Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition

被引：173

作者：

Park, Cheolsoo ^{[1
]}

Looney, David ^{[2
]}

Rehman, Naveed Ur ^{[3
]}

Ahrabian, Alireza ^{[2
]}

Mandic, Danilo P. ^{[2
]}

机构：

[1] Univ Calif San Diego, Dept Bioengn, La Jolla, CA 92093 USA

[2] Univ London Imperial Coll Sci Technol & Med, Elect & Elect Engn Dept, London SW7 2BT, England

[3] COMSATS Inst Informat Technol, Islamabad, Pakistan

来源：

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING | 2013年 / 21卷 / 01期

关键词：

Brain-computer interface (BCI); electroencephalogram (EEG); empirical mode decomposition; motor imagery paradigm; noise assisted multivariate extensions of empirical mode decomposition (NA-MEMD); BRAIN-COMPUTER INTERFACE; SINGLE-TRIAL EEG; DESYNCHRONIZATION; SYNCHRONIZATION; COMPONENTS; SPECTRUM; DYNAMICS; MU;

D O I：

10.1109/TNSRE.2012.2229296

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Brain electrical activity recorded via electroencephalogram (EEG) is the most convenient means for brain-computer interface (BCI), and is notoriously noisy. The information of interest is located in well defined frequency bands, and a number of standard frequency estimation algorithms have been used for feature extraction. To deal with data nonstationarity, low signal-to-noise ratio, and closely spaced frequency bands of interest, we investigate the effectiveness of recently introduced multivariate extensions of empirical mode decomposition (MEMD) in motor imagery BCI. We show that direct multi-channel processing via MEMD allows for enhanced localization of the frequency information in EEG, and, in particular, its noise-assisted mode of operation (NA-MEMD) provides a highly localized time-frequency representation. Comparative analysis with other state of the art methods on both synthetic benchmark examples and a well established BCI motor imagery dataset support the analysis.

引用

页码：10 / 22

页数：13

共 42 条

[1] EEG-based mental task classification: Linear and nonlinear classification of movement imagery [J].

Akrami, Athena ;

Solhjoo, Soroosh ;

Motie-Nasrabadi, Ali ;

Hashemi-Golpayegani, Mohammad-Reza .

2005 27TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7, 2005, :4626-4629

[2]

[Anonymous], 1997, SUPPORT VECTOR REGRE

[3]

[Anonymous], 2003, IEEE EURASIP WORKSH

[4]

[Anonymous], 2008, Signal processing techniques for knowledge extraction and information fusion, DOI DOI 10.1007/978-0-387-74367-7

[5]

Bin Altaf MU, 2007, INT CONF ACOUST SPEE, P1009

[6] The non-invasive Berlin Brain-Computer Interface:: Fast acquisition of effective performance in untrained subjects [J].

Blankertz, Benjamin ;

Dornhege, Guido ;

Krauledat, Matthias ;

Mueller, Klaus-Robert ;

Curio, Gabriel .

NEUROIMAGE, 2007, 37 (02) :539-550

[7]

Brevdo E., 2011, CORR

[8]

Canu S., 2005, PERCEPTION SYST INFO

[9]

Cohen L., 1993, ICASSP-93. 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing (Cat. No.92CH3252-4), P105, DOI 10.1109/ICASSP.1993.319605

[10] Equidistribution on the sphere [J].

Cui, JJ ;

Freeden, W .

SIAM JOURNAL ON SCIENTIFIC COMPUTING, 1997, 18 (02) :595-609

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