基于小波变换与ICA结合的EP信号提取研究  

Independent wavelet transform and component analysis in EP signal extraction

作　　者：刘超[1] 林晓焕[1] 廖文[1] 高莹[1] Liu Chao Lin Xiaohuan Liao Wen Gao Ying(School of Electronics and Inform, Xi＇an Polytechnic University, Xi＇an 710048, China)

机构地区：[1]西安工程大学电子信息学院,西安710048

出　　处：《国外电子测量技术》2017年第9期35-39,共5页Foreign Electronic Measurement Technology

摘　　要：针对脑电信号非侵入采集造成被采集信号中含有较多高频噪声信号并且信号难以被干净分离的特点,设计一种将独立分量分析法(ICA)与小波变换法相结合的一种改进型算法,实现对已分离的脑电信号降噪提取作用。通过小波变换,滤除目标信号中的高频信号,将该信号重构为ICA算法的输入信号,克服独立分量分析法不能区分噪声的缺点。将两种方法结合提取脑电信号中诱发电位的提取,将小波包滤波后的信号重构为ICA的输入信号,有效的降低了噪声信号对EP信号的影响,在信源分离中取得了良好的效果。The EEG acquisition caused by non-invasive acquisition,which make the characteristics of signal contains more high frequency signal and the signal is difficult to separation cleanly,design a new method by combine component analysis（ICA）method with an improved algorithm combined with wavelet transform,to achieve the separation of EEG signal noising.The high-frequency signal in the target signal is filtered by Wavelet transform.And the output signal is reconstructed as the input signal of the ICA algorithm;overcome the independent component analysis method of genetic algorithm cannot distinguish the noise.This paper incorporates wavelet transform with ICA method based on genetic algorithm,and applicate them to evoked potentional extraction,the signal after wavelet packet filtering is reconstructed into the input signal of ICA,which get a good effect.

关 键 词：EP信号 独立分量分析 小波变换 信噪分离 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]