BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm

We propose an approach to analyze data from the P300 speller paradigm using the machine-learning technique support vector machines. In a conservative classification scheme, we found the correct solution after five repetitions. While the classification within the competition is designed for offline a...

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Bibliographic Details
Published in:	IEEE transactions on biomedical engineering 2004-06, Vol.51 (6), p.1073-1076
Main Authors:	Kaper, M., Meinicke, P., Grossekathoefer, U., Lingner, T., Ritter, H.
Format:	Article
Language:	eng
Subjects:	Algorithms Artificial Intelligence Brain - physiology Brain computer interfaces Cognition - physiology Councils Data analysis Databases, Factual Electrodes Electroencephalography Electroencephalography - classification Electroencephalography - methods Event detection Event-Related Potentials, P300 - physiology Humans Pattern recognition Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Signal Processing, Computer-Assisted Support vector machine classification Support vector machines Testing User-Computer Interface Word Processing
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Summary:	We propose an approach to analyze data from the P300 speller paradigm using the machine-learning technique support vector machines. In a conservative classification scheme, we found the correct solution after five repetitions. While the classification within the competition is designed for offline analysis, our approach is also well-suited for a real-world online solution: It is fast, requires only 10 electrode positions and demands only a small amount of preprocessing.
ISSN:	0018-9294 1558-2531