P047 Graph Analysis of EEG Functional Connectivity during Lie Detection
Yun-jeong Cho1, Hoon-hee Kim*2
1 Department of Data Engineering, Pukyong National University, Busan, South Korea 2Department of Computer Engineering and Artificial Intelligence, Pukyong National University, Busan, South Korea
*Email: h2kim@pknu.ac.kr
Introduction
Lie detection is an important research topic in various fields, including psychology, forensic science, and neuroscience, as it involves complex processes. By measuring the brain's functional connectivity using EEG data and calculating the graph-theoretical metrics (e.g., the average clustering coefficient), it is possible to quantitatively assess the changes in brain network dynamics between lie and truth conditions [1]. In this study, we aimed to analyze the overall brain connectivity differences between lie and truth conditions by computing inter-channel coherence and network metrics within a specific frequency band during the answer phase. Methods Among 12 subjects, participants were divided into two groups —those who consistently lied and those who consistently told the truth. After excluding two lie subjects, each group comprised five subjects. EEG data were recorded for 15 seconds while subjects answered a specific question with only the first 3 seconds after answer onset analyzed. Inter-channel coherence [2] was computed in the high-frequency range, focusing on the beta band activated during lying. A functional connectivity (FC) matrix was constructed by applying a threshold, and key metrics —such as the average clustering coefficient and global efficiency were calculated. Statistical validation was performed using t-tests and Mann-Whitney U tests. Results Overall, significant differences in brain network metrics were observed between the lie and truth conditions (Fig. 1). In particular, the subjects in the lie group, the average clustering coefficient was found to increase significantly than in the subjects in the truth group. Statistical analyses confirmed that these differences were significant, with a larger than expected effect size, suggesting that overall brain connectivity is altered when individuals lie. These findings support the notion that the complex cognitive processes involved in lying may lead to changes in the brain’s network organization. Discussion This study compared the overall brain network changes between lie and truth conditions using the average clustering coefficient computed for each subject. The results showed that the lie condition exhibited increased global brain connectivity, suggesting an additional cognitive load during lying. However, using subject-level averages limits the ability to directly assess local connectivity changes in specific brain regions, and caution is warranted in interpretation due to the small sample size. Future research should include a larger number of subjects and incorporate various network metrics, such as inter-channel analyses, to more precisely evaluate brain connectivity changes.
Figure 1. Fig 1. Topographic maps of the average clustering coefficient comparing lie (left) and truth (right) groups during the answer phase. Increased clustering (dark red) in the lie condition indicates significantly greater overall brain connectivity compared to the truth condition. Acknowledgements This study was supported by the National Police Agency and the Ministry of Science, ICT & Future Planning (2024-SCPO-B-0130), the National Research Foundation of Korea grant funded by the Korea government (RS-2023-00242528), the National Program for Excellence in SW, supervised by the IITP(Institute of Information & communications Technology Planing & Evaluation) in 2025(2024-0-00018) References 1. Gao J, Gu L, Min X et al. (2022). Brain Fingerprinting and Lie Detection: A Study of Dynamic Functional Connectivity Patterns of Dedeption Using EEG Phase Synchrony Analysis. IEEE Journal of Biomedical and Health Informatics, 26(2), 600-613.https://doi.org/10.1109/jbhi.2021.3095415 2. Bowyer S. (2016). Coherence a measure of the brain networks: past and present. Neuropsychiatric Electrophysiology, 2(1).https://doi.org/10.1186/s40810-015-0015-7
