Effective connectivity in cortical networks during deception: a lie detection study using EEG

Gao, Junfeng, Min, Xiangde, Kang, Qianruo, Si, Huifang, Zhan, Huimiao, Manyande, Anne ORCID: https://orcid.org/0000-0002-8257-0722, Tian, Xuebi, Dong, Yinhong, Zheng, Hua and Song, Jian (2022) Effective connectivity in cortical networks during deception: a lie detection study using EEG. IEEE Journal of Biomedical and Health Informatics. ISSN 2168-2194

Gao et al (2022)_Effective connectivity in cortical networks during deception_A lie detection study using EEG.pdf - Accepted Version

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Previous studies have identified activated regions associated with deceptive tasks and most of them utilized time, frequency, or temporal features to identify 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 in-degree 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 frontoparietal 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.

Item Type: Article
Identifier: 10.1109/jbhi.2022.3172994
Additional Information: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Electroencephalography, Task analysis, Frontal lobe, Feature extraction, Scalp, Brain, Bioinformatics
Subjects: Medicine and health > Clinical medicine
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Depositing User: Anne Manyande
Date Deposited: 18 Jun 2022 17:50
Last Modified: 25 Jul 2022 14:49
URI: https://repository.uwl.ac.uk/id/eprint/9177


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