Abstract
This paper addresses the problem of signal responses variability within a single subject in P300 speller Brain-Computer Interfaces. We propose here a method to cope with these variabilities by considering a single learner for each acquisition session. Each learner consists of a channel selection procedure and a classifier. Our algorithm has been benchmarked with the data and the results of the BCI 2003 competition dataset and we clearly show that our approach yields to state-of-the art results.
This work was supported by grants from the IST programme of the European Community under the PASCAL Network of excellence, IST-2002-506778.
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Rakotomamonjy, A., Guigue, V., Mallet, G., Alvarado, V. (2005). Ensemble of SVMs for Improving Brain Computer Interface P300 Speller Performances. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_8
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DOI: https://doi.org/10.1007/11550822_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28752-0
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