Adrien Combaz

Comparison of classification methods for P300 brain-computer interface on disabled subjects

We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI) on a group of amyotrophic lateral sclerosis (ALS), middle cerebral artery (MCA) stroke, and subarachnoid hemorrhage (SAH) patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patients disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects.

Towards the detection of error-related potentials and its integration in the context of a P300 speller brain-computer interface

A P300 Speller is a brain-computer interface (BCI) that enables subjects to spell text on a computer screen by detecting P300 Event-Related Potentials in their electroencephalograms (EEG). This BCI application is of particular interest to disabled patients who have lost all means of verbal and motor communication. Error-related Potentials (ErrPs) in the EEG are generated by the subjects perception of an error. We report on the possibility of using these ErrPs for improving the performance of a P300 Speller. Overall nine subjects were tested, allowing us to study their EEG responses to correct and incorrect performances of the BCI, compare our findings to previous studies, explore the possibility of detecting ErrPs and discuss the integration of ErrP classifiers into the P300 Speller system.

Steady-State Visual Evoked Potential-Based Computer Gaming on a Consumer-Grade EEG Device

In this paper, we introduce a game in which the player navigates an avatar through a maze by using a brain-computer interface (BCI) that analyzes the steady-state visual evoked potential (SSVEP) responses recorded with electroencephalography (EEG) on the players scalp. The four-command control game, called The Maze, was specifically designed around an SSVEP BCI and validated in several EEG setups when using a traditional electrode cap with relocatable electrodes and a consumer-grade headset with fixed electrodes (Emotiv EPOC). We experimentally derive the parameter values that provide an acceptable tradeoff between accuracy of game control and interactivity, and evaluate the control provided by the BCI during gameplay. As a final step in the validation of the game, a population study on a broad audience was conducted with the EPOC headset in a real-world setting. The study revealed that the majority (85%) of the players enjoyed the game in spite of its intricate control (mean accuracy 80.37%, mean mission time ratio 0.90). We also discuss what to take into account while designing BCI-based games.

A comparison of two spelling Brain-Computer Interfaces based on visual P3 and SSVEP in Locked-In Syndrome.

Objectives We study the applicability of a visual P3-based and a Steady State Visually Evoked Potentials (SSVEP)-based Brain-Computer Interfaces (BCIs) for mental text spelling on a cohort of patients with incomplete Locked-In Syndrome (LIS). Methods Seven patients performed repeated sessions with each BCI. We assessed BCI performance, mental workload and overall satisfaction for both systems. We also investigated the effect of the quality of life and level of motor impairment on the performance. Results All seven patients were able to achieve an accuracy of 70% or more with the SSVEP-based BCI, compared to 3 patients with the P3-based BCI, showing a better performance with the SSVEP BCI than with the P3 BCI in the studied cohort. Moreover, the better performance of the SSVEP-based BCI was accompanied by a lower mental workload and a higher overall satisfaction. No relationship was found between BCI performance and level of motor impairment or quality of life. Conclusion Our results show a better usability of the SSVEP-based BCI than the P3-based one for the sessions performed by the tested population of locked-in patients with respect to all the criteria considered. The study shows the advantage of developing alternative BCIs with respect to the traditional matrix-based P3 speller using different designs and signal modalities such as SSVEPs to build a faster, more accurate, less mentally demanding and more satisfying BCI by testing both types of BCIs on a convenience sample of LIS patients.

Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling

The Mind Speller is a Brain-Computer Interface which enables subjects to spell text on a computer screen by detecting P300 Event-Related Potentials in their electroencephalograms. This BCI application is of particular interest for disabled patients who have lost all means of verbal and motor communication. We report on the implementation of a feature extraction procedure on a new ultra low-power 8-channel wireless EEG device. The feature extraction procedure is based on downsampled EEG signal epochs, the Students t-statistic of the Continuous Wavelet Transform, and the Common Spatial Pattern technique. For classification, we use a linear Least-Squares Support Vector Machine. The results show that subjects are potentially able to communicate a character in less than ten seconds with an accuracy of 94.5%, which is more than twice as fast as the state of the art. In addition since our EEG device is wireless it offers an increased comfort to the subject.

