Nonnegative matrix factorization for motor imagery EEG classification

Hyekyoung Lee, Andrzej Cichocki, Seungjin Choi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

33 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationArtificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540388710, 9783540388715
Publication statusPublished - 2006
Externally publishedYes
Event16th International Conference on Artificial Neural Networks, ICANN 2006 - Athens, Greece
Duration: 10 Sep 200614 Sep 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4132 LNCS - II
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Artificial Neural Networks, ICANN 2006


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