Responses of functional brain networks in micro-expressions: An EEG study
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2022.996905/full |
| _version_ | 1811335985527521280 |
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| author | Xingcong Zhao Xingcong Zhao Jiejia Chen Jiejia Chen Tong Chen Tong Chen Shiyuan Wang Shiyuan Wang Ying Liu Xiaomei Zeng Xiaomei Zeng Guangyuan Liu Guangyuan Liu |
| author_facet | Xingcong Zhao Xingcong Zhao Jiejia Chen Jiejia Chen Tong Chen Tong Chen Shiyuan Wang Shiyuan Wang Ying Liu Xiaomei Zeng Xiaomei Zeng Guangyuan Liu Guangyuan Liu |
| author_sort | Xingcong Zhao |
| collection | DOAJ |
| description | 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 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 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. |
| first_indexed | 2024-04-13T17:32:33Z |
| format | Article |
| id | doaj.art-df3d94715b2c4882a918a2fdf460b3c9 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-13T17:32:33Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-df3d94715b2c4882a918a2fdf460b3c92022-12-22T02:37:31ZengFrontiers Media S.A.Frontiers in Psychology1664-10782022-10-011310.3389/fpsyg.2022.996905996905Responses of functional brain networks in micro-expressions: An EEG studyXingcong Zhao0Xingcong Zhao1Jiejia Chen2Jiejia Chen3Tong Chen4Tong Chen5Shiyuan Wang6Shiyuan Wang7Ying Liu8Xiaomei Zeng9Xiaomei Zeng10Guangyuan Liu11Guangyuan Liu12School of Electronic and Information Engineering, Southwest University, Chongqing, ChinaKey Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, ChinaSchool of Electronic and Information Engineering, Southwest University, Chongqing, ChinaKey Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, ChinaSchool of Electronic and Information Engineering, Southwest University, Chongqing, ChinaKey Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, ChinaSchool of Electronic and Information Engineering, Southwest University, Chongqing, ChinaKey Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, ChinaSchool of Music, Southwest University, Chongqing, ChinaSchool of Electronic and Information Engineering, Southwest University, Chongqing, ChinaKey Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, ChinaSchool of Electronic and Information Engineering, Southwest University, Chongqing, ChinaKey Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, ChinaMicro-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 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 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.https://www.frontiersin.org/articles/10.3389/fpsyg.2022.996905/fullmicro-expressionsinhibitory controlelectroencephalographybrain connectivityemotion |
| spellingShingle | Xingcong Zhao Xingcong Zhao Jiejia Chen Jiejia Chen Tong Chen Tong Chen Shiyuan Wang Shiyuan Wang Ying Liu Xiaomei Zeng Xiaomei Zeng Guangyuan Liu Guangyuan Liu Responses of functional brain networks in micro-expressions: An EEG study Frontiers in Psychology micro-expressions inhibitory control electroencephalography brain connectivity emotion |
| title | Responses of functional brain networks in micro-expressions: An EEG study |
| title_full | Responses of functional brain networks in micro-expressions: An EEG study |
| title_fullStr | Responses of functional brain networks in micro-expressions: An EEG study |
| title_full_unstemmed | Responses of functional brain networks in micro-expressions: An EEG study |
| title_short | Responses of functional brain networks in micro-expressions: An EEG study |
| title_sort | responses of functional brain networks in micro expressions an eeg study |
| topic | micro-expressions inhibitory control electroencephalography brain connectivity emotion |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2022.996905/full |
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