Nonnegative tensor factorization for continuous EEG classification

Hyekyoung Lee, Yong Deok Kim, Andrzej Cichocki, Seungjin Choi

Research output: Contribution to journalArticlepeer-review

103 Citations (Scopus)


In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classify multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two data sets in BCI competition, confirm the useful behavior of the method for continuous EEG classification.

Original languageEnglish
Pages (from-to)305-317
Number of pages13
JournalInternational Journal of Neural Systems
Issue number4
Publication statusPublished - Aug 2007
Externally publishedYes


  • Brian computer interface
  • EEG classification
  • Nonnegative matrix factorization
  • Nonnegative tensor factorization
  • Spectral feature extraction


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