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

人工智能学院

Xi'an Jiaotong University

科研成果: 书/报告/会议事项章节 › 章节 › 同行评审

11 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
编辑	Fuliang Yin, Chengan Guo, Jun Wang
出版商	Springer Verlag
页	470-475
页数	6
ISBN（印刷版）	3540228438, 9783540228431
DOI	https://doi.org/10.1007/978-3-540-28648-6_75
出版状态	已出版 - 2004

出版系列

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	3174
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

访问文件

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

其它文件与链接

链接到 Scopus 的出版物

引用此

Liu, W., Zheng, N., & Li, X. (2004). Nonnegative matrix factorization for EEG signal classification. 在 F. Yin, C. Guo, & J. Wang (编辑), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (页码 470-475). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 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). 编辑 / Fuliang Yin ; Chengan Guo ; Jun Wang. Springer Verlag, 2004. 页码 470-475 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Nonnegative matrix factorization for EEG signal classification",

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",

doi = "10.1007/978-3-540-28648-6\_75",

language = "英语",

isbn = "3540228438",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "470--475",

editor = "Fuliang Yin and Chengan Guo and Jun Wang",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Liu, W, Zheng, N & Li, X 2004, Nonnegative matrix factorization for EEG signal classification. 在 F Yin, C Guo & J Wang (编辑), 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), 卷 3174, Springer Verlag, 页码 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). 编辑 / Fuliang Yin; Chengan Guo; Jun Wang. Springer Verlag, 2004. 页码 470-475 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 3174).

科研成果: 书/报告/会议事项章节 › 章节 › 同行评审

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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U2 - 10.1007/978-3-540-28648-6_75

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

M3 - 章节

AN - SCOPUS:35048872171

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

A2 - Wang, Jun

PB - Springer Verlag

ER -

Liu W, Zheng N, Li X. Nonnegative matrix factorization for EEG signal classification. 在 Yin F, Guo C, Wang J, 编辑, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. 页码 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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