Research on Removing Ocular Artifact Automatically From EEG Signals

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

doi:10.3969/j.issn.0372-2112.2013.06.026

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Research on Removing Ocular Artifact Automatically From EEG Signals

更新时间：2025-07-16

- Research on Removing Ocular Artifact Automatically From EEG Signals
- Acta Electronica Sinica Vol. 41, Issue 6, Pages: 1207-1213(2013)
- 作者机构：
  
  北京工业大学电子信息与控制工程学院,北京,100124
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2013.06.026
  CLC： R318
- Published：2013
- 稿件说明：
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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.
DOI：

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. DOI： 10.3969/j.issn.0372-2112.2013.06.026.

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.

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.

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