Nonnegative Tensor Factorization for Continuous EEG Classification

Department of Computer Science, Pohang University of Science and Technology, San 31 Hyoja-dong, Nam-gu, Pohang 790-784, Republic of Korea.
International Journal of Neural Systems (Impact Factor: 6.51). 09/2007; 17(4):305-17. DOI: 10.1142/S0129065707001159
Source: PubMed


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.

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Available from: Lee Hyekyoung, Jun 24, 2014
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    IEEE International Workshop on Machine Learning for Signal Processing; 09/2013
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