Abstract
In this paper, we present a novel approach to imaging sparse and focal neural current sources from MEG (magnetoencephalography) data. Using the framework of Tikhonov regularization theory, we introduce a new stabilizer that uses the concept of controlled support to incorporate a priori assumptions about the area occupied by focal sources. The paper discusses the underlying Tikhonov theory and its relationship to a Bayesian formulation which in turn allows us to interpret and better understand other related algorithms.
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