单通道脑电信号眼电伪迹去除算法研究

发布时间：2018-04-29 10:40

本文选题：脑电信号 + 眼电伪迹　；参考：《自动化学报》2017年10期

【摘要】：由眨眼和眼动产生的眼电伪迹(Electrooculography,EOG)信号是脑电信号(Electroencephalography,EEG)中的主要噪声信号之一.目前,多通道脑电信号中眼电伪迹的去除算法已经较为成熟.而在单通道脑电信号的眼电伪迹去除中,由于采集通道数量较少且缺乏参考眼电信号,目前尚无十分有效的去除方法.本文提出一种基于小波变换(Wavelet transform,WT)、集合经验模态分解(Ensemble empirical mode decomposition,EEMD)和独立成分分析(Independent component analysis,ICA)的WT-EEMD-ICA单通道脑电信号眼电伪迹去除算法.实验表明:WT-EEMD-ICA算法有效地解决了单通道WT-ICA算法中的超完备问题,能够有效去除单通道脑电信号中的眼电伪迹,并且分离出的眼电伪迹成分与参考通道采集的眼电信号相关性较强.
[Abstract]:Electrooculography EOG (EOG) signal produced by blink and eye movement is one of the main noise signals in EEG electroencephalography (EGG). At present, the removal algorithm of Eye-electric artifacts in multichannel EEG signals has been more mature. However, there is no effective method for the removal of Eye-electric artifacts in single-channel EEG because of the small number of collecting channels and the lack of reference Eye-signals. In this paper, an Eye-artifact removal algorithm for WT-EEMD-ICA single-channel EEG based on wavelet transform WTA, empirical mode decomposition (EMD) and independent component analysis (ICA) is proposed. The experimental results show that the one-channel WT-ICA algorithm can effectively remove the Eye-electric artifacts from the single-channel EEG signals, and the components of the separated Eye-EEG artifacts have strong correlation with the Eye-electric signals collected by the reference channels. The proposed algorithm can effectively solve the problem of supercompleteness in the single-channel WT-ICA algorithm and can effectively remove the Eye-electric artifacts from the single-channel EEG signals.
【作者单位】：哈尔滨工业大学电气工程及自动化学院;61135部队;哈尔滨医科大学基础医学院;
【基金】：国家自然科学基金(61301012) 哈尔滨市科技创新人才研究专项资金(2015RAXXJ038) 中央高校基本科研业务费专项资金(2013004,2013005)资助~~
【分类号】：TN911.7

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