用于MEG/EEG源分析的时空噪声协方差模型
Modeling spatiotemporal noise covariance for MEG/EEG source analysis
摘要：
我们提出了一种新的模型，用于近似时空噪声协方差用于MEG/EEG源分析。我们的模型是现有模型[1，2]的扩展，该模型使用一对矩阵的单个Kronecker乘积-时间和空间协方差;我们采用一系列Kronecker乘积来构建完整协方差的更好近似。与假设所有空间分量具有相同时间结构的单对模型相比，所提出的模型允许每个空间分量具有不同的独立时间过程。该模型更好地描述了空间和时间相关的背景活动。同时，该模型反演速度快，可用于反演分析。我们探索了该模型的两个版本。一种是基于背景的正交空间分量。另一种更一般的模型是基于独立的空间组件。新的和以前的模型的性能进行了比较，在逆解大量的单偶极子问题与模拟的时间过程和真实的脑磁图数据的背景。
英文摘要：
We propose a new model for approximating spatiotemporal noise covariance for use in MEG/EEG source analysis. Our model is an extension of an existing model [1,2] that uses a single Kronecker product of a pair of matrices - temporal and spatial covariance; we employ a series of Kronecker products in order to construct a better approximation of the full covariance. In contrast to the single-pair model that assumes the same temporal structure for all spatial components, the proposed model allows for distinct, independent time courses at each spatial component. This model better describes spatially and temporally correlated background activity. At the same time, inversion of the model is fast which makes it useful in the inverse analysis. We have explored two versions of the model. One is based on orthogonal spatial components of the background. The other, more general model, is based on independent spatial components. Performance of the new and previous models is compared in inverse solutions to a large number of single dipole problems with simulated time courses and background from authentic MEG data.
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