摘要： 为消除混杂在脑电信号中的噪声,提出一种总体平均经验模态分解(EEMD)与改进提升小波相结合的脑电信号消噪方法。利用EEMD算法将含噪脑电信号分解为若干个内蕴模式函数(IMF)分量,通过自相关函数特性法提取出由噪声主导的高频IMF分量,并运用改进提升小波进行消噪处理,将保留的低频IMF分量与消噪后的高频IMF分量进行叠加,从而得到消噪后的脑电信号。实验结果表明,与传统提升小波消噪方法、以及改进的提升小波消噪方法相比,该方法的信噪比较高,均方根误差较低。

关键词: 脑电信号, 经验模态分解, 提升小波, 自适应阈值, 消噪

Abstract: To eliminate the noise mixed in the Electroencephalogram(EEG),the paper presents a kind of EEG de-noising method based on Ensemble Empirical Mode Decomposition(EEMD) and improved lifting wavelet.Firstly,the noise-added EEG signals are decomposed into several Intrinsic Mode Function (IMF) components by EEMD.Secondly,the high-frequency IMF components dominated by the noise component are extracted through the properties of auto correlation function method,and de-noised by improved lifting wavelet.Finally,the high-frequency IMF components processed and low-frequency IMF components are reconstructed to get the de-noised signal.Experimental results show that this method has better Signal-to-noise Ratio(SNR) and smaller Root Mean Square Error(RMSE) compared with the traditional method and the improved lifting wavelet de-noising method.

Key words: Electroencephalogram(EEG), Empirical Mode Decomposition(EMD), lifting wavelet, adaptive threshold, de-noising

中图分类号: 

• TP391.4