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.
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Lee, H., Cichocki, A., Choi, S. (2006). Nonnegative Matrix Factorization for Motor Imagery EEG Classification. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_26
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DOI: https://doi.org/10.1007/11840930_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38871-5
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