Nonnegative tensor factorization for continuous EEG classification.

Nonnegative tensor factorization for continuous EEG classification. Research Abstract Details 

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Nonnegative tensor factorization for continuous EEG classification. Abstract Text:

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

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.

Nonnegative tensor factorization for continuous EEG classification. Publishing Authors By Initials

H Lee,YD Kim,A Cichocki,S Choi,

For similar information science: computing methodologies: software: user-computer interface research abstracts see: information science: computing methodologies: software: user-computer interface research

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Nonnegative tensor factorization for continuous EEG classification. Journal Published:

PUBLICATION TYPE: Research Support, Non-U.S. Gov

Journal: International journal of neural systems

VOLUME: 17

Page Numbers: 305-17

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ISSN: 0129-0657

DAY: 28

MONTH: Aug

YEAR: 2007

Nonnegative tensor factorization for continuous EEG classification. Information

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LANGUAGE: eng

NlmUniqueID: 9100527

Nonnegative tensor factorization for continuous EEG classification. Keywords Mesh Terms:

KEYWORDS: User-Computer Interface

MESH TERMS: classification

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Grant and Affiliation Information for Nonnegative tensor factorization for continuous EEG classification.

AFFILIATION: Department of Computer Science, Pohang University of Science and Technology, San 31 Hyoja-dong, Nam-gu, Pohang 790-784, Republic of Korea. leehk@postech.ac.kr

Country: Singapore

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MEDLINETA: Int J Neural Syst

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