BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller

Brain-computer interface P300 speller aims at helping patients unable to activate muscles to spell words by means of their brain signal activities. Associated to this BCI paradigm, there is the problem of classifying electroencephalogram signals related to responses to some visual stimuli. This pape...

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Bibliographic Details
Published in:	IEEE transactions on biomedical engineering 2008-03, Vol.55 (3), p.1147-1154
Main Authors:	Rakotomamonjy, Alain, Guigue, Vincent
Format:	Article
Language:	eng
Subjects:	Algorithms Artificial Intelligence Brain - physiology Brain computer interfaces channel selection Cognition - physiology Cognitive science Computer interfaces Computer science Databases, Factual Diseases electroencephalogram signal classification Electroencephalography - methods ensemble of classifiers Enterprise resource planning Event-Related Potentials, P300 - physiology Humans Muscles Pattern classification Pattern Recognition, Automated - methods Performance evaluation Reproducibility of Results Sensitivity and Specificity Support vector machine classification Support vector machines Testing User-Computer Interface Word Processing - methods
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Summary:	Brain-computer interface P300 speller aims at helping patients unable to activate muscles to spell words by means of their brain signal activities. Associated to this BCI paradigm, there is the problem of classifying electroencephalogram signals related to responses to some visual stimuli. This paper addresses the problem of signal responses variability within a single subject in such brain-computer interface. We propose a method that copes with such variabilities through an ensemble of classifiers approach. Each classifier is composed of a linear support vector machine trained on a small part of the available data and for which a channel selection procedure has been performed. Performances of our algorithm have been evaluated on dataset II of the BCI Competition III and has yielded the best performance of the competition.
ISSN:	0018-9294 1558-2531