Removing electroencephalographic artifacts by blind source separation
Removing electroencephalographic artifacts by blind source separation
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DOI：
10.1017/s0048577200980259
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发表时间：
2000-03-01
期刊：
影响因子：
3.7
通讯作者：
Sejnowski, TJ
中科院分区：
文献类型：
--
作者：
Jung, TP;Makeig, S;Sejnowski, TJ
眼球运动、眨眼、心脏信号、肌肉噪声和线路噪声是脑电图（EEG）解释和分析的严重问题，当拒绝污染的脑电图片段导致不可接受的数据丢失时。人们提出了许多方法来去除脑电图记录中的伪影，特别是由眼球运动和眨眼引起的伪影。通常对并行脑电图和眼电（EOG）记录进行时域或频域回归，以获得表征EEG通道中EOG伪影的外观和分布的参数。由于脑电图和眼活动是双向混合的，因此对眼伪影的回归也不可避免地涉及到从每个记录中减去相关的脑电图信号。当一个好的回归通道不能用于每个伪源时，回归方法变得更加成问题，就像肌肉伪源的情况一样。提出了利用主成分分析（PCA）去除多通道脑电图中的眼部伪影。然而，PCA不能完全将眼睛伪影从大脑信号中分离出来，特别是当它们具有可比的振幅时。在此，我们提出了一种基于独立分量分析（ICA）的盲源分离方法来去除EEG记录中各种各样的伪影。我们对正常和自闭症受试者的脑电图数据的研究结果表明，ICA可以有效地检测、分离和去除脑电图记录中各种人工来源的污染，其结果优于使用回归和PCA方法获得的结果。ICA也可以用来分析眨眼相关的大脑活动。
Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.