Micro-state-based neural decoding of speech categorization using Bayesian non-parametrics

Understanding the many-to-many mapping between patterns of functional brain connectivity and discrete behavioral responses is critical for speech-language processing. We present a microstate-based analysis of EEG recordings to characterize spatio-temporal dynamics of neural activities that underly rapid speech categorization decisions. We implemented a data driven approach using Bayesian non-parametrics to capture the mapping between EEG and the speed of listeners phoneme identification [i.e., response time (RT)] during speech labeling tasks. Based on our empirical analyses, we show task-relevant events such as resting-state, stimulus coding, auditory-perceptual object (category) formation, and response selection can be explained using patterns of micro-state dwell-time and are decodable as unique time segments during speech perception. State-dependent activities localize to a fronto-temporo-parietal circuit (superior temporal, supramarginal, inferior frontal gyri) exposing a core decision brain network (DN) underlying rapid speech categorization. Furthermore, RTs were inversely proportional to the frequency of state transitions, such that the rate of change between brain microstates was higher for trials with slower compared to faster RTs. Our findings imply that during rapid speech perception, higher uncertainty producing prolonged RTs (slower decision-making) is associated with staying in the DN longer compared lower RTs (faster decisions). We also show that listeners perceptual RTs are highly sensitive to individual differences. Our computational method opens a new avenue in segmentation and dynamic brain connectivity for modeling neuroimaging data and understanding task-related cognitive events.