脑电信号中眼电伪迹自动去除方法的研究

doi:10.3969/j.issn.0372-2112.2013.06.026

电子学报 ›› 2013, Vol. 41 ›› Issue (6): 1207-1213.DOI: 10.3969/j.issn.0372-2112.2013.06.026

脑电信号中眼电伪迹自动去除方法的研究

李明爱, 崔燕, 杨金福

北京工业大学电子信息与控制工程学院, 北京 100124

收稿日期:2012-04-05 修回日期:2012-09-25 出版日期:2013-06-25
- 作者简介:
- 李明爱女,1966年生,河南鹤壁人,2006年于北京工业大学获得博士学位,现为北京工业大学副教授、硕导,主要从事脑机接口、智能信息处理与模式识别等领域的研究. E-mail:limingai@bjut.edu.cn 崔燕女,1987年生,江苏盐城人,2010年获得南京工程学院学士学位,现为北京工业大学模式识别与智能系统专业硕士研究生,主要研究方向为脑机接口、信息处理与模式识别.
- 基金资助:
- 北京市教委项目面上项目 (No.KM201110005005); 北京市自然科学基金 (No.4112011); 北京工业大学基础研究基金 (No.X4002011201101)

Research on Removing Ocular Artifact Automatically From EEG Signals

LI Ming-ai, CUI Yan, YANG Jin-fu

College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China

Received:2012-04-05 Revised:2012-09-25 Online:2013-06-25 Published:2013-06-25
- Supported by:
- General Program of R&D Program of Beijing Municipality Education Commission (No.KM201110005005); Natural Science Foundation of Beijing Municipality, China (No.4112011); Basic Research Foundation of Beijing University of Technology (No.X4002011201101)

摘要/Abstract

摘要： 针对实际采集的脑电信号受眼电干扰较大,提出一种基于离散小波变换(DWT)与独立分量分析(ICA)的自动去除眼电伪迹的方法(DWICA).对采集的多导脑电和眼电信号进行离散小波变换,获取多尺度小波系数,将串接小波系数作为ICA的输入;利用基于负熵判据的FastICA算法实现独立成分的快速获取,引入夹角余弦准则自动识别眼迹成分,并经过ICA逆变换将剔除眼迹后的独立成分投影返回到原脑电信号各个电极;通过DWT逆变换重构信号,即可得到去除眼迹的各导脑电信号.实验结果表明,DWICA方法极大地提高了脑电信号的信噪比,抗噪能力强且实时性好,为脑电信号的在线预处理提供了新思路.

关键词: 脑电, 眼电伪迹, 离散小波变换, 独立分量分析, 自动去除

Abstract: Electroencephalography(EEG)is easily affected by ocular artifact(OA),which appears in EEG randomly as a big pulse.Based on discrete wavelet transform(DWT)and independent component analysis(ICA),a novel automatic method of OA removal,denoted as DWICA,was proposed.Firstly,DWT was applied to the recorded EEG and electrooculogram(EOG)to obtain multiple scale coefficients,and the combined coefficients were considered as the input for ICA.Secondly,the independent components were acquired based on FastICA algorithm with negentropy criterion.The angle cosine criterion was introduced to recognize ocular artifact component.Furthermore,the inverse algorithm of ICA was applied to project the independent components without OA to original electrodes.Finally,the EEG were reconstructed using the inverse algorithm of DWT,and then the pure EEG were obtained.Experimental results show that DWICA is preferable in automatic removal of OA.The method provides a new idea for on-line preprocessing of EEG signals.

Key words: electroencephalography, ocular artifact, discrete wavelet transform(DWT), independent component analysis(ICA), automatic removal

中图分类号:

R318

李明爱, 崔燕, 杨金福. 脑电信号中眼电伪迹自动去除方法的研究[J]. 电子学报, 2013, 41(6): 1207-1213.

LI Ming-ai, CUI Yan, YANG Jin-fu . Research on Removing Ocular Artifact Automatically From EEG Signals[J]. Acta Electronica Sinica, 2013, 41(6): 1207-1213.

参考文献

[1] N P Castellanos,V A Makarov.Recovering EEG brain signals:artifact suppression with wavelet enhanced independent component analysis[J].Journal of Neuroscience Methods,2006,158(2):300-312.

[2] 徐宝国,宋爱国,费树岷.在线脑机接口中脑电信号的特征提取与分类方法[J].电子学报,2011,39(5):1025-1030. Xu Bao-guo,Song Ai-guo,Fei Shu-min.Feature extraction and classification of EEG in online brain-computer interface[J].Acta Electronica Sinica,2011,39(5):1025-1030.(in Chinese)

[3] 杜晓燕,李颖洁,朱贻盛,等.脑电信号伪迹去除的研究进展[J].生物医学工程学杂志,2008,25(2):464-467. Du Xiaoyan,Li Yingjie,Zhu Yisheng,et al.Removal of artifacts from EEG signal.Journal of Biomedical Engineering,2008,25(2):464-467.(in Chinese)

[4] T Zikov,et al.A wavelet based de-noising technique for ocular artifact correction of the electroencephalogram[A].Proceedings of the Second Joint EMBS/BMES Conference[C].Houston:Institute of Electrical and Electronics Engineers,2002.98-105.

[5] TP Jung,et al.Removing electroencephalographic artifacts:Comparison between ICA and PCA[A].Proceedings of the 1998 IEEE Signal Processing Society Workshop[C].Cambridge:IEEE,1998.63-72.

[6] 谢松云,张振中,张伟平,等.基于ICA的脑电信号去噪方法研究与应用[J].中国医学影像技术,2007,23(10):1562-1565. Xie Song-yun,Zhang Zhen-zhong,Zhang Wei-ping,et al.Method and application of removing noise from EEG signals based on ICA method[J].Chinese Journal of Medical Imaging Technology,2007,23(10):1562-1565.(in Chinese)

[7] C A Joyce,I F Gorodnitsky,M Kutas.Automatic removal of eye movement and blink artifacts from EEG data using blind component separation[J].Psychophysiology,2004,41(2):313-325.

[8] A Flexer,H Bauer,J Pripfl,et al.Using ICA for removal of ocular artifacts in EEG recorded from blind subjects[J].Neural Networks,2005,18(7):998-1005.

[9] 罗志增,李文国.基于小波变换和盲信号分离的多通道肌电信号处理方法[J].电子学报,2009,37(4):823-827. Luo Zhi-zeng,Li Wen-guo.A method of multi-channel EMG disposal based on wavelet transform and blind signal separation[J].Acta Electronica Sinica,2009,37(4):823-827.(in Chinese)

[10] M G Jafari,J A Chambers.Fetal electrocardiogram extraction by sequential source separation in the wavelet domain[J].IEEE Transactions on Biomedical Engineering,2005,52(3):390-400.

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脑电信号中眼电伪迹自动去除方法的研究

Research on Removing Ocular Artifact Automatically From EEG Signals

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