北京生物醫(yī)學(xué)工程

基于ＫＩＣＡ和ＭＡＭＡ?ＥＭＤ的眼電偽跡去除方法

An EOG artifact removal method based on KICA and MAMA-EMD

作者：張圓圓孫炎珺李明愛

單位：北京工業(yè)大學(xué)信息學(xué)部(北京 100124), <br />計(jì)算智能與智能系統(tǒng)北京市重點(diǎn)實(shí)驗(yàn)室(北京 100124）<br />,數(shù)字社區(qū)教育部工程研究中心(北京 100124), <br />通信作者：李明愛。E-mail: [email protected]

關(guān)鍵詞：腦電信號(hào); 眼電偽跡; 核獨(dú)立分量分析; 模態(tài)分裂；掩膜最小弧長經(jīng)驗(yàn)?zāi)B(tài)分解

分類號(hào)：R318.04  

出版年·卷·期（頁碼）：2022·41·4（360-367）

摘要：

目的為了準(zhǔn)確地分離并去除腦電(EEG)信號(hào)中的尖峰狀眼電(EOG)偽跡，本文提出一種基于核獨(dú)立分量分析(kernel independent component analysis, KICA)和掩膜最小弧長經(jīng)驗(yàn)?zāi)B(tài)分解(masking-aided minimum arclength empirical mode decomposition, MAMA-EMD)的眼電偽跡去除方法，即KICMME。方法首先，使用KICA將多通道受污染的EEG信號(hào)分離為多個(gè)獨(dú)立分量(independent components, ICs)；然后，計(jì)算每個(gè)IC的峰度值，確定與EOG相關(guān)的IC，并利用MAMA-EMD算法對其進(jìn)一步分解，得到一組固有模態(tài)函數(shù)(intrinsic mode function, IMF)；進(jìn)而，通過計(jì)算各IMF的低頻功率占比，識(shí)別并剔除與EOG相關(guān)性高的IMF；最后，基于MAMA-EMD和KICA的逆變換重構(gòu)出“純凈”EEG信號(hào)。結(jié)果在半模擬和真實(shí)腦電兩個(gè)數(shù)據(jù)集上進(jìn)行實(shí)驗(yàn)研究，KICMME取得的均方誤差和信噪比分別為0.82 〖μV〗^2和12.51 dB，獲得的分類準(zhǔn)確率和Kappa值分別為91%和0.83。結(jié)論 MAMA-EMD能夠準(zhǔn)確地分離出與EOG相關(guān)的IMF分量，使得KICMME可以在保留有用神經(jīng)信息的同時(shí)，最大限度地去除EEG信號(hào)中的EOG偽跡，相對現(xiàn)有基于盲源分離的眼電偽跡去除方法具有明顯優(yōu)勢。

Objective In order to isolate and remove the spike-like electrooculogram (EOG) artifacts from EEG signals accurately, this paper proposed a novel method based on kernel independent component analysis (KICA) and masking-aided minimum arclength empirical mode decomposition (MAMA-EMD), namely KICMME, for the removal of EOG artifacts. Methods Firstly, the multichannel contaminated EEG signals were separated by KICA into several independent components (ICs). Secondly, the kurtosis value of each IC was calculated to detect EOG-related IC, and MAMA-EMD algorithm was used to decompose it further to obtain a set of intrinsic mode functions (IMFs). Furthermore, the IMFs linked with EOG were identified and eliminated by calculating the low-frequency power proportion of each IMF. Finally, the ‘clean’ EEG was reconstructed by performing the inverse transform of MAMA-EMD and KICA. Results Based on the experimental research of semi-simulated and real EEG data, KICMME achieved a mean square error (MSE) of 0.82 〖μV〗^2and a signal-to-noise ratio (SNR) of 12.51 dB, and the classification accuracy and Kappa values were 91% and 0.83 respectively. Conclusions MAMA-EMD can accurately isolate the IMF component associated with EOG, and KICMME can remove EOG artifacts from EEG signals to the maximum extent while retaining useful neural information, which has great improvement compared with the existing EOG artifact removal methods based on blind source separation.

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