Title: A grouped Beta process model for multivariate resting-state EEG microstate analysis on twins
Authors: Mark Fiecas - University of Minnesota (United States) [presenting]
Abstract: EEG microstate analysis investigates the collection of distinct temporal blocks that characterize the electrical activity of the brain. We propose a Bayesian nonparametric model that estimates the number of microstates and the underlying behavior. We use a Markov switching vector autoregressive (VAR) framework, where a hidden Markov model (HMM) controls the non-random state switching dynamics of the EEG activity and a VAR model defines the behavior of all time points within a given state. We analyze resting state EEG data from twin pairs collected through the Minnesota Twin Family Study, consisting of 70 epochs per participant corresponding to 140 seconds of EEG data. We fit our model at the twin pair level, sharing information within epochs from the same participant and within epochs from the same twin pair. We capture within twin pair similarity by using a Beta process Bernoulli process to consider an infinite library of microstates and allowing each participant to select a finite number of states from this library. The state spaces of highly similar twins may completely overlap while dissimilar twins could select distinct state spaces. In this way, our Bayesian nonparametric model defines a sparse set of states which describe the EEG data. All epochs from a single participant use the same set of states and are assumed to adhere to the same state switching dynamics in the HMM model.