Automatic Removal of Eye-Movement and Blink Artifacts from EEG Signals
Automatic Removal of Eye-Movement and Blink Artifacts from EEG Signals
复制标题
自动去除脑电图信号中的眼球运动和眨眼伪影
DOI：
10.1007/s10548-009-0131-4
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发表时间：
2010-03-01
期刊：
影响因子：
2.7
通讯作者：
Zheng, Chong Xun
中科院分区：
文献类型：
--
作者：
Gao, Jun Feng;Yang, Yong;Zheng, Chong Xun
眼电图伪影的频繁出现给脑电图的解释和分析带来了严重的问题。本文提出了一种鲁棒的自动消除脑电信号中眼球运动和眨眼伪影的方法。独立分量分析（ICA）是一种将脑电信号分解成独立分量的方法。然后，提取这些成分的地形特征和功率谱密度特征来识别眼动伪影成分，并采用支持向量机（SVM）分类器，因为它比其他几种分类器具有更高的性能。分类结果表明，特征提取方法不适合识别眨眼伪影成分，在此基础上提出了一种新的独立分量峰值检测算法（pdic）来识别眨眼伪影成分。最后，通过对去除伪影前后的脑电图数据进行对比，对所提出的伪影去除方法进行评价。结果表明，该方法能有效地去除脑电信号中的伪影，且对脑电信号失真较小。
Frequent occurrence of electrooculography (EOG) artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In this paper, a robust method is presented to automatically eliminate eye-movement and eye-blink artifacts from EEG signals. Independent Component Analysis (ICA) is used to decompose EEG signals into independent components. Moreover, the features of topographies and power spectral densities of those components are extracted to identify eye-movement artifact components, and a support vector machine (SVM) classifier is adopted because it has higher performance than several other classifiers. The classification results show that feature-extraction methods are unsuitable for identifying eye-blink artifact components, and then a novel peak detection algorithm of independent component (PDAIC) is proposed to identify eye-blink artifact components. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove EOG artifacts effectively from EEG signals with little distortion of the underlying brain signals.