一种结合自适应噪声完备经验模态分解和盲反卷积去除脑电中眼电伪迹的新方法

A New Method for Electrooculography Artifact Automatic Removal Based on CEEMDAN and BD in EEG Signals

针对微弱的脑电(Electroencephalogram,EEG)信号在采集过程中夹杂着各种生理伪迹,特别易遭到眨眼和眼动产生的眼电(Electrooculography,EOG)伪迹干扰。本文提出在自适应噪声完备经验模态分解(Complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)的基础上,构建盲反卷积(Blind deconvolution,BD)模型,实现EOG伪迹分离的方法。该方法首先运用CEEMDAN方法将含有伪迹的EEG信号分解成若干固有模态函数(Intrinsic mode function,IMF)分量,再以模态分量为观测信号送入EEG信号和EOG伪迹两个源信号构成的盲反卷积模型中,通过构建代价函数迭代实现EEG信号与EOG伪迹分离。为了验证新提出的算法,采用标准CHB⁃MIT头皮脑电数据库进行实验验证,EOG伪迹分离后的数据跟原始脑电数据作相关性分析,其相关系数是0.82。结果证实本文提出的方法保留有大多数原始EEG信号分量,同时对EOG伪迹的分离也具有良好的效果。

Due to the weak electroencephalogram(EEG)signal during the acquisition process,the EEG is mixed with various physiological artifacts,so it is particularly susceptible to electrooculography(EOG)interference caused by eye blinking and eye movement.A method for constructing a blind deconvolution(BD)model based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)is proposed to achieve EOG artifact separation.Firstly,the CEEMDAN method is used to decompose the EEG signal containing artifacts into several intrinsic mode functions(IMF).Secondly,the modal component is used as the observation signal to send the EEG signal and the EOG artifacts to form a BD model.Finally,the separation of EEG signal and EOG artifacts is realized by constructing the cost function iteratively.To verify the proposed algorithm,the standard Children’s Hospital Boston(CHB)and the Massachusetts Institute of Technology(MIT)(CHB-MIT)scalp EEG database is used for experimental verification.The correlation between the EOG artifact separation data and the original EEG data is analyzed,and the correlation coefficient is 0.82.The results confirm that this method retains most of the original EEG signal components and has a good effect on the separation of EOG artifacts.