Localization of evoked electric sources and design of EEG/MEG sensor arrays
Aleksandar Dogandzic, Arye Nehorai · 2002
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.