中国组织工程研究

中国组织工程研究 ›› 2013, Vol. 17 ›› Issue (9): 1655-1659.doi: 10.3969/j.issn.2095-4344.2013.09.020

• 骨与关节损伤基础实验 basic experiments of bone and joint injury • 上一篇    下一篇

基于扩展Infomax 独立分量分析算法的脑电信号消噪

黄 艳，黄 华   

  1. 四川大学电气信息学院医学信息工程系， 四川省成都市 610065
  • 收稿日期:2012-08-09 修回日期:2012-12-18 出版日期:2013-02-26 发布日期:2013-02-26
  • 通讯作者: 黄华，博士，教授，四川大学电气信息学院医学信息工程系，四川省成都市 610065
  • 作者简介:黄艳，女，1987年生，四川省锦阳市人，汉族，2010年四川大学毕业，主要从事医学电子学方面的研究。 hyyuxin@yahoo.com.cn

Noise removal in electroencephalogram signal via independent component analysis approach based on the extended information maximization

Huang Yan, Huang Hua   

  1. Department of Medical Information Engineer, Electricity Information College, Sichuan University, Chengdu 610065, Sichuan Province, China
  • Received:2012-08-09 Revised:2012-12-18 Online:2013-02-26 Published:2013-02-26
  • Contact: Huang Hua, Doctor, Professor, Department of Medical Information Engineer, Electricity Information College, Sichuan University, Chengdu 610065, Sichuan Province, China
  • About author:Huang Yan, Department of Medical Information Engineer, Electricity Information College, Sichuan University, Chengdu 610065, Sichuan Province, China hyyuxin@yahoo.com.cn

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598

被引次数

4

摘要：

背景：脑电信号能够反映大脑不同的生理病理状态，但在采集和分析处理过程中极易受到各种噪声的干扰，如眼球运动、眨眼、心电、肌电等，这些噪声的存在严重影响了脑电信号的分析和处理。
目的：介绍了一种基于扩展Infomax的独立分量分析方法，并用于脑电信号消噪。
方法：通过扩展Infomax算法的迭代求得分离矩阵，采用去除噪声分量后的独立成分重构需要记录的脑电信号，观察Matlab仿真得到的去噪后的脑电信号，同时比较去噪前后各导联脑电信号与眼电信号的相关性。
结果与结论：使用扩展Infomax 独立分量分析算法能够成功地去除多导脑电信号中的眼电干扰。再比较去噪前后各导联脑电信号的功率谱，可以发现使用扩展Infomax独立分量分析算法同时也能够成功地去除多导脑电信号中的工频干扰，且对脑电信号中的其他有用信号几乎没有破坏。

关键词: 骨关节植入物, 骨关节损伤基础实验, 脑电信号, 噪声, 干扰, 消噪, 去除, Infomax算法, 扩展, 独立分量分析, 神经网络, 大脑疾病

Abstract:

BACKGROUND: The electroencephalogram signal can reflect the different physiological and pathological activity of brain, many noises are interfused into electroencephalogram signals during the collecting and analyzing process, such as eye movements, eye blinks, heart beats and muscle activities, which affects people’s right to analysis and process the electroencephalogram signal.
OBJECTIVE: To introduce an independent component analysis approach based on the extended information maximization in order to perform the noise removal in electroencephalogram signal.
METHODS: The iteration of extended information maximization was performed to obtain the separation matrix, and the independent component after noise removal was used to reconstruct the electroencephalogram signal that need to be recorded. The electroencephalogram signal after noise removal with Matlab simulation was observed, and the correlation between electroencephalogram signal and electrooculogram signal was compared.
RESULTS AND CONCLUSION: The independent component analysis approach based on extended information maximization could successfully remove the electrooculogram signal intervention from the electroencephalogram signal. The comparison of the power spectrum of the electroencephalogram signals before and after noise removal showed the independent component analysis approach based on extended information maximization could remove the frequency interference from the multi-channel electroencephalogram signals effectively and have no damage to other signals.

Key words: bone and joint implants, basic experiment of bone injury, electroencephalogram signal, noise, interference, noise removal, removal, information maximization algorithm, expansion, independent component analysis, neural network, brain disease

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