Arne Robben

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.

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.

Decoding phase-based information from steady-state visual evoked potentials with use of complex-valued neural network

In this paper, we report on the decoding of phase-based information from steady-state visual evoked potential (SSVEP) recordings by means of a multilayer feedforward neural network based on multivalued neurons. Networks of this kind have inputs and outputs which are well fitted for the considered task. The dependency of the decoding accuracy w.r.t. the number of targets and the decoding window size is discussed. Comparing existing phase-based SSVEP decoding methods with the proposed approach, we show that the latter performs better for the larger amount of target classes and the sufficient size of decoding window. The necessity of the proper frequency selection for each subject is discussed.

Subject-adaptive steady-state visual evoked potential detection for brain-computer interface

We report on the development of a four command Brain-Computer Interface (BCI) based on steady-state visual evoked potential (SSVEP) responses detected from human electroencephalograms (EEGs). The proposed system combines spatial filtering, feature extraction and selection, and a classifier. Two types of classifiers were compared: one based on equal treatment of all harmonics in all EEG channels and the second based on preliminary training resulting in a weighted treatment of the harmonics. Results from six healthy subjects are evaluated.

Decoding phase-based information from SSVEP recordings: A comparative study

In this paper, we report on the decoding of phase-based information, from steady-state visual evoked potential (SSVEP) recordings, by means of different classifiers. In addition to the ones reported in the literature, we also consider other types of classifiers such as the multilayer feedforward neural network based on multi-valued neurons (MLMVN), and the classifier based on fuzzy logic, which we especially tuned for phase-based SSVEP decoding. The dependency of the decoding accuracy on the number of targets and on the decoding window size are discussed. When comparing existing phase-based SSVEP decoding methods with the proposed ones, we are able to show that the latter ones perform better, for different parameter settings, but especially when having multiple targets. The necessity of optimizing the target frequencies to the individual subject is also discussed.

Brain-computer interface research at Katholieke Universiteit Leuven

We present an overview of our Brain-computer interface (BCI) research, invasive as well as non-invasive, during the past four years. The invasive BCIs are based on local field-and action potentials recorded with microelectrode arrays implanted in the visual cortex of the macaque monkey. The non-invasive BCIs are based on electroencephalogram (EEG) recorded from a human subjects scalp. Several EEG paradigms were used to enable the subject to type text or to select icons on a computer screen, without having to rely on ones fingers, gestures, or any other form of motor activity: the P300 event-related potential, the steady-state visual evoked potential, and the error related potential. We report on the status of our EEG BCI tests on healthy subjects as well as patients with severe communication disabilities, and our demonstrations to a broad audience to raise the public awareness of BCI.

Combining object detection and brain computer interfacing: Towards a new way of subject-environment interaction

In this paper, we present an application that is a synergy of two research disciplines: visual object detection (and localization) and brain-computer interfacing. The goal is to construct an alternative way for a person to select real objects in the environment, without using speech, or any other form of muscular activity. Due to the latter, our application is potentially useful for patients that suffer from severe motor impairments since it would enable them to engage in a high-level interaction process with objects.