东北大学学报:自然科学版 ›› 2016, Vol. 37 ›› Issue (8): 1100-1103.DOI: 10.12068/j.issn.1005-3026.2016.08.008

• 信息与控制 • 上一篇    下一篇

基于盲反卷积的脑电信号盲分离研究

黄璐1,2, 王宏3   

  1. ( 1. 东北大学 中荷生物医学与信息工程学院, 辽宁 沈阳110167; 2. 大连海洋大学 信息工程学院, 辽宁 大连116023; 3. 东北大学 机械工程与自动化学院, 辽宁 沈阳110819)
  • 收稿日期:2015-05-18 修回日期:2015-05-18 出版日期:2016-08-15 发布日期:2016-08-12
  • 通讯作者: 黄璐
  • 作者简介:黄璐(1979-),女,辽宁开原人,东北大学博士研究生; 王宏(1960-),女,辽宁沈阳人,东北大学教授,博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61071057).

Blind Separation of EEG Based on Blind Deconvolution

HUANG Lu1,2, WANG Hong3   

  1. 1. Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110167, China; 2. College of Information Engineering, Dalian Ocean University, Dalian 116023, China; 3. School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China.
  • Received:2015-05-18 Revised:2015-05-18 Online:2016-08-15 Published:2016-08-12
  • Contact: WANG Hong
  • About author:-
  • Supported by:
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摘要: 采用卷积混合模型描述观测脑电信号(electroencephalogram,EEG),提出一种基于盲反卷积的EEG盲分离方法.结合EEG源成分的独立性确定代价函数,并采用共轭梯度法进行迭代寻优.针对EEG仿真实验数据进行方法验证,采用分离信号与源信号之间的相关系数作为验证指标.实验结果表明,本文方法可以较好地实现EEG盲分离,为EEG信号处理和其他生理信号处理分析提供理论和方法借鉴.

关键词: 生理信号, 脑电信号, 信号处理, 盲分离, 盲反卷积

Abstract: The convolution mixture model was adopted to describe the as-observed EEG signals, and then a method for the blind separation of EEG based on blind deconvolution was put forward. The cost function was established based on the independence of EEG sources, and iteration was carried out using conjugate gradient method. The verification was implemented with simulation experiment, adopting the correlation coefficients between separated signals and source signals as the verification indexes. Experimental results show that the method proposed can achieve blind separation of EEG successfully, providing a theoretical and practical reference for the processing of EEG and other physiological signals.

Key words: physiological signal, electroencephalogram, signal processing, blind separation , blind deconvolution

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