Van Veen B D, van Drongelen W, Yuchtman M, Suzuki A
Department of Electrical and Computer Engineering, University of Wisconsin, Madison 53706, USA.
IEEE Trans Biomed Eng. 1997 Sep;44(9):867-80. doi: 10.1109/10.623056.
A spatial filtering method for localizing sources of brain electrical activity from surface recordings is described and analyzed. The spatial filters are implemented as a weighted sum of the data recorded at different sites. The weights are chosen to minimize the filter output power subject to a linear constraint. The linear constraint forces the filter to pass brain electrical activity from a specified location, while the power minimization attenuates activity originating at other locations. The estimated output power as a function of location is normalized by the estimated noise power as a function of location to obtain a neural activity index map. Locations of source activity correspond to maxima in the neural activity index map. The method does not require any prior assumptions about the number of active sources of their geometry because it exploits the spatial covariance of the source electrical activity. This paper presents a development and analysis of the method and explores its sensitivity to deviations between actual and assumed data models. The effect on the algorithm of covariance matrix estimation, correlation between sources, and choice of reference is discussed. Simulated and measured data is used to illustrate the efficacy of the approach.
描述并分析了一种用于从表面记录中定位脑电活动源的空间滤波方法。空间滤波器通过对不同部位记录的数据进行加权求和来实现。权重的选择是为了在满足线性约束的条件下使滤波器输出功率最小化。线性约束迫使滤波器通过来自指定位置的脑电活动，而功率最小化则会衰减源自其他位置的活动。将作为位置函数的估计输出功率除以作为位置函数的估计噪声功率，以获得神经活动指数图。源活动的位置对应于神经活动指数图中的最大值。该方法不需要对活动源的数量及其几何形状做任何先验假设，因为它利用了源电活动的空间协方差。本文介绍了该方法的发展与分析，并探讨了其对实际数据模型与假设数据模型之间偏差的敏感性。讨论了协方差矩阵估计、源之间的相关性以及参考选择对算法的影响。使用模拟数据和实测数据来说明该方法的有效性。
