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

We present a maximum likelihood (ML) method for estimating evoked dipole responses using electroencephalography (EEC) and magnetoencephalography (MEG) arrays, which allows for spatially correlated noise between sensors with unknown covariance. The electric source is modeled as a collection of current dipoles at flxed 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 nonparametric basis functions. We estimate the dipoles' locations and moments, and derive the Fisher information matrix for the unknown parameters. We also propose an ML-based method for scanning the brain response data, which can be used to initialize the multidimensional search required to obtain the true dipole location estimates. A goodness-of-flt measure accounting for multiple time snapshots and correlated noise is introduced. We present numerical examples of both simulated and real data to demonstrate the performance of the proposed method.