Spatiotemporal EEG/MEG source analysis based on a parametric-noise covariance model
Spatiotemporal EEG/MEG source analysis based on a parametric-noise covariance model
复制标题
DOI：
10.1109/tbme.2002.1001967
复制
发表时间：
2002-06-01
影响因子：
4.6
通讯作者：
Grasman, RPPP
中科院分区：
文献类型：
--
作者：
Huizenga, HM;de Munck, JC;Grasman, RPPP
介绍了一种将时空噪声协方差矩阵纳入时空噪声源分析的方法。其基本特征是将估计问题分为两个部分。首先，对观测到的噪声协方差矩阵进行拟合。这个模型是空间矩阵和时间矩阵的克罗内克积。空间矩阵通过依赖于传感器距离的函数来模拟空间协方差。时间矩阵将时间协方差建模为滞后相关的。在第二部分中，给出该噪声模型估计源，由于Kronecker公式，这可以非常有效地完成。对实际脑电图数据的应用表明，噪声模型能很好地拟合数据。仿真结果表明，所得到的源估计比忽略噪声协方差的标准分析得到的估计更精确。此外，源参数估计的估计标准误差比标准分析得到的估计标准误差精确得多。最后，利用源参数标准误差分析了时间采样的影响。结果表明，将采样量增加一个因子x，所有源参数的标准误差都以x的平方根减小。
A method is described to incorporate the spatiotemporal noise covariance matrix into a spatiotemporal source analysis. The essential feature is that the estimation problem is split into two parts. First, a model is fitted to the observed noise covariance matrix. This model is a Kronecker product of a spatial and a temporal matrix. The spatial matrix models the spatial covariances by a function dependent on sensor distance. The temporal matrix models the temporal covariances as lag dependent. In the second part, sources are estimated given this noise model, which can be done very efficiently due to the Kronecker formulation.An application to real electroencephalogram (EEG) data shows that the noise model fits the data very well. Simulation results show that the resulting source estimates are more precise than those obtained from a standard analysis neglecting the noise covariance. In addition, the estimated standard errors of the source parameter estimates are far more precise than those obtained from a standard analysis. Finally, the source parameter standard errors are used to investigate the effects of temporal sampling. It is shown that increasing the sampling by a factor x, decreases the standard errors of all source parameters with the square root of x.