Analysis of Speech Imagery using Functional and Effective EEG based Brain Connectivity Parameters

Abstract

Speech imagery is a form of mental imagery which refers to the activity of talking to oneself in silence. In this paper, EEG coherence, a functional connectivity parameter is calculated to analyze the concurrence of the different regions of the brain and Effective connectivity parameters such as Partial Directed Coherence (PDC), Directed Transfer Function (DTF) and Information theory based parameter Transfer Entropy (TE) are estimated to find the direction and strength of the connectivity patterns of the given speech imagery task. It has been observed from the results that by using functional and effective connectivity parameters the left frontal lobe electrodes was found to be high during speech production and left temporal lobe electrodes was found to be high while imagining the word silently in the brain due to the proximity of the electrodes to the Broca's and Wernicke's area respectively. The results suggest that the proposed methodology is a promising non-invasive approach to study directional connectivity in the brain between mutually interconnected neural populations.