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Research of Brain Network on Deception Identification Using Phase Synchrony

Recently many researches have focused on the lie detection (LD) using the event-related-potentials (ERPs) of EEG signals. Deception is a complex cognition process which involves activities in different brain regions. However, most of current ERP-based LD systems only focus on extracting the various features from the EEG signals on one or few channels. In this study, we used the phase lag index (PLI) to establish brain network connections and applied graph theory approach to investigate structure features in functional networks. Thirty participants were required to tell the truth or lie when facing certain stimuli, and their EEG signals were recorded. Statistical analysis indicates that the differences in the extracted graph-based features between the two groups were significant. Furthermore, calculated result shows that the guilty group shows more obvious small-world property than the innocent group, which provides a new approach that could automatically identify the deception in future.