In this study, partial mutual information at the source level was used to construct brain functional networks in order to examine differences in brain functions between lying and honest responses. The study used independent component analysis and clustering methods to computationally generate source signals from EEG signals recorded from subjects who were lying and those who were being honest. Partial mutual information was calculated between regions of interest (ROIs), and used to construct a functional brain network with ROIs as nodes and partial mutual information values as connections between them. The partial mutual information connections that showed significant differences between the two groups of people were selected as the feature set and classified using a functional connectivity network (FCN) classifier, resulting in an accuracy of 88.5%. Analysis of the brain networks of the lying and honest groups showed that, in the lying state, there was increased informational exchange between the frontal lobe and temporal lobe, and the language motor center of the frontal lobe exchanged more information with other brain regions, suggesting increased working and episodic memory load and the mobilization of more cognitive resources.

Previous studies have identified activated regions associated with deceptive tasks and most of them utilized time, frequency, or temporal features to determine deceptive responses. However, when deception behaviors occur, the functional connectivity pattern and the communication between different brain areas remain largely unclear. In this study, we explored the most important information flows between different brain cortices during deception. First, we employed the guilty knowledge test protocol and recorded on 64 electrodes' electroencephalogram (EEG) signals from 30 subjects (15 guilty and 15 innocent). EEG source estimation was then performed to compute the cortical activities on the 24 regions of interest (ROIs). Next, effective connectivity was calculated by partial directed coherence (PDC) analysis applied to the cortical signals. Furthermore, based on the graph theoretical analysis, the network parameters with significant differences were extracted as features to identify two groups of subjects. In addition, the ROIs frequently involved in the above network parameters were selected and based on the difference in the group mean of PDC values of all the edges connected with the selected ROIs, we presented the strongest information flows (MIIF) in the guilty group relative to the innocent group. Experimental results first show that the optimal classification features are mainly indegree and out-degree measures of the ROI and the high classification accuracy for four bands demonstrated that the proposed method is suitable for lie detection. In addition, the frontal-parietal network was found to be most prominent among all the MIIFs in four bands. Finally, combining the neurophysiology signification of four frequency bands, respectively, we analyzed the roles of all the important information flows to uncover the underlying cognitive processes and mechanisms used in deception.

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