Probabilistic Structure Learning for EEG/MEG Source Imaging With Hierarchical Graph Priors

Feng Liu, Li Wang*, Yifei Lou, Ren-Cang Li, Patrick L. Purdon*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

16 Citations (Scopus)


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 the source activities at different time points are unrelated, and do not utilize the temporal structure in the source activation, making the ESI analysis sensitive to noise. Some methods may encourage very similar activation patterns across the entire time course and may be incapable of accounting the variation along the time course. 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 an alternating convex search is presented to solve the resulting problem of the proposed model with guaranteed convergence. Comprehensive numerical studies using synthetic data on a realistic 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 datasets in two real applications, in which our ESI reconstructions are neurologically plausible. All the results demonstrate significant improvements of the proposed method over benchmark methods in terms of source localization performance, especially at high noise levels.
Original languageEnglish
Pages (from-to)321-334
Number of pages14
JournalIEEE Transactions on Medical Imaging
Issue number1
Publication statusPublished - Jan 2021


Dive into the research topics of 'Probabilistic Structure Learning for EEG/MEG Source Imaging With Hierarchical Graph Priors'. Together they form a unique fingerprint.

Cite this