Nonnegative Matrix Factorization for Motor Imagery EEG Classification

  • Hyekyoung Lee
  • Andrzej Cichocki
  • Seungjin Choi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


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.


Mutual Information Motor Imagery Nonnegative Matrix Factorization Brain Computer Interface Complex Morlet Wavelet 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hyekyoung Lee
    • 1
  • Andrzej Cichocki
    • 2
  • Seungjin Choi
    • 1
  1. 1.Department of Computer SciencePohang University of Science and TechnologyPohangKorea
  2. 2.Laboratory for Advanced Brain Signal ProcessingBrain Science Institute, RIKENSaitamaJapan

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