Independent component analysis and time-frequ ency method for noisy EEG signal analysis

被引：0

作者：

Ye, Ning ^{[1
]}

Wang, Xu ^{[1
]}

Sun, Yuge ^{[1
]}

机构：

[1] Northeastern Univ, Informat Sci & Engn Coll, Shenyang 110004, Peoples R China

来源：

2006 8TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, VOLS 1-4 | 2006年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

EEG signal recorded by scalp electrode is a mixture of signals from different brain regions and with noisy signal. Independent component analysis (ICA) is essentially a method for extracting individual signals from mixtures of signals. Time-frequency analysis provides a powerful tool for the analysis of EEG signals. The original EEG signal is divided into independent components, and the noisy components were chosen by time-frequency analysis method. The results show that reconstructed the reserved components can obtain clean EEG signal.

引用

页码：2460 / +

页数：2

共 10 条

[1] Analysis of EEG records in an epileptic patient using wavelet transform [J].

Adeli, H ;

Zhou, Z ;

Dadmehr, N .

JOURNAL OF NEUROSCIENCE METHODS, 2003, 123 (01) :69-87

[2]

CO M, 2005, ARTIF INTELL, V33, P209

[3]

DAVID V, 2003, J FRANKLIN I, V340, P77

[4]

DIK KW, 2004, NEUROCOMPUTING, V61, P479

[5] Phase and amplitude analysis in time-frequency space - application to voluntary finger movement [J].

Ginter, J ;

Blinowska, KJ ;

Kaminski, M ;

Durka, PJ .

JOURNAL OF NEUROSCIENCE METHODS, 2001, 110 (1-2) :113-124

[6] Independent component analysis for noisy data - MEG data analysis [J].

Ikeda, S ;

Toyama, K .

NEURAL NETWORKS, 2000, 13 (10) :1063-1074

[7]

JAMES, 2002, TRENDS COGN SCI, V6, P59

[8] Wigner distributions (nearly) everywhere: time-frequency analysis of signals, systems, random processes, signal spaces, and frames [J].

Matz, G ;

Hlawatsch, F .

SIGNAL PROCESSING, 2003, 83 (07) :1355-1378

[9]

Oja Erkki., 2001, INDEPENDENT COMPONEN

[10]

SILVIA C, 2006, NEUROCOMPUTING, V69, P769

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