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Abstract:
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EEG microstate analysis investigates the collection of distinct temporal blocks that characterize the electrical activity of the brain. Brain activity within each microstates is stable, but activity switches rapidly between different microstates in a non-random way. We propose a Bayesian nonparametric model that concurrently estimates the number of microstates and their underlying behavior. We use a Markov switching vector autoregressive (VAR) framework, where a hidden Markov model 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. 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. In this way, our Bayesian nonparametric model defines a sparse set of states which describe the EEG data.
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