Bayesian Multi-Group Functional Factor Models for studying group-specific neural activity patterns

Thomas M. Menino Convention & Exhibition Center 

Functional data consist of trajectories observed over a continuous domain, such as time, space, or wavelength. Here we consider curves observed on different groups of subjects and propose a Bayesian multi-group functional factor analysis framework that jointly models the data via an explicit decomposition into group-specific mean functions and latent components that capture both common and distinct latent structures across the groups. We impose a parameter-expanded cumulative shrinkage process prior on the factor loadings, which induces increasing shrinkage and enables data-driven basis selection. For real data analysis, we apply the model to EEG data on alcoholic and healthy subjects and identify shared latent factors that capture canonical characteristic components of the EEG curves which are common to both groups of subjects, along with group-specific factors that reveal group-specific neural activity patterns.

Functional data

Bayesian analysis

data-driven basis

EEG data