EEG-Based Micro-Expression Recognition: Flexible Brain Network Reconfiguration Supporting Micro-Expressions Under Positive Emotion.

IF 2.8 3区 心理学 Q2 PSYCHOLOGY, CLINICAL
Psychology Research and Behavior Management Pub Date : 2025-04-02 eCollection Date: 2025-01-01 DOI:10.2147/PRBM.S506311
Jiejia Chen, Xingcong Zhao, Zhiheng Xiong, Guangyuan Liu
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引用次数: 0

Abstract

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).

基于脑电图的微表情识别:支持积极情绪下微表情的灵活脑网络重构。
目的:微表情识别在临床、安全、司法、经济、教育和人机交互等领域具有重要价值。与基于图像的方法不同,基于脑电图(EEG)的微表情识别因其客观性和抗干扰性而备受关注。然而,微表情的神经机制仍不清楚,限制了基于脑电图的识别技术的发展:方法:我们利用脑电图、图论分析和功能连接,在全局网络、功能网络模块和中枢脑区探索了微表情(与宏表情和中性表情相比)在积极情绪下的大脑重组机制:结果:在全局网络中,微表情表现出更高的网络效率、聚类系数和局部效率,以及更短的平均路径长度。在功能网络模块中,微表达增强了双侧额上回(SFG)、扣带回前皮层和腹内侧前额叶皮层(认知控制)之间以及左侧眶额皮层(OFC)、颞极(TP)和额下回(情绪处理)之间的连通性。在中枢脑区,微表达提高了双侧SFG、左侧OFC、左侧TP和左侧布罗卡区的中枢中心性、信息传递效率和局部聚类:结论:微表情需要更高效的全局通信和专门的情绪与认知控制模块。支持积极微表情的关键枢纽区域包括双侧 SFG(抑制控制)、左侧 OFC 和 TP(情绪处理)以及左侧 Broca 区(语言处理)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.50
自引率
4.70%
发文量
341
审稿时长
16 weeks
期刊介绍: Psychology Research and Behavior Management is an international, peer-reviewed, open access journal focusing on the science of psychology and its application in behavior management to develop improved outcomes in the clinical, educational, sports and business arenas. Specific topics covered in the journal include: -Neuroscience, memory and decision making -Behavior modification and management -Clinical applications -Business and sports performance management -Social and developmental studies -Animal studies The journal welcomes submitted papers covering original research, clinical studies, surveys, reviews and evaluations, guidelines, expert opinion and commentary, case reports and extended reports.
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