Study on Applying Independent Component Analysis to Remove Blink Artifacts and Power Noise in EEG WAN Bai-kun,ZHU Xin,YANG Chun-mei,GAO-Yang

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Study on Applying Independent Component Analysis to Remove Blink Artifacts and Power Noise in EEG WAN Bai-kun,ZHU Xin,YANG Chun-mei,GAO-Yang

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- Study on Applying Independent Component Analysis to Remove Blink Artifacts and Power Noise in EEG WAN Bai-kun,ZHU Xin,YANG Chun-mei,GAO-Yang
- Acta Electronica Sinica Vol. 31, Issue 10, Pages: 1571-1574(2003)
- 作者机构：
  
  天津大学生物医学工程与科学仪器系,天津,300072
- 作者简介：
- 基金信息：
- DOI：
  CLC： TH772+.2
- Published Online：25 October 2003，
  
  Published：2003
- 稿件说明：
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Study on Applying Independent Component Analysis to Remove Blink Artifacts and Power Noise in EEG WAN Bai-kun,ZHU Xin,YANG Chun-mei,GAO-Yang[J]. Acta Electronica Sinica, 2003, 31(10): 1571-1574.
DOI：

Study on Applying Independent Component Analysis to Remove Blink Artifacts and Power Noise in EEG WAN Bai-kun,ZHU Xin,YANG Chun-mei,GAO-Yang[J]. Acta Electronica Sinica, 2003, 31(10): 1571-1574. DOI：

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(Independent Component Analysis

ICA)方法分离EEG中此类噪声.通过对早老性痴呆症(Alzheimer disease

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(Extended Infomax)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(approximate entropy

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.

Abstract

Blink artifacts and power noise are constantly found:to strongly iufluence 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 -40dB).Meanwhile

it proved by calculating approximation entropy (ApEn) that ICA algorithms could preserve the nonlinear characteristics of EEG after removing the interference.

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