Berg P, Scherg M
Dept. of Psychology, University of Konstanz, FRG.
Electroencephalogr Clin Neurophysiol. 1994 Mar;90(3):229-41. doi: 10.1016/0013-4694(94)90094-9.
Previously published methods correct eye artifacts by subtracting proportions of the EOG from EEG electrodes. The implicit assumption made by these methods is that the EOG signals are a good measure of eye activity and contain no EEG. In this paper a new multiple source eye correction (MSEC) method of eye artifact treatment based on multiple source analysis is presented, which incorporates a model of brain activity. An accurate, head model-independent estimate of the spatial distribution of eye activity can be obtained empirically from calibration data containing systematic eye movements and blinks. Using the resulting spatial vectors together with the brain model, eye activity in EEG and event-related response data can be estimated in the presence of overlapping brain activity and corrected. A consequence of the MSEC approach is that data at EOG electrodes can be included in analyses of brain activity. In addition, by suitable selection of the spatial vectors, the eye activity can be split into signals which identify vertical and horizontal movements and eyeblinks. Using auditory ERP data sets with and without large eye artifacts, the MSEC method is compared with a "traditional" method in which brain activity is not modelled, particularly with respect to the spatial distribution of the corrected EEG. Traditional eye correction methods are shown to alter the spatial distribution of the EEG, resulting, for example, in changes in location and orientation of modelled equivalent sources. Such distortion is much reduced in the MSEC method, thus enhancing the precision of topographical EEG analyses.
先前发表的方法通过从脑电图电极中减去眼电图（EOG）的比例来校正眼部伪迹。这些方法隐含的假设是，EOG信号是眼部活动的良好度量，且不包含脑电图信号。本文提出了一种基于多源分析的眼部伪迹处理新方法——多源眼校正（MSEC）方法，该方法纳入了脑活动模型。通过包含系统性眼球运动和眨眼的校准数据，可以凭经验获得与头部模型无关的眼部活动空间分布的准确估计。将得到的空间向量与脑模型一起使用，可以在存在重叠脑活动的情况下估计脑电图和事件相关响应数据中的眼部活动并进行校正。MSEC方法的一个结果是，EOG电极处的数据可以纳入脑活动分析。此外，通过适当选择空间向量，可以将眼部活动分解为识别垂直和水平运动以及眨眼的信号。使用有无大量眼部伪迹的听觉事件相关电位（ERP）数据集，将MSEC方法与未对脑活动进行建模的“传统”方法进行比较，特别是在校正后的脑电图的空间分布方面。结果表明，传统的眼部校正方法会改变脑电图的空间分布，例如导致建模等效源的位置和方向发生变化。在MSEC方法中，这种失真大大减少，从而提高了脑电图地形分析的精度。
