Spatio-temporal Spike and Slab Priors for Multiple Measurement Vector Problems
This work addresses EEG source localization, an incremental improvement for neuroimaging applications.
The paper tackled the multiple measurement vector problem with spatio-temporal sparsity patterns, specifically for EEG source localization, by proposing a probabilistic model with a structured spike and slab prior and Expectation Propagation inference, demonstrating its viability through numerical experiments.
We are interested in solving the multiple measurement vector (MMV) problem for instances, where the underlying sparsity pattern exhibit spatio-temporal structure motivated by the electroencephalogram (EEG) source localization problem. We propose a probabilistic model that takes this structure into account by generalizing the structured spike and slab prior and the associated Expectation Propagation inference scheme. Based on numerical experiments, we demonstrate the viability of the model and the approximate inference scheme.