Raquel Prado University of California, Santa Cruz (UC Santa Cruz) We present Bayesian approaches for studying multiple brain signals. We consider state-space models with time-varying parameters, factor models with structured priors, and hierarchical models with sparsity priors. We illustrate the use of these modeling approaches for analyzing fMRI and EEG datasets recorded in studies that involved multiple subjects and multiple treatments or experimental conditions.