A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG

►Precise characterization of the inter-trial amplitude variability. ►Sparsity on the source number through sparse Bayesian learning. ►Joint modeling of EEG data from two experimental conditions. ►Analysis of both simulated and real data demonstrates the efficacy of our approach. ►Many in-depth insights are offered into our methodology.