The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition
The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition
复制标题
使用独立分量分析和多元经验模式分解从脑电图信号中去除眼电伪影
DOI：
10.1109/jbhi.2015.2450196
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发表时间：
2016-09-01
影响因子：
7.7
通讯作者：
Yan, Xiangguo
中科院分区：
文献类型：
--
作者：
Wang, Gang;Teng, Chaolin;Yan, Xiangguo
脑电（EEG）记录信号中通常会含有眼电（EOG）伪影。本文将独立分量分析（伊卡）和多元经验模式分解（MEMD）相结合，提出了一种基于ICA的多元经验模式分解（MEMD）方法来去除多通道脑电信号中的眼电伪影（EOAs）。首先，脑电信号被MEMD分解成多个多元固有模态函数（MIMFs）。然后通过重构EOA对应的MIMF来提取EOG相关分量。在对EOG相关信号进行ICA伊卡后，识别并剔除与EOG相关的独立成分。最后，通过伊卡和MEMD的逆变换，重构出纯净的脑电信号。仿真和真实的数据的实验结果表明，该方法能够有效地消除脑电信号中的EOA，并在很小的损失下保留有用的脑电信号信息。通过与现有方法的比较，该方法在去除EOA后信噪比的提高和均方误差的减小方面都取得了很大的改善。
The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.