Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Prior
This work solves the problem of accurately localizing brain activity from noisy EEG/MEG data for neuroscience researchers, but it is incremental as it builds on existing ESI methods with a novel prior.
The paper tackled the problem of EEG/MEG source imaging by addressing noise sensitivity and inflexibility in traditional methods, proposing a probabilistic model with a hierarchical graph prior that showed significant improvements in source localization, especially at high noise levels.
Brain source imaging is an important method for noninvasively characterizing brain activity using Electroencephalogram (EEG) or Magnetoencephalography (MEG) recordings. Traditional EEG/MEG Source Imaging (ESI) methods usually assume that either source activity at different time points is unrelated, or that similar spatiotemporal patterns exist across an entire study period. The former assumption makes ESI analyses sensitive to noise, while the latter renders ESI analyses unable to account for time-varying patterns of activity. To effectively deal with noise while maintaining flexibility and continuity among brain activation patterns, we propose a novel probabilistic ESI model based on a hierarchical graph prior. Under our method, a spanning tree constraint ensures that activity patterns have spatiotemporal continuity. An efficient algorithm based on alternating convex search is presented to solve the proposed model and is provably convergent. Comprehensive numerical studies using synthetic data on a real brain model are conducted under different levels of signal-to-noise ratio (SNR) from both sensor and source spaces. We also examine the EEG/MEG data in a real application, in which our ESI reconstructions are neurologically plausible. All the results demonstrate significant improvements of the proposed algorithm over the benchmark methods in terms of source localization performance, especially at high noise levels.