. Furthermore, a considerable amount of users is already familiar with it, as it is particularly popular in the AT community.
A Mental Imagery (MI) based BCI system has been interfaced with QualiWORLD as a first demo of BCI integration in commercial AT software. Preliminary results in coupling BCI with QualiWORLD prove the feasibility of using BCI as an additional communication and control pathway for disabled people. Users have
been able to edit a text and browse a photo album using only one BCI command, along with the QualiWORLD Virtual Keyboard and Auto-Scan mouse mode. More specifically, after a short period of MI training, the user is able to select the application’s contents (virtual keys, images) while the QualiWORLD Auto-Scan sequentially highlights them, by performing the predefined MI. Thereby, the user’s EEG brain activity is classified by the BCI and the resulting class probabilities are reduced into a single binary (yes/no) command (key-press). This binary command is translated by the QualiWORLD into a selection event. Problematic is the fact that the BCI subject is only receiving a discrete feedback via the QualiWORLD and not a continuous one, as usually in BCI research. So,
the user is unaware of the development of his/her brain patterns over time, and how close he/she is to the decision border at a given time. This issue will be addressed in the near future, so that the QualiWORLD can provide visual feedback to the user concerning the confidence of the decision making process (probability distribution visualization). The goal is, on one hand, to demonstrate the suitability of BCI for controling state of the art AT products and, on the other hand, to provide cues on how these products can adapt to the BCI interaction channel, leading to more efficient user interfaces.
Future work will address the coupling of more complex applications with the BCI, the introduction of multiple BCI commands, and the evaluation of the potential of this novel interaction techniques by disabled users. Special attention will be paid to increase the robustness of the system in order to achieve satisfactory user experience and to establish BCI as a key player in the AT market. Towards more robust BCI, TOBI will take advantage of novel Human-Computer Interaction principles, take into account the inherent properties of EEG/BCI signals (low Signal-to-Noise-Ratio, non-stationarity, low output bitrate, lagged
dynamics) and address mental state and performance monitoring, as well as on-line adaptation issues. Finally, novel interaction objects for various tasks will be benchmarked, aiming a multimodal approach.

This work is supported by the European ICT Programme Project FP7-224631. This paper only reflects the authors’ views and funding agencies are not liable for any use that may be made of the information contained herein.

Abstract:

THE P300- BRAIN-COMPUTER INTERFACE BROWSER: A MUSCLE-INDEPENDENT SURFING TOOL FOR PARALYZED PEOPLE

Carolin Ruf1, Emily Mugler1, Sebastian Halder1, Michael Bensch2 & Andrea Kübler3

1 Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen,
2 Wilhelm-Schickard-Institute for Computer Science, University of Tübingen
3 Department of Psychology I, Biological Psychology, Clinical Psychology and Psychotherapy, University of Würzburg


EEG-based-Brain-Computer Interfaces (BCIs) can be used by paralyzed people for communication. To increase the general usefulness of BCI systems applications for particular activities are needed. The presented study evaluated the efficacy of a BCI application for surfing the web. A matrix paradigm based on the event-related potential P300 was used to control the BCI web browser. Ten healthy subjects and three paralyzed patients diagnosed with amyotrophic lateral sclerosis (ALS) performed web surfing tasks in several sessions. All participants were asked to evaluate the BCI browser after use. The healthy subjects achieved an average accuracy of 90 % and an information transfer rate (ITR) of 16.5 bits/minute when controlling the web browser. The ALS patients used the browser with an average accuracy of 72% and an ITR of 7 bits/minute. The patients indicated that they would use the BCI browser in everyday life and would participate in more BCI web browser sessions. The results confirmed a decreased ITR in people with neurological disease as compared to healthy controls. The lower P300 amplitude and a longer latency in the ALS patients may account for this difference. This aspect has to be taken into account when designing BCI protocols for patients. Nevertheless, accuracy in patients was still high enough to control the browser reliably.

