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The removal of blink and saccade artifact in EEG recordings by Independent Component Analysis

机译:通过独立分量分析去除脑电图记录中的眨眼和扫视伪像

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摘要

Pervasive electroencephalographic (EEG) artifacts are associated with eye movement. This study shows that Independent Component Analysis (ICA) using Joint Approximate Diagonalization of Eigenmatrice (JADE) algorithm can be applied to removing ocular artifacts in EEG. The nine channels of EEGs were recorded from three young healthy subjects with additional VEOG and HEOG channels in blinking and saccade conditions respectively. Seven EEG recordings were selected for artifacts removal tests with two EOG recordings indicating the possible interferences. Ocular artifacts for both conditions were successfully presented by an independent component, which was then eliminated to reconstruct the artifact-free EEGs. This study demonstrates that JADE algorithm can be an effective tool in correcting EOG interference with multichannel EEG recordings.
机译:普遍的脑电图(EEG)伪影与眼睛运动有关。这项研究表明,使用特征矩阵联合近似对角化(JADE)算法进行的独立成分分析(ICA)可用于去除EEG中的眼部伪影。记录了来自三名年轻健康受试者的9个脑电图通道,分别在眨眼和扫视条件下有另外的VEOG和HEOG通道。选择了七个EEG记录进行伪影去除测试,其中两个EOG记录表明可能存在干扰。两种情况下的眼部伪影均由一个独立的组件成功呈现,然后被消除以重建无伪影的脑电图。这项研究表明,JADE算法可以有效地纠正多通道EEG录音对EOG的干扰。

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