Hierarchical Bayesian modeling of inter-trial variability and variational Bayesian learning of common spatial patterns from multichannel EEG

In numerous neuroscience studies, multichannel EEG data are often recorded over multiple trial periods under the same experimental condition. To date, little effort is aimed to learn spatial patterns from EEG data to account for trial-to-trial variability. In this paper, a hierarchical Bayesian framework is introduced to model inter-trial source variability while extracting common spatial patterns under multiple experimental conditions in a supervised manner. We also present a variational Bayesian algorithm for model inference, by which the number of sources can be determined effectively via automatic relevance determination (ARD). The efficacy of the proposed learning algorithm is validated with both synthetic and real EEG data. Using two brain-computer interface (BCI) motor imagery data sets we show the proposed algorithm consistently outperforms the common spatial patterns (CSP) algorithm while attaining comparable performance with a recently proposed discriminative approach.