  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.
