Modeling spatiotemporal noise covariance for MEG/EEG source analysis

Abstract

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.