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计算机工程

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基于EEMD与改进提升小波的脑电信号消噪方法

孟明,鲁少娜,马玉良   

  1. (杭州电子科技大学智能控制与机器人研究所,杭州 310018)
  • 收稿日期:2015-03-09 出版日期:2016-04-15 发布日期:2016-04-15
  • 作者简介:孟明(1975-),男,副教授、博士,主研方向为智能控制、模式识别;鲁少娜,硕士;马玉良,副研究员、博士后。
  • 基金资助:
    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)。

Electroencephalogram De-noising Method Based on EEMD and Improved Lifting Wavelet

MENG Ming,LU Shaona,MA Yuliang   

  1. (Institute of Intelligent Control and Robot,Hangzhou Dianzi University,Hangzhou 310018,China)
  • Received:2015-03-09 Online:2016-04-15 Published:2016-04-15

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

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