Estimating evoked dipole responses in unknown spatially correlated noise with EEG/MEG arrays

We present maximum likelihood (ML) methods for estimating evoked dipole res ponses using electroencephalography (EEG) and magnetoencephalography (MEG) arrays, which allow for spatially correlated noise between sensors with unk nown covariance. 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 linear combinat ions of parametric or nonparametric etc. basis functions. We estimate the d ipoles' locations and moments and derive the Cramer-Rao bound for the unkno wn parameters. We also propose an ML-based method for scanning the brain re sponse data, which can be used to initialize the multidimensional search re quired to obtain the true dipole location estimates. Numerical simulations demonstrate the performance of the proposed methods.