Jung T P, Makeig S, Westerfield M, Townsend J, Courchesne E, Sejnowski T J
University of California, San Diego, La Jolla, CA 92093, USA.
Clin Neurophysiol. 2000 Oct;111(10):1745-58. doi: 10.1016/s1388-2457(00)00386-2.
Electrical potentials produced by blinks and eye movements present serious problems for electroencephalographic (EEG) and event-related potential (ERP) data interpretation and analysis, particularly for analysis of data from some clinical populations. Often, all epochs contaminated by large eye artifacts are rejected as unusable, though this may prove unacceptable when blinks and eye movements occur frequently.
Frontal channels are often used as reference signals to regress out eye artifacts, but inevitably portions of relevant EEG signals also appearing in EOG channels are thereby eliminated or mixed into other scalp channels. A generally applicable adaptive method for removing artifacts from EEG records based on blind source separation by independent component analysis (ICA) (Neural Computation 7 (1995) 1129; Neural Computation 10(8) (1998) 2103; Neural Computation 11(2) (1999) 606) overcomes these limitations.
Results on EEG data collected from 28 normal controls and 22 clinical subjects performing a visual selective attention task show that ICA can be used to effectively detect, separate and remove ocular artifacts from even strongly contaminated EEG recordings. The results compare favorably to those obtained using rejection or regression methods.
The ICA method can preserve ERP contributions from all of the recorded trials and all the recorded data channels, even when none of the single trials are artifact-free.
眨眼和眼球运动产生的电势给脑电图（EEG）和事件相关电位（ERP）的数据解释与分析带来了严重问题，尤其是对一些临床人群的数据进行分析时。通常，所有被大眼电伪迹污染的时段都会被当作不可用而剔除，不过当眨眼和眼球运动频繁发生时，这样做可能并不合适。
额部通道常被用作参考信号来回归去除眼电伪迹，但不可避免地，EOG通道中也出现的部分相关EEG信号会因此被消除或混入其他头皮通道。一种基于独立成分分析（ICA）进行盲源分离的、普遍适用的从EEG记录中去除伪迹的自适应方法（《神经计算》7（1995）1129；《神经计算》10(8)（1998）2103；《神经计算》11(2)（1999）606）克服了这些局限性。
对28名正常对照者和22名临床受试者在执行视觉选择性注意任务时收集的EEG数据的研究结果表明，即使是对被严重污染的EEG记录，ICA也可用于有效检测、分离和去除眼电伪迹。结果优于使用剔除或回归方法所获得的结果。
ICA方法能够保留所有记录试验和所有记录数据通道的ERP成分，即使没有一个单次试验是无伪迹的。
