Brain Connectivity Analysis for EEG-Based Face Perception Task

Abstract

Face perception is considered a highly developed visual recognition skill in human beings. Most face perception studies used functional magnetic resonance imaging to identify different brain cortices related to face perception. However, studying brain connectivity networks for face perception using electroencephalography (EEG) has not yet been done. In the proposed framework, initially, a correlation-tree traversal-based channel selection algorithm is developed to identify the “optimum” EEG channels by removing the highly correlated EEG channels from the input channel set. Next, the effective brain connectivity network among those “optimum” EEG channels is developed using multivariate transfer entropy (TE) while participants watched different face stimuli (i.e., famous, unfamiliar, and scrambled). We transform EEG channels into corresponding brain regions for generalization purposes and identify the active brain regions for each face stimulus. To find the stimuluswise brain dynamics, the information transfer among the identified brain regions is estimated using several graphical measures [global efficiency (GE) and transitivity]. Our model archives the mean GE of 0.800, 0.695, and 0.581 for famous, unfamiliar, and scrambled faces, respectively. Identifying face perception-specific brain regions will enhance understanding of the EEG-based face-processing system. Understanding the brain networks of famous, unfamiliar, and scrambled faces can be useful in criminal investigation applications.