中文摘要：

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。

英文摘要：

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.