Effective Connectivity in Cortical Networks During Deception: A Lie Detection Study Based on EEG.

Abstract

Thus far, when deception behaviors occur, the connectivity patterns and the communication between different brain areas remain largely unclear. In this study, the most important information flows (MIIFs) between different brain cortices during deception were explored. First, the guilty knowledge test protocol was employed, and 64 electrodes' electroencephalogram (EEG) signals were recorded from 30 subjects (15 guilty and 15 innocent). Cortical current density waveforms were then estimated on the 24 regions of interest (ROIs). Next, partial directed coherence (PDC), an effective connectivity (EC) analysis was applied in the cortical waveforms to obtain the brain EC networks for four bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz) and beta (13-30 Hz). Furthermore, using the graph theoretical analysis, the network parameters with significant differences in the EC network were extracted as features to identify the two groups. The high classification accuracy of the four bands demonstrated that the proposed method was suitable for lie detection. In addition, based on the optimal features in the classification mode, the brain "hub" regions were identified, and the MIIFs were significantly different between the guilty and innocent groups. Moreover, the fronto-parietal network was found to be most prominent among all MIIFs at the four bands. Furthermore, combining the neurophysiology significance of the four frequency bands, the roles of all MIIFs were analyzed, which could help us to uncover the underlying cognitive processes and mechanisms of deception.