Abstract
In this paper, we present a method of feature extraction for motor imagery single trial EEG classification, where we exploit nonnegative matrix factorization (NMF) to select discriminative features in the time-frequency representation of EEG. Experimental results with motor imagery EEG data in BCI competition 2003, show that the method indeed finds meaningful EEG features automatically, while some existing methods should undergo cross-validation to find them. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Lee, H., Cichocki, A., & Choi, S. (2006). Nonnegative matrix factorization for motor imagery EEG classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4132 LNCS-II, pp. 250–259). Springer Verlag. https://doi.org/10.1007/11840930_26
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