A Novel Automatic Recognition and Removal Method of Ocular Artifacts

LI Ming-ai; GUO Shuo-da; TIAN Xiao-xia; YANG Jin-fu; HAO Dong-mei

doi:10.3969/j.issn.0372-2112.2016.05.004

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A Novel Automatic Recognition and Removal Method of Ocular Artifacts

更新时间：2025-07-16

- A Novel Automatic Recognition and Removal Method of Ocular Artifacts
- Acta Electronica Sinica Vol. 44, Issue 5, Pages: 1032-1039(2016)
- 作者机构：
  
  1. 计算智能与智能系统北京市重点实验室,北京,100124
  2. 北京工业大学电子信息与控制工程学院,北京,100124
  4. 北京工业大学生命科学与生物工程学院,北京,100124
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2016.05.004
  CLC： R318
- Published Online：25 May 2016，
  
  Published：2016
- 稿件说明：
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LI Ming-ai, GUO Shuo-da, TIAN Xiao-xia, et al. A Novel Automatic Recognition and Removal Method of Ocular Artifacts[J]. Acta Electronica Sinica, 2016, 44(5): 1032-1039.
DOI：

LI Ming-ai, GUO Shuo-da, TIAN Xiao-xia, et al. A Novel Automatic Recognition and Removal Method of Ocular Artifacts[J]. Acta Electronica Sinica, 2016, 44(5): 1032-1039. DOI： 10.3969/j.issn.0372-2112.2016.05.004.

摘要

为了改善脑电中的眼电伪迹过估计问题及环境干扰耦合引起的非线性混合对眼电去除效果的影响

提出一种基于快速核独立成分分析(Fast Kernel Independent Component Analysis

FastKICA)与离散小波变换(Discrete Wavelet Transform

DWT)的眼电自动去除方法

即(Fast Kernel Independent Wavelet Transform

FKIWT)方法.首先

利用FastKICA方法对脑电信号进行分离得到独立成分

并以相关系数为依据识别出眼电伪迹;进而

基于DWT对眼电伪迹进行多分辨率分析

将逼近分量置零

而细节分量保持不变

使得重构所得眼电伪迹成分保留更多有用脑电信号;最后

利用FastKICA逆变换重建眼电去除后的脑电信号.实验结果表明:FKIWT不仅有效改善了眼电过估计问题

增强了抗干扰能力和鲁棒性

而且在线性混合和非线性混合情况下

均得到较好的伪迹去除效果

特别是在非线性混合时优势更为明显

适合于实际在线应用.

Abstract

In order to improve the overestimation of ocular artifacts (OA) in electroencephalogram (EEG) and the OA removal effect of nonlinear mixture caused by environmental interference coupling

a novel automatic removal method is proposed based on fast kernel independent component analysis (FastKICA) and discrete wavelet transform (DWT)

and it is denoted as FKIWT.The independent components are separated from the mixed EEG by using the FastKICA algorithm

and the correlation coefficient is applied to identify OA component;Then

the Multiresolution analysis of OA is achieved with DWT

the approximation wavelet coefficients are set to zero and the detail wavelet coefficients are not changed.So more useful EEG is remained in the reconstructed OA component;Furthermore

the clean EEG is restored with the inverse algorithm of FastKICA.The experimental results show that FKIWT can effectively improve the overestimation of OA and has perfect anti-interference ability and robustness.Meanwhile

the better effects of OA elimination are also obtained on the condition that the linear or nonlinear mixed model is adopted

and the latter's advantage is especially obvious.The FKIWT is suitable for on-line application.

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