Abstract:

Brain-Computer Interfaces (BCI) provide muscle-independent communication for paralyzed patients. Individuals differ in their ability to use a BCI. This study examines the relation between motivation and the P300 amplitude within a BCI controlled by event-related potentials (ERP). In two experimental groups participants received 25 or 50 Cent for each correct letter selection; the control group was not rewarded. Motivation was assessed with a BCI adapted questionnaire and a visual analogue scale. BCI performance was defined as the percentage of correctly selected characters (group mean = 99%). At Cz the P300 amplitude was positively correlated to self-rated motivation (r=.50). Offline analysis revealed that highly motivated participants would have needed fewer trials for a discriminable ERP and thus, would have been able to communicate faster with the ERP-BCI. These results indicate that motivation may contribute to variance in BCI performance and has to be monitored in BCI settings.

Abstract:

People with amyotrophic lateral sclerosis (ALS) lose their motor activity and so their ability to talk in the course of their disease. Brain-Computer Interfaces (BCIs) provide an alternative communication channel because they rely on brain signals and are thus muscle independent. However, individuals differ in their ability to use a BCI. To investigate the relevance of psychological influencing variables such as motivation in patients with ALS this study examined the relation between motivation and the ability to learn using a BCI and the P300 amplitude measured within a BCI controlled by event-related potentials (ERP). Motivation was manipulated with a 20 Euro gift certificate for an internet store. In the first run twelve ALS patients spelled a 14 character sentence without receiving a reward. In the second run they were promised a gift certificate for trying particularly hard to spell the sentence correctly. Motivation was assessed with a BCI-adapted questionnaire and a visual analogue scale. BCI performance was defined as the overall percentage of correctly selected characters (correct response rate=CRR). Three patients were not able to finish the session and were excluded from analysis. Average CRR across all runs and patients was 93%; four patients had a CRR of 100%. The gift certificate did not affect motivation but BCI performance. We found a trend for CRR being higher after motivation (96%) than before motivation (89%, Z=-1.84, p=.07). The results indicate that motivation may explain some of the variance in BCI performance and should be monitored in BCI settings.

Abstract:

P300 BCI Performance Prediction Using an Auditory Standard Oddball

Halder, S. a
Hammer, E.-M. a
Kleih, S. C. a
Kübler, A. b,a

a Institute for Medical Psychology and Behavioural Neurobiology, University of Tübingen, Tübingen, Germany
b Department of Psychology I, Biological Psychology, Clinical Psychology and Psychotherapy, University of Würzburg, Würzburg, Germany

Brain-Computer Interfaces (BCIs) enable paralyzed people to communicate with their environment. Differences in performance between users and sessions remain largely unexplained, as does the question as to why communication in the complete locked-in-state (CLIS) has not been possible. A reliable performance indicator would allow an analysis of subject-to-subject and session-to-session performance differences and serve as and indicator of the capacity to use a BCI during the progression of a disease.
A study with 40 healthy participants was conducted to determine the viability of performance indicators. All participants performed a single 20 symbol visual (VP300) and auditory P300 (AP300) BCI session. Additionally, an auditory oddball was recorded from each subject. Online feedback performance (<100% VP300, <70% AP300) was used to separate groups of good and bad performers (BP).
Average performance of 94.5% (12 subjects in BP group) using the VP300 and 63% (16 subjects in BP group) using AP300 were achieved. Mean P300 amplitude in the auditory oddball was significantly higher in good as compared to bad performers (Wilcoxon rank test, p < 0.05). Using the amplitudes of two samples at 395 ms on CPz and CP1 correlations (Pearson) with performance of r=0.57 were found. This result shows the viability of the auditory standard oddball in to predict individual BCI performance and suggests that the long term tracking of the P300 elicited by the auditory oddball will lead to a better understanding of BCI performance degradation in the CLIS.


Acknowledgements

Funded by DFG KU 1453/3-1. This work is supported by the European ICT Program Project FP7-224631. This paper only reflects the authors’ views and funding agencies are not liable for any use that may be made of the information contained herein.

