Nonnegative matrix factorization for motor imagery EEG classification

Authors:
Hyekyoung Lee;Andrzej Cichocki;Seungjin Choi
Affiliations:
Department of Computer Science, Pohang University of Science and Technology, Pohang, Korea;Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Saitama, Japan;Department of Computer Science, Pohang University of Science and Technology, Pohang, Korea
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

Citing 2
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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.