Estimation and model selection for an IDE-based spatio-temporal model
IEEE Transactions on Signal Processing
Iterative adaptive Kronecker MIMO radar beamformer: description and convergence analysis
IEEE Transactions on Signal Processing
Separable linear discriminant analysis
Computational Statistics & Data Analysis
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The stationary dipole model for the inverse problem of magnetoencephalographic (MEG) and electroencephalographic (EEG) data is extended by including spatio-temporal correlations of the background noise. For that purpose, the spatio-temporal covariances are described as a Kronkecker product of a spatial and a temporal covariance matrix. The maximum likelihood method is used to estimate this Kronecker product from a series of trials of MEG/EEG data. A simulation study shows that the inclusion of the background noise generally improves the dipole estimate substantially. When the frequency of the source time functions, however, coincides with the frequency contents of the covariance function, the dipole estimate worsens when the temporal correlations are included. The inclusion of spatial correlations always improves the estimates