Localization of brain electrical activity via linearly constrained minimum variance spatial filtering
Localization of brain electrical activity via linearly constrained minimum variance spatial filtering
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DOI：
10.1109/10.623056
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发表时间：
1997-09-01
影响因子：
4.6
通讯作者：
Suzuki, A
中科院分区：
文献类型：
--
作者：
VanVeen, BD;vanDrongelen, W;Suzuki, A
描述和分析了一种从地面记录中定位脑电活动来源的空间滤波方法。空间过滤器被实现为在不同站点记录的数据的加权和。权重的选择是为了使线性约束下的滤波器输出功率最小化。线性约束迫使过滤器传递来自指定位置的脑电活动，而功率最小化衰减源自其他位置的活动，作为位置的函数的估计输出功率被作为位置的函数的估计噪声功率归一化，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。该方法不需要任何关于其几何形状的有源源的数量的先验假设，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和分析，并探讨了它对实际数据模型和假设数据模型之间偏差的敏感度。讨论了协方差矩阵估计算法、信源之间的相关性以及参照物的选择对算法的影响。通过仿真和实测数据验证了该方法的有效性。
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.