基于带参考信号的ICA算法的脑电信号眨眼伪差的分离研究  

Removing Blinking Artifacts from EEG Based on ICA Algorithm with Reference Signals

作　　者：李婷[1] 邱天爽[1] 

机构地区：[1]大连理工大学电子与信息工程学院

出　　处：《中国生物医学工程学报》2006年第3期296-299,309,共5页Chinese Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(30170259;60172072;60372081;30570475);辽宁省科学技术基金资助项目(2001101057)。

摘　　要：独立分量分析(ICA)是一种从混合信号中提取统计独立的分量的一种方法。本研究提出了一种基于带参考信号的ICA算法的脑电信号眨眼伪差的分离方法,可以得到纯净的脑电信号。这个方法的主要思路是:先选取一导眨眼伪差比较明显的数据,从中获得眨眼伪差的参考信号,再用ICA方法把眨眼伪差第一个提取出来,最后得到消除伪差后的EEG信号。详细讨论了使用带参考信号的ICA算法消除眨眼伪差的方法与步骤,并给出了应用于真实信号的实验结果。Independent component analysis （ICA） is a technique which extracts statistically independent components from a set of measured signals. Based on ICA algorithm with reference signals, a method of removing blinking artifacts was proposed in this paper. The core idea of the method included： first select one channel of EEG with obvious blinking artifacts, and obtain reference signals from it, then extract the blinking artifact first with ICA algorithm, and at last get pure EEG signals. The idea and steps of the ICA algorithm with reference signals were discussed, and the results of processing real signals were given in the paper.

关 键 词：独立分量分析 脑电信号(EEG) 眨眼伪差分离 参考信号 

分 类 号：R318[医药卫生—生物医学工程]