An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Single Trial Detection of EEG Error Potentials: A Tool for Increasing BCI Transmission Rates
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
An introduction to variable and feature selection
The Journal of Machine Learning Research
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
The Journal of Machine Learning Research
Fully online multicommand brain-computer interface with visual neurofeedback using SSVEP paradigm
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
P300 detection based on feature extraction in on-line brain-computer interface
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Brain-computer interface research at Katholieke Universiteit Leuven
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Feasibility of error-related potential detection as novelty detection problem in p300 mind spelling
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Pattern Recognition Letters
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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 subject's 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.