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Nonnegative tensor factorization for continuous EEG classification
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AI
DOI：10.1142/S0129065707001159.png)
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Abstract
En 中文
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.
Keywords:
brian computer interface
EEG classification
nonnegative matrix factorization
nonnegative
tensor factorization
spectral feature extraction
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