Journal of South China University of Technology (Natural Science Edition) ›› 2011, Vol. 39 ›› Issue (5): 12-17,35.doi: 10.3969/j.issn.1000-565X.2011.05.003

• Electronics, Communication & Automation Technology • Previous Articles     Next Articles

Blind Extraction of μ-Rhythm ElectroencephalogramSignals Based on High-Order Statistics

Cai Kun  Xie Sheng-li   

  1. School of Electronic and Information Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China
  • Received:2010-05-07 Revised:2010-11-08 Online:2011-05-25 Published:2011-04-01
  • Contact: 蔡坤(1977-)，男，博士生，讲师，主要从事盲信号处理技术在生物医学工程中的应用研究 E-mail:caikun@scau.edu.cn
  • About author:蔡坤(1977-)，男，博士生，讲师，主要从事盲信号处理技术在生物医学工程中的应用研究
  • Supported by:

    国家自然科学基金资助项目(60774094，60874061)

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Abstract:

Proposed in this paper is a new fixed-point blind extraction algorithm of μ-rhythm electroencephalogramsignals based on high-order statistics,which takes into consideration the super-Guassian characteristic and non-symmetricdistribution of the signals. Then,the local stability and convergence of the proposed algorithm are discussed,and the corresponding conditions are determined. Finally,the algorithm is compared with the FastICA algorithmthrough a case study on simulated and clinical μ-rhythm electroencephalogram signals,with a higher effectivenessof it being revealed.

Key words: electroencephalogram, signal processing, blind extraction, kurtosis, skewness, &mu, rhythm