Extended ICA Removes Artifacts from Electroencephalographic Recordings
Extended ICA Removes Artifacts from Electroencephalographic Recordings
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发表时间：
1997-12
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通讯作者：
T. Jung;Colin J. Humphries;Te-Won Lee;S. Makeig;M. McKeown;V. Iragui;T. Sejnowski
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作者：
T. Jung;Colin J. Humphries;Te-Won Lee;S. Makeig;M. McKeown;V. Iragui;T. Sejnowski
眼球运动、眨眼、肌肉、心脏和直线噪声对脑电活动的严重污染是脑电解释和分析的一个严重问题。拒绝受污染的脑电片段会导致相当大的信息损失，对于临床数据来说可能是不切实际的。已经提出了许多方法来去除EEG记录中的眼动和眨眼伪影。通常对同时的EEG和EOG(EOG)记录执行时间或频率域中的回归，以得出表征EOG伪影在EEG通道中的出现和扩散的参数。然而，EOG记录也包含大脑信号[1，2]，因此EOG活动的回归不可避免地涉及从每个记录中减去相关EEG信号的一部分。回归不能用于去除肌肉噪声或线噪声，因为它们没有参考通道。在这里，我们提出了一种新的、普遍适用的方法来去除脑电记录中的各种伪影。该方法基于先前的独立分量分析(ICA)算法的扩展版本[3，4]，该算法用于对具有亚高斯或超高斯分布的独立源信号的线性混合执行盲源分离。实验结果表明，ICA能够有效地检测、分离和去除各种伪像来源的脑电记录中的活动，其结果与基于回归的方法相比是有利的。
Severe contamination of electroencephalographic (EEG) activity by eye movements, blinks, muscle, heart and line noise is a serious problem for EEG interpretation and analysis. Rejecting contaminated EEG segments results in a considerable loss of information and may be impractical for clinical data. Many methods have been proposed to remove eye movement and blink artifacts from EEG recordings. Often regression in the time or frequency domain is performed on simultaneous EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. However, EOG records also contain brain signals [1, 2], so regressing out EOG activity inevitably involves subtracting a portion of the relevant EEG signal from each recording as well. Regression cannot be used to remove muscle noise or line noise, since these have no reference channels. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records. The method is based on an extended version of a previous Independent Component Analysis (ICA) algorithm [3, 4] for performing blind source separation on linear mixtures of independent source signals with either sub-Gaussian or super-Gaussian distributions. Our results show that ICA can effectively detect, separate and remove activity in EEG records from a wide variety of artifactual sources, with results comparing favorably to those obtained using regression-based methods.