ICA去除EEG中眼动伪差和工频干扰方法研究  

Study on Applying Independent Component Analysis to Remove Blink Artifacts and Power Noise in EEG

作　　者：万柏坤[1] 朱欣[1] 杨春梅[1] 高扬[1] 

机构地区：[1]天津大学生物医学工程与科学仪器系,天津300072

出　　处：《电子学报》2003年第10期1571-1574,共4页Acta Electronica Sinica

基　　金：天津市自然科学基金 (No .99360 751 1 ) ;天津市重点学科建设基金

摘　　要：眼动伪差和工频干扰是临床脑电图 (EEG)中常见噪声 ,严重影响其有用信息提取 .本文尝试采用独立分量分析 (IndependentComponentAnalysis,ICA)方法分离EEG中此类噪声 .通过对早老性痴呆症 (Alzheimerdisease,AD)患者临床EEG信号 (含眼动伪差和混入工频干扰 ,信噪比仅 0dB)作ICA分析 ,比较了最大熵 (Infomax)和扩展最大熵(ExtendedInfomax)ICA算法的分离效果 ,证实虽然最大熵算法可以分离出眼动慢波 ,但难以消除工频干扰 ,为此需采用扩展的最大熵算法 ;并知ICA方法在极低信噪比时也有较好的抗干扰性 ,且在处理非平稳信号时有好的鲁棒性 ;文中还结合近似熵 (approximateentropy ,ApEn)分析说明利用ICA去除干扰后有助于恢复和保持原始EEG信号的非线性特征 .研究结果表明ICA方法在生物医学信号处理中具有潜在的重要应用价值 ,值得深入研究和推广 .Blink artifacts and power noise are constantly found: to strongly influence the acquisition and analysis of EEG signals. In this paper, by comparing the efficiencies of two ICA algorithms-Infomax-ICA and Extended-Infomax-ICA methods in extracting blink artifacts and power noise in the EEG signals, it was shown that ICA algorithms were insensitive to disturbance in the conditions of low signal-noise-ratio, and ICA algorithms demonstrated a strong robustness in processing non-stationary signals. Though blink slow waves could be extracted by infomax algorithm, but power noise was unlikely to be removed by it. Therefore, Extended-Infomax ICA algorithm should be used. By applying Extended-Infomax algorithms, blink artifacts and power noise contained in the 16-channel EEG signals of Alzheimer-disease patients were removed successfully (the lowest signal-noise-ratio for power noise can be -40 dB). Meanwhile, it proved by calculating approximation entropy (ApEn) that ICA algorithms could preserve the nonlinear characteristics of EEG after removing the interference.

关 键 词：脑电(EEG) 眼动伪差 工频干扰 独立分量分析(ICA) 最大熵(Infomax) 

分 类 号：TH772.2[机械工程—仪器科学与技术]