Robust removal of ocular artifacts by combining Independent Component Analysis and system identification
Robust removal of ocular artifacts by combining Independent Component Analysis and system identification
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DOI：
10.1016/j.bspc.2013.10.006
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发表时间：
2014-03
期刊：
影响因子：
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通讯作者：
Zhenyu Wang;Peng Xu;Tiejun Liu;Yin Tian;Xu Lei;D. Yao
中科院分区：
文献类型：
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作者：
Zhenyu Wang;Peng Xu;Tiejun Liu;Yin Tian;Xu Lei;D. Yao
眼活动是脑电图记录中伪影的主要来源之一，但眼活动会严重扭曲脑电图记录。如何在不丢失有用脑电图信息的前提下尽可能彻底地去除眼部伪影是一个有待解决的问题。在实际应用中，独立分量分析（ICA）已成为矫正眼伪影的一种方法。然而，当脑电源和眼源不能用不同的独立分量（ic）表示时，基于ICA的方法可能会过度或不足地去除伪影。本文提出了一种结合ICA和自回归外源（ARX） （ICA-ARX）的新方法，用于更鲁棒地去除眼部伪像。在该方法中，为了降低ICA带来的负面影响，基于ICA校正后的信号和污染时段前选取的参考脑电图，利用ARX对每个通道建立多模型，然后选择最优模型进行进一步的伪影去除。模拟信号和实际脑电图记录的结果表明，该方法对去除眼部伪影是有效的，具有应用于脑电图相关研究的潜力。
Eye activity is one of the main sources of artifacts in electroencephalogram (EEG) recordings, however, the ocular artifact can seriously distort the EEG recordings. It is an open issue to remove the ocular artifact as completely as possible without losing the useful EEG information. Independent Component Analysis (ICA) has been one of the correction approaches to correct the ocular artifact in practice. However, ICA based approach may overly or less remove the artifacts when the EEG sources and ocular sources cannot be represented in different independent components (ICs). In this paper, a new approach combining ICA and Auto-Regressive eXogenous (ARX) (ICA-ARX) is proposed for a more robust removal of ocular artifact. In the proposed approach, to lower the negative effect induced by ICA, ARX is used to build the multi-models based on the ICA corrected signals and the reference EEG selected before contamination period for each channel, and then the optimal model will be selected for further artifact removal. The results applied to both the simulated signals and actual EEG recordings demonstrate the effectiveness of the proposed approach for ocular artifact removal, and its potential to be used in the EEG related studies.