Localization of evoked electric sources and design of EEG/MEG sensor arrays

We present a maximum likelihood (ML) method for estimating evoked dipole responses using electroencephalography (EEG) and magnetoencephalography (MEG) arrays and discuss EEG/MEG sensor array design. The electric source is modeled as a collection of current dipoles at fixed locations and the head as a spherical conductor. We permit the dipoles´ moments to vary with time by modeling them as a linear combination of parametric or non-parametric basis functions and further discuss the case when the dipoles have fixed orientations in time. We estimate the dipoles´ locations and moments, and derive the Fisher information matrix for the unknown parameters. Finally, we propose an array optimization criterion based on minimizing the volume of the linearized confidence region