Scalable Spatio-Temporal Bayesian Analysis of High-Dimensional Electroencephalography Data

• Activity Number: 500 - Invited Papers: Journal of Statistical Analysis and Data Mining
• Type: Invited
• Date/Time: Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
• Sponsor: Journal on Statistical Analysis and Data Mining
• Author(s): Dipak K Dey* and Shariq Mohammed and Yuping Zhang
• Companies: University of Connecticut and Boston University and University of Connecticut
• Keywords: Gibbs sampling; EEG inverse problem; Murphy Diagram; likelihood approximation; Variable selection; Kronecker product

Abstract

We present a scalable Bayesian modeling approach for identifying brain regions that respond to a certain stimulus and use them to classify subjects. We specifically deal with multi-subject EEG data with a binary response distinguishing between alcoholic and control groups. The covariates are matrix-variate with measurements taken for each subject at different locations across multiple time points. EEG data has a complex structure with both spatial and temporal attributes to it. We use a divide-and-conquer strategy to build multiple local models, i.e., one model at each time point separately. We employ Bayesian variable selection approaches using a structured continuous spike-and-slab prior to identify the locations which respond to a certain stimulus. We incorporate the spatio-temporal structure through a Kronecker product of the spatial and temporal correlation matrices. We develop a highly scalable estimation algorithm using likelihood approximation to deal with large number of parameters in the model. Variable selection is done via clustering of the locations based on their duration of activation. We use scoring rules to evaluate the prediction performance for a case study.