In this paper the maximum a posteriori (MAP) image reconstruction of magnetoencephalograms (MEG) is investigated. A mathematical framework for vector Markov random field models (MRF) suitable for MEG modeling of brain neuron current dipole activity is developed. A new method for simulating an MRF over a non-uniformly spaced sample grid while approximating an arbitrary desired covariance structure at these samples is also presented. Simulation results validate the effectiveness of this random sampled field model, and clinical MEG evoked response data is processed to demonstrate algorithm performance. 1
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