Abstract
We present maximum likelihood (ML) methods for estimating evoked dipole responses using electroencephalography (EEG) and magnetoencephalography (MEG) arrays, which allow for spatially correlated noise between sensors with unknown covariance. The electric source is modeled as a collection of current dipoles at fixed locations and the head as a spherical conductor.We permit correlation between the dipoles' moments. The dipoles' locations and moments are estimated. We also propose ML-based methods for scanning the brain response data, which can be used for imaging the brain's electromagnetic activity.
