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。
To eliminate the noise mixed in the Electroencephalogram （EEG）, the paper presents a kind of EEG denoising 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 1MF 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.
出处
《计算机工程》
CAS
CSCD
北大核心
2016年第4期313-317,共5页
Computer Engineering
基金
国家自然科学基金资助项目(61372023)
浙江省自然科学基金资助项目(Y14F030078)
关键词
脑电信号
经验模态分解
提升小波
自适应阈值
消噪
Electroencephalogram （EEG）
Empirical Mode Decomposition （EMD）
lifting wavelet
adaptive threshold
de-noising
