Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data.

ABSTRACT: The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required. In this work, we propose a pipeline to characterize time-varying networks in single-subject EEG task-related data and further, evaluate its validity on both simulated and experimental datasets. Pre-processing is done to remove channel-wise and trial-wise differences in activity. Functional networks are estimated from short non-overlapping time windows within each "trial," using a sparse-MVAR (Multi-Variate Auto-Regressive) model. Functional "states" are then identified by partitioning the entire space of functional networks into a small number of groups/symbols via k-means clustering.The multi-tria

SUBMITTER: Williams NJ 

PROVIDER: S-EPMC6160873 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature