Nonnegative matrix factorization for EEG signal classification

Weixiang Liu; Nanning Zheng; Xi Li

doi:10.1007/978-3-540-28648-6_75

Nonnegative matrix factorization for EEG signal classification

Weixiang Liu
, Nanning Zheng
, Xi Li

College of Artificial Intelligence and Robotics

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

11 Scopus citations

Abstract

Nonnegative matrix factorization (NMF) is a powerful feature extraction method for nonnegative data. This paper applies NMF to feature extraction for Electroencephalogram (EEG) signal classification. The basic idea is to decompose the magnitude spectra of EEG signals from six channels via NMF. Primary experiments on signals from one subject performing two tasks show high classification accuracy rate based on linear discriminant analysis. Our best results are close to 98% 1 when training data and testing data from the same day, and 82% when training data and testing data from different days.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Fuliang Yin, Chengan Guo, Jun Wang
Publisher	Springer Verlag
Pages	470-475
Number of pages	6
ISBN (Print)	3540228438, 9783540228431
DOIs	https://doi.org/10.1007/978-3-540-28648-6_75
State	Published - 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3174
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-540-28648-6_75

Cite this

Liu, W., Zheng, N., & Li, X. (2004). Nonnegative matrix factorization for EEG signal classification. In F. Yin, C. Guo, & J. Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 470-475). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3174). Springer Verlag. https://doi.org/10.1007/978-3-540-28648-6_75

Liu, Weixiang ; Zheng, Nanning ; Li, Xi. / Nonnegative matrix factorization for EEG signal classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Fuliang Yin ; Chengan Guo ; Jun Wang. Springer Verlag, 2004. pp. 470-475 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Nonnegative matrix factorization (NMF) is a powerful feature extraction method for nonnegative data. This paper applies NMF to feature extraction for Electroencephalogram (EEG) signal classification. The basic idea is to decompose the magnitude spectra of EEG signals from six channels via NMF. Primary experiments on signals from one subject performing two tasks show high classification accuracy rate based on linear discriminant analysis. Our best results are close to 98\% 1 when training data and testing data from the same day, and 82\% when training data and testing data from different days.",

author = "Weixiang Liu and Nanning Zheng and Xi Li",

year = "2004",

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Liu, W, Zheng, N & Li, X 2004, Nonnegative matrix factorization for EEG signal classification. in F Yin, C Guo & J Wang (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3174, Springer Verlag, pp. 470-475. https://doi.org/10.1007/978-3-540-28648-6_75

Nonnegative matrix factorization for EEG signal classification. / Liu, Weixiang; Zheng, Nanning; Li, Xi.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Fuliang Yin; Chengan Guo; Jun Wang. Springer Verlag, 2004. p. 470-475 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3174).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Nonnegative matrix factorization for EEG signal classification

AU - Liu, Weixiang

AU - Zheng, Nanning

AU - Li, Xi

PY - 2004

Y1 - 2004

N2 - Nonnegative matrix factorization (NMF) is a powerful feature extraction method for nonnegative data. This paper applies NMF to feature extraction for Electroencephalogram (EEG) signal classification. The basic idea is to decompose the magnitude spectra of EEG signals from six channels via NMF. Primary experiments on signals from one subject performing two tasks show high classification accuracy rate based on linear discriminant analysis. Our best results are close to 98% 1 when training data and testing data from the same day, and 82% when training data and testing data from different days.

AB - Nonnegative matrix factorization (NMF) is a powerful feature extraction method for nonnegative data. This paper applies NMF to feature extraction for Electroencephalogram (EEG) signal classification. The basic idea is to decompose the magnitude spectra of EEG signals from six channels via NMF. Primary experiments on signals from one subject performing two tasks show high classification accuracy rate based on linear discriminant analysis. Our best results are close to 98% 1 when training data and testing data from the same day, and 82% when training data and testing data from different days.

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SN - 3540228438

SN - 9783540228431

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 470

EP - 475

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Yin, Fuliang

A2 - Guo, Chengan

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PB - Springer Verlag

ER -

Liu W, Zheng N, Li X. Nonnegative matrix factorization for EEG signal classification. In Yin F, Guo C, Wang J, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 470-475. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-28648-6_75

Nonnegative matrix factorization for EEG signal classification

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