Responses of functional brain networks in micro-expressions: An EEG study

Frontiers in Psychology

article

Xingcong Zhao¹  Jiejia Chen¹  Tong Chen¹  Shiyuan Wang¹  Ying Liu³  Xiaomei Zeng¹  Guangyuan Liu¹ 

[1] School of Electronic and Information Engineering, Southwest University;Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University;School of Music, Southwest University

关键词: Micro-expressions;    Inhibitory Control;    Electroencephalography;    brain connectivity;    emotion;   

DOI  :  10.3389/fpsyg.2022.996905

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Micro-expressions (MEs) can reflect an individual’s subjective emotions and true mental state, and they are widely used in the fields of mental health, justice, law enforcement, intelligence, and security. However, one of the major challenges of working with MEs is that their neural mechanism is not entirely understood. To the best of our knowledge, the present study is the first to use electroencephalography (EEG) to investigate the reorganizations of functional brain networks involved in MEs. We aimed to reveal the underlying neural mechanisms that can provide electrophysiological indicators for ME recognition. A real-time supervision and emotional expression suppression (SEES) experimental paradigm was designed to collect video and EEG data of MEs and no expressions (NEs) of 70 participants expressing positive emotions. Based on the graph theory, we analyzed the efficiency of functional brain network at the scalp level on both macro and micro scales. The results revealed that in the presence of MEs compared with NEs, the participants exhibited higher global efficiency and nodal efficiency in the frontal, occipital, and temporal regions. Additionally, using the random forest algorithm to select a subset of functional connectivity features as input, the support vector machine classifier achieved a classification accuracy for MEs and NEs of 0.81, with an area under the curve (AUC) of 0.85. This finding demonstrates the possibility of using EEG to recognize MEs, with a wide range of application scenarios, such as persons wearing face masks or patients with expression disorders.

【 授权许可】

CC BY