Removing electroencephalographic artifacts by blind source separation
Removing electroencephalographic artifacts by blind source separation
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DOI：
10.1111/1469-8986.3720163
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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
当拒绝受污染的EEG段导致不可接受的数据丢失时，眼球运动、眨眼、心脏信号、肌肉噪声和线噪声对脑电图（EEG）解释和分析提出了严重的问题。已经提出了许多方法来去除EEG记录中的伪影，特别是那些由眼球运动和眨眼引起的伪影。通常在并行EEG和眼电图（EOG）记录上执行时域或频域中的回归，以导出表征EEG通道中EOG伪影的出现和扩散的参数。由于EEG和眼部活动双向混合，因此回归出眼部伪影不可避免地还涉及从每个记录中减去相关EEG信号。当一个好的回归通道对于每个伪影源不可用时，回归方法变得更有问题，如在肌肉伪影的情况下。使用主成分分析（PCA）已被提出来消除眼睛的伪影从多通道EEG。然而，PCA不能完全将眼睛伪影与大脑信号分开，特别是当它们具有可比的幅度时。在这里，我们提出了一种基于独立分量分析（伊卡）盲源分离的新的且普遍适用的方法，用于从EEG记录中去除各种伪影。我们的研究结果表明，从正常和自闭症受试者收集的EEG数据伊卡可以有效地检测，分离，并从各种各样的人为来源的EEG记录中去除污染的结果相比，使用回归和PCA方法获得的。伊卡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.