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Brain source imaging is an important method for noninvasively characterizingbrain activity using Electroencephalogram (EEG) or Magnetoencephalography (MEG)recordings. Traditional EEG/MEG Source Imaging (ESI) methods usually assumethat either source activity at different time points is unrelated, or thatsimilar spatiotemporal patterns exist across an entire study period. The formerassumption makes ESI analyses sensitive to noise, while the latter renders ESIanalyses unable to account for time-varying patterns of activity. Toeffectively deal with noise while maintaining flexibility and continuity amongbrain activation patterns, we propose a novel probabilistic ESI model based ona hierarchical graph prior. Under our method, a spanning tree constraintensures that activity patterns have spatiotemporal continuity. An efficientalgorithm based on alternating convex search is presented to solve the proposedmodel and is provably convergent. Comprehensive numerical studies usingsynthetic data on a real brain model are conducted under different levels ofsignal-to-noise ratio (SNR) from both sensor and source spaces. We also examinethe EEG/MEG data in a real application, in which our ESI reconstructions areneurologically plausible. All the results demonstrate significant improvementsof the proposed algorithm over the benchmark methods in terms of sourcelocalization performance, especially at high noise levels.

Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Prior