Abstract:

P300 BCI's robustness as a communication device needs to be tested against the concurrent psychological states of users. We investigated the influence of emotional state and subjective depressed mood of 40 healthy participants on their letter spelling performance. Emotional state was induced by presenting images from the International Affective Picture System (IAPS). Depressed mood was assessed using subjects’ self-rating of the Centre for Epidemiologic Study Depression scale (CES-D). The study employed a within-subject design and each subject undertook three letter-spelling sessions associated to pleasant, unpleasant, and neutral valence. P300 event-related potential (ERP) amplitude is compared between conditions to provide a psychophysiological index to performance. No difference was observed between the affective conditions in terms of performance. Furthermore, there was no difference in P300 ERP amplitudes across subjects between the emotional conditions. Given the past findings of P300 amplitude suppression due to emotional experience, it is likely that our emotional manipulation was not sufficiently strong for this study although it successfully induced emotional states in the normative direction. More importantly however, this study demonstrated a significant correlation between the self-rated depressed mood and BCI performance. More depressed mood is related to poorer overall performance. We can conclude that depressed mood hinders the performance of P300 BCI. More sophisticated emotional induction is needed to investigate the influence of emotional states on BCI performance.

Abstract:

Introduction
Brain-computer interfacing (BCI) aims at providing paralyzed patients with a communication device that obviates the need of using the usual motor pathways. A large number of BCI systems is based on motor imagery for encoding the user's intention. Motor imagery typically leads to event-related desynchronization (ERD) of the 10Hz mu-rhythm in the motor cortex associated to the respective limb. This EEG phenomenon can be used for feedback control for most subjects by a classifier that was individually trained on the subject's EEG [1]. We introduce the goalkeeper paradigm that aims at improving online BCI performance by subject training under time pressure conditions.

Methods
Multi-channel EEG of 8 BCI-experienced subjects was acquired while they were playing 3 runs (100 trials each) of a BCI-controlled computer game that imitated the task of a goalkeeper during a penalty kick. During a trial, a ball was moving from the top of the screen towards one of its bottom corners. Using two different types of motor imagery (chosen from left hand, right hand and foot) the subjects had to control the horizontal movements of a bar at the bottom of the screen in order to catch the ball. The speed of the ball increased linearly from trial to trial and over the 3 runs. Subjects had to catch the ball within 2500ms (at the beginning of run 1) to 1250ms (at the end of run 3). Late arrival in a correct corner or arrival in a wrong corner were interpreted as misses.
In order to achieve a constant goalkeeping performance, the subjects were thus required to generate faster and/or stronger ERD responses in the later runs to steer the bar quickly into the correct corner. In an offline analysis, the goalkeeping performance, the reaction times (defined as the time needed to reach the correct corner) and EEG features were analyzed in relation to the block design of the experiment.

Results
The goalkeeper paradigm effectively increased time pressure over the 3 runs. Performance was measured in terms of balls caught within the first 1250ms. 7 out of 8 subjects managed to respond with increased performance from run 1 to 3 (avg. of 33.8 balls caught in run 1 to 41.6 in run 3, see Fig. 1).
A close analysis of time-frequency EEG features between successful trials of run 1 and 3 revealed different strategies of the subjects, e.g. earlier ERD or stronger ERD in the alpha band under time pressure. As a side effect, the training introduced for some subjects an additional ERD in the beta band (which had not been used for feedback). Earlier re-synchronization (ERS) could be observed for some subjects in run 3, where trials were shorter.

Acknowledgements
This work is supported by a BMBF grant No. 01GQ0850, a DFG grant MU 987/3-1, and by the European ICT Programme Project FP7-224631. This abstract only reflects the authors' views. Funding agencies are not liable for any use that may be made of the information contained herein.

References
[1] Müller KR, Tangermann M, Dornhege G, Krauledat M, Curio G, Blankertz B. Machine learning for real-time single-trial eeg-analysis: from brain-computer interfacing to mental state monitoring. J Neurosci Methods, 167(1):82–90, 2008.

Abstract:

The Effect of Emotions on P300 Brain-Computer Interface (BCI) Performance

S. Lukito1, S. Halder2, P. Bretherton3, C. Vögele3, A. Kübler1,2
1University of Würzburg, Germany
2University of Tübingen, Germany
3Roehampton University, London, UK


The rapid development of BCI technology and research has not been matched by research into its clinical applicability. It is important to know for instance whether BCI performance is affected by its users’ cognitive and emotional state. This question is all the more important because potential users of BCI in the clinical setting are patients in the locked-in-state, among whom an increased incidence of emotional problem and depression is reported.

