Brain Fingerprinting and Lie Detection: A Study of Dynamic Functional Connectivity Patterns of Deception Using EEG Phase Synchrony Analysis

被引:5
|
作者
Gao, Junfeng [1 ]
Gu, Lingyun [2 ]
Min, Xiangde [3 ]
Lin, Pan [4 ]
Li, Chenhong [1 ]
Zhang, Quan [5 ]
Rao, Nini [6 ]
机构
[1] South Cent Univ Nationalities, Coll Biomed Engn, Hubei Key Lab Med Informat Anal & Tumor Diag & Tr, Key Lab Cognit Sci,State Ethn Affairs Commiss, Wuhan 430074, Peoples R China
[2] Southeast Univ, Minist Educ, Sch Biol Sci & Med Engineer, Key Lab Child Dev & Learning Sci, Nanjing 210096, Peoples R China
[3] Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Dept Radiol, Wuhan 430030, Peoples R China
[4] Hunan Normal Univ, Sch Educ Sci, Changsha 410081, Peoples R China
[5] Harvard Med Sch, Massachusetts Gen Hosp, Neural Syst Grp, Charlestown, MA 02129 USA
[6] Univ Elect Sci & Technol China, Sch Life Sci & Technol, Chengdu 610054, Peoples R China
基金
中国国家自然科学基金;
关键词
Phase synchrony; lie detection; EEG; brain fingerprinting; functional connectivity; EVENT-RELATED DESYNCHRONIZATION; SUSTAINED ATTENTION DEFICITS; ANTERIOR CINGULATE CORTEX; PARIETAL CORTEX; VISUAL INFORMATION; PREFRONTAL CORTEX; EPISODIC MEMORY; ALPHA-ACTIVITY; POWER CHANGES; OSCILLATIONS;
D O I
10.1109/JBHI.2021.3095415
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This study investigated the brain functional connectivity (FC) patterns related to lie detection (LD) tasks with the purpose of analyzing the underlying cognitive processes and mechanisms in deception. Using the guilty knowledge test protocol, 30 subjects were divided randomly into guilty and innocent groups, and their electroencephalogram (EEG) signals were recorded on 32 electrodes. Phase synchrony of EEG was analyzed between different brain regions. A few-trials-based relative phase synchrony (FTRPS) measure was proposed to avoid the false synchronization that occurs due to volume conduction. FTRPS values with a significantly statistical difference between two groups were employed to construct FC patterns of deception, and the FTRPS values from the FC networks were extracted as the features for the training and testing of the support vector machine. Finally, four more intuitive brain fingerprinting graphs (BFG) on delta, theta, alpha and beta bands were respectively proposed. The experimental results reveal that deceptive responses elicited greater oscillatory synchronization than truthful responses between different brain regions, which plays an important role in executing lying tasks. The functional connectivity in the BFG is mainly implicated in the visuo-spatial imagery, bottom-top attention and memory systems, work memory and episodic encoding, and top-down attention and inhibition processing. These may, in part, underlie the mechanism of communication between different brain cortices during lying. High classification accuracy demonstrates the validation of BFG to identify deception behavior, and suggests that the proposed FTRPS could be a sensitive measure for LD in the real application.
引用
收藏
页码:600 / 613
页数:14
相关论文
共 49 条
  • [1] Effective Connectivity in Cortical Networks During Deception: A Lie Detection Study Based on EEG
    Gao, Junfeng
    Min, Xiangde
    Kang, Qianruo
    Si, Huifang
    Zhan, Huimiao
    Manyande, Anne
    Tian, Xuebi
    Dong, Yinhong
    Zheng, Hua
    Song, Jian
    [J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2022, 26 (08) : 3755 - 3766
  • [2] Neural correlates of deception in lie detection using EEG frequency analysis
    Chun, YeSeul
    Jeong, Ji Woon
    Jeon, Hyeonjin
    Lee, Sang Hyun
    Kim, Suk Chan
    Bang, Cheol
    Choi, Hoon
    Kim, Keun Young
    Kim, Hyun Taek
    [J]. INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY, 2014, 94 (02) : 260 - 260
  • [3] Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity
    Emily S Finn
    Xilin Shen
    Dustin Scheinost
    Monica D Rosenberg
    Jessica Huang
    Marvin M Chun
    Xenophon Papademetris
    R Todd Constable
    [J]. Nature Neuroscience, 2015, 18 : 1664 - 1671
  • [4] Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity
    Finn, Emily S.
    Shen, Xilin
    Scheinost, Dustin
    Rosenberg, Monica D.
    Huang, Jessica
    Chun, Marvin M.
    Papademetris, Xenophon
    Constable, R. Todd
    [J]. NATURE NEUROSCIENCE, 2015, 18 (11) : 1664 - 1671
  • [5] Coherence and phase synchrony analyses of EEG signals in Mild Cognitive Impairment (MCI): A study of functional brain connectivity
    Handayani, Nita
    Haryanto, Freddy
    Khotimah, Siti Nurul
    Arif, Idam
    Taruno, Warsito Purwo
    [J]. POLISH JOURNAL OF MEDICAL PHYSICS AND ENGINEERING, 2018, 24 (01): : 1 - 9
  • [6] A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG
    Abazid, Majd
    Houmani, Nesma
    Boudy, Jerome
    Dorizzi, Bernadette
    Mariani, Jean
    Kinugawa, Kiyoka
    [J]. ENTROPY, 2021, 23 (11)
  • [7] ADHD detection using dynamic connectivity patterns of EEG data and ConvLSTM with attention framework
    Bakhtyari, Mohammadreza
    Mirzaei, Sayeh
    [J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2022, 76
  • [8] Phase synchrony analysis of EEG during music perception reveals changes in functional connectivity due to musical expertise
    Bhattacharya, J
    Petsche, H
    [J]. SIGNAL PROCESSING, 2005, 85 (11) : 2161 - 2177
  • [9] Analysis of EEG Fluctuation Patterns Using Nonlinear Phase-Based Functional Connectivity Measures for Emotion Recognition
    Kumar, Himanshu
    Ganapathy, Nagarajan
    Puthankattil, Subha D.
    Swaminathan, Ramakrishnan
    [J]. FLUCTUATION AND NOISE LETTERS, 2024,
  • [10] Multi-Scale Analysis of the Dynamics of Brain Functional Connectivity using EEG
    Haddad, Ali
    Najafizadeh, Laleh
    [J]. PROCEEDINGS OF 2016 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS), 2016, : 240 - 243