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基于脑电图的微表情识别:持续情绪下支持微表情的灵活脑网络重构
Authors Chen J, Zhao X, Xiong Z, Liu G
Received 15 November 2024
Accepted for publication 13 March 2025
Published 2 April 2025 Volume 2025:18 Pages 781—796
DOI http://doi.org/10.2147/PRBM.S506311
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Mei-Chun Cheung
Jiejia Chen,1,2,* Xingcong Zhao,1,3,* Zhiheng Xiong,4 Guangyuan Liu1,2
1School of Electronic and Information Engineering, Southwest University, Chongqing, People’s Republic of China; 2Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, People’s Republic of China; 3West China Institute of Children’s Brain and Cognition, Chongqing University of Education, Chongqing, People’s Republic of China; 4School of Humanities, Southeast University, Nanjing, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Guangyuan Liu, Email liugy@swu.edu.cn
Purpose: Micro-expression recognition is valuable in clinical, security, judicial, economic, educational, and human-computer interaction fields. Electroencephalography (EEG)-based micro-expression recognition has gained attention for its objectivity and resistance to interference, unlike image-based methods. However, the neural mechanisms of micro-expressions remain unclear, limiting the development of EEG-based recognition technology.
Methods: We explored the brain reorganization mechanisms of micro-expressions (compared with macro-expressions and neutral expressions) under positive emotions across global networks, functional network modules, and hub brain regions using EEG, graph theory analysis, and functional connectivity.
Results: In global network, micro-expressions demonstrated higher network efficiency, clustering coefficient, and local efficiency, along with shorter average path lengths. In functional network modules, micro-expressions enhanced connectivity between the bilateral superior frontal gyrus (SFG), anterior cingulate cortex, and ventromedial prefrontal cortex (cognitive control), as well as between the left orbitofrontal cortex (OFC), temporal pole (TP), and inferior frontal gyrus (emotional processing). In hub brain regions, micro-expressions increased the hub centrality, information transmission efficiency, and local clustering of bilateral SFG, left OFC, left TP, and left Broca’s area.
Conclusion: Micro-expressions require more efficient global communication and specialized emotion and cognitive control modules. Key hub regions supporting positive micro-expressions include the bilateral SFG (inhibitory control), left OFC and TP (emotion processing), and left Broca’s area (language processing).
Keywords: macro-expressions, neutral expressions, electroencephalography, EEG, graph theory, functional connectivity