基于盲反卷积的脑电信号盲分离研究

R318; 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。...

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Bibliographic Details
Published in东北大学学报（自然科学版） Vol. 37; no. 8; pp. 1100 - 1103
Main Author 黄璐 王宏
Format Journal Article
LanguageChinese
Published 东北大学中荷生物医学与信息工程学院，辽宁沈阳 110167 2016
大连海洋大学信息工程学院，辽宁大连 116023%东北大学机械工程与自动化学院,辽宁沈阳,110819
Subjects
信号处理
生理信号
盲分离
盲反卷积
脑电信号
physiological signal
盲反卷积
signal processing
生理信号
脑电信号
blind separation
信号处理
electroencephalogram
盲分离
blind deconvolution
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ISSN1005-3026
DOI10.3969/j.issn.1005-3026.2016.08.008

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Summary:R318; 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。
Bibliography:physiological signal; electroencephalogram; signal processing; blind separation; blind deconvolution
21-1344/T
H U A N G L u , W A N G H o n g （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 ofMechanical Engineering GAutomation, Northeastern University, Shenyang 110819,China.）
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 showthat the method proposed can achieve blind separation of EEG successfully,providi
ISSN:1005-3026
DOI:10.3969/j.issn.1005-3026.2016.08.008