This study investigated the effect of subjective feeling of depression, as assessed by a self-rated questionnaire, and emotional priming on the oddball BCI letter-spelling performance of forty healthy participants. In conjunction to observing the letter selection performance, the study also investigates whether there is a difference between P300 event-related potential (ERP) parameters across the affective valence conditions. Participants completed three blocks of letter-spelling task corresponding to three emotional valence categories (positive, neutral, and negative). During each block, participants were instructed to spell the word BRAINPOWER whilst being exposed to five images, taken from the International Affective Picture System (IAPS), prior to each letter selection. Participants were also requested to imagine recent or past emotional experience relevant to the valence condition of each block throughout the task. We found significant negative correlation between the subjective feeling of depression and BCI performance. However, evidence of the effect of emotional priming on performance is lacking, even though participants’ rating of valence and arousal of the pictures were at normative values. From these results, we cautiously conclude that negative mood indeed hampers BCI performance. To investigate further the influence of emotion on BCI performance, more sophisticated approaches to manipulation of emotion will be used in further studies.

Abstract:

Implementation of SMR Based Brain Painting

S. Halder1, A. Furdea1, R. Leeb2, G. Müller-Putz2, A. Hösle3, A. Kübler1,4
1 Universitätsklinikum Tübingen, Institute of Medical Psychology and Behavioral Neu-
robiology, Gartenstr. 29, 72074 Tübingen, Germany
2 Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37 8010 Graz, Austria
3 87727 Babenhausen, Germany
4 Universität Würzburg, Lehrstuhl für Psychologie I, Marcusstr. 9-11, 97070 Würzburg, Germany

Current brain-computer interface (BCI) systems are mostly used for communication with late-stage motoneuron disease patients. These systems offer only restricted possibilities to their users to express themselves creatively. Nonetheless, many patients consider artistic activity to be a valuable aspect of their lives.
We previously extended our P300 BCI that is being used by amyotrophic lateral sclerosis (ALS) patients for communication, to enable the use of a painting application. This was achieved by mapping the individual fields of the control matrix to painting functions. These can be used for e.g., cursor control and placing various figures on the virtual canvas used for painting. When using a P300 BCI though, the user is restricted to a predefined step intervals when moving a cursor or changing the size of objects on the canvas. This limitation was overcome by designing a new painting application that is controllable with a sensorimotor rhythm (SMR) BCI based on the detection of event-related desynchronization and synchronization (ERD/S) of those rhythms.
In this design command icons are placed in six hexagons that are arranged in a circle (modeled after the hex-o-spell interface [1]). The BCI is used to control an arrow which extends from the center of these hexagons to select the intended command or to rotate the arrow further to the next hexagon. Several commands transfer control from the menu to the canvas itself so that the BCI can be used to e.g., freely move the cursor to the desired position. Other commands which allow user adjustment are changing object size, object transparency and zooming. Returning control to the menu is achieved by using a non-control class that is recognized by the system when the user imagines neither of the two control classes.


[1] Klaus-Robert Müller, Michael Tangermann, Guido Dornhege, Matthias Krauledat, Gabriel Curio, and Benjamin Blankertz. Machine learning for real-time single-trial eeg-analysis: from brain-computer interfacing to mental state monitoring. J Neurosci Methods, 167(1):82–90, 2008 Jan 15.

Abstract:

In this paper we show how four healthy subjects operate, with high accuracy and speed, a
non-invasive asynchronous BCI for controlling a FES neuroprosthesis. In our experiment subjects
were asked to carry on a handwriting task. The novelty of our approach relies on the natural
interaction paradigm used to control the prosthesis. In fact subjects deliver congruent commands by
imagining a movement of the same hand they control through FES. Interestingly, the very low
number of errors illustrates how during the experiments subjects were able to deliver commands just
when they intended to do so.