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We focus on electromagnetoencephalography imaging of the neural activity and,in particular, finding a robust estimate for the primary current distributionvia the hierarchical Bayesian model (HBM). Our aim is to develop a reasonablyfast maximum a posteriori (MAP) estimation technique which would be applicablefor both superficial and deep areas without specific a priori knowledge of thenumber or location of the activity. To enable source distinguishability for anydepth, we introduce a randomized multiresolution scanning (RAMUS) approach inwhich the MAP estimate of the brain activity is varied during thereconstruction process. RAMUS aims to provide a robust and accurate imagingoutcome for the whole brain, while maintaining the computational cost on anappropriate level. The inverse gamma (IG) distribution is applied as theprimary hyperprior in order to achieve an optimal performance for the deep partof the brain. In this proof-of-the-concept study, we consider the detection ofsimultaneous thalamic and somatosensory activity via numerically simulated datamodeling the 14-20 ms post-stimulus somatosensory evoked potential and fieldresponse to electrical wrist stimulation. Both a spherical and realistic modelare utilized to analyze the source reconstruction discrepancies. In thenumerically examined case, RAMUS was observed to enhance the visibility of deepcomponents and also marginalizing the random effects of the discretization andoptimization without a remarkable computation cost. A robust and accurate MAPestimate for the primary current density was obtained in both superficial anddeep parts of the brain.

Randomized Multiresolution Scanning in Focal and Fast E/MEG Sensing of Brain Activity with a Variable Depth