Simultaneous detection of P300 and steady-state visually evoked potentials for hybrid brain-computer interface.

Objective We study the feasibility of a hybrid Brain-Computer Interface (BCI) combining simultaneous visual oddball and Steady-State Visually Evoked Potential (SSVEP) paradigms, where both types of stimuli are superimposed on a computer screen. Potentially, such a combination could result in a system being able to operate faster than a purely P300-based BCI and encode more targets than a purely SSVEP-based BCI. Approach We analyse the interactions between the brain responses of the two paradigms, and assess the possibility to detect simultaneously the brain activity evoked by both paradigms, in a series of 3 experiments where EEG data are analysed offline. Main Results Despite differences in the shape of the P300 response between pure oddball and hybrid condition, we observe that the classification accuracy of this P300 response is not affected by the SSVEP stimulation. We do not observe either any effect of the oddball stimulation on the power of the SSVEP response in the frequency of stimulation. Finally results from the last experiment show the possibility of detecting both types of brain responses simultaneously and suggest not only the feasibility of such hybrid BCI but also a gain over pure oddball- and pure SSVEP-based BCIs in terms of communication rate.

Steady State Visual Evoked Potential Based Computer Gaming – The Maze

We introduce a game, called “The Maze”, as a brain-computer interface (BCI) application in which an avatar is navigated through a maze by analyzing the player’s steady-state visual evoked potential (SSVEP) responses recorded with electroencephalography (EEG). The same computer screen is used for displaying the game environment and for the visual stimulation. The algorithms for EEG data processing and SSVEP detection are discussed in depth. We propose the system parameter values, which provide an acceptable trade-off between the game control accuracy and interactivity.

Error-related potential recorded by EEG in the context of a p300 mind speller brain-computer interface

The Mind Speller® is a Brain-Computer Interface (BCI) which enables subjects to spell text on a computer screen by detecting P300 Event-Related Potentials in their electroencephalograms (EEG). This BCI application is of particular interest for disabled patients who have lost all means of verbal and motor communication. Error-related Potentials (ErrP) in the EEG are generated by the subjects perception of an error. We report on the possibility of using this ErrP for improving the performance of our Mind Speller®. We tested 6 subjects and recorded several typing sessions for each of them. Responses to correct and incorrect performances of the BCI are recorded and compared. The shape of the received ErrP is compared to other studies. The detection of this ErrP and its integration in the Mind Speller® are discussed.

COMPARISON OF LINEAR CLASSIFICATION METHODS FOR P300 BRAIN-COMPUTER INTERFACE ON DISABLED SUBJECTS

In this position paper, we investigate whether a parallel factor analysis (Parafac) decomposition is beneficial to the decoding of steady-state visual evoked potentials (SSVEP) present in electroencephalogram (EEG) recordings taken from the subject’s scalp. In particular, we develop an automatic algorithm aimed at detecting the stimulation frequency after Parafac decomposition. The results are validated on recordings made from 54 subjects using consumer-grade EEG hardware (Emotiv’s EPOC headset) in a real-world environment. The detection of one frequency among 12, 4 and 2 possible was considered to assess the feasibility for Brain Computer Interfacing (BCI). We determined the frequencies subsets, among all subjects, that maximize the detection rate.

Steady State Visual Evoked Potential (SSVEP) - Based Brain Spelling System with Synchronous and Asynchronous Typing Modes

The paper presents an EEG-based wireless brain-computer interface (BCI) with which subjects can mind-spell text on a computer screen. The application is based on the detection of steady-state visual evoked potentials (SSVEP) in EEG signals recorded on the scalp of the subject. The performance of the BCI is compared for two different classification paradigms, called synchronous and asynchronous modes.