文献类型：期刊文献

中文题名：基于AR-Copula与ICA的脑电噪声检测和去除

英文题名：Automatic Artifact Detection and Elimination for EEG Signal Based on AR-Copula and ICA

作者：施伟俊[1];王蓓[1];卿湘运[1];王行愚[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2015

卷号：41

期号：5

起止页码：659

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；

基金：国家自然科学基金(61074113)

语种：中文

中文关键词：脑电信号;眼电信号干扰;AR时序模型;Copula理论;尾部相关性

外文关键词：EEG; EOG artifact; AR time series model; Copula theory; tail dependence

摘要：在脑电信号的实时采集过程中,噪声伪迹会对采集到的脑电信号产生较大的畸变。利用Copula理论结合AR时间序列模型研究脑电信号与引起其畸变的噪声之间的相关性,设计并实现了基于尾部相关性的脑电噪声自动检测算法。根据检测结果,对受干扰的数据段进行了ICA噪声去除处理。本文方法能够自动检测受干扰影响的数据段,并且在很大程度上减少了ICA算法的迭代次数,提高了数据实时处理的效率,适用于脑电信号的实时处理过程。
During the acquisition process, EEG （Electroencephalograph） is inevitably contaminated by various artifacts. In this paper, an AR-Copula model is employed to analyze the correlation between the contaminated EEG and the related artifacts, and a tail-dependence-based automation detection algorithm is proposed. According to the detection results, ICA is adopted to eliminate the artifacts for the contaminated EEG data segments. It is shown that the proposed algorithm can automatically detect the contaminated data segments and remarkably reduce the iteration numbers of ICA algorithm. Moreover, the efficiency of data processing can be also improved for real-time application.