[1]吴彦泽,王海玲,高宇飞.基于脑电的脑网络稳定模式情绪识别研究[J].中国医学物理学杂志,2023,40(6):727-735.[doi:DOI:10.3969/j.issn.1005-202X.2023.06.010]
　WU Yanze,WANG Hailing,GAO Yufei.Stable brain network pattern for EEG-based emotion recognition[J].Chinese Journal of Medical Physics,2023,40(6):727-735.[doi:DOI:10.3969/j.issn.1005-202X.2023.06.010]
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基于脑电的脑网络稳定模式情绪识别研究()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第6期
页码:
727-735
栏目:
医学信号处理与医学仪器
出版日期:
2023-06-27

文章信息/Info

Title:
Stable brain network pattern for EEG-based emotion recognition
文章编号:
1005-202X（2023）06-0727-09
作者:
吴彦泽1; 王海玲1; 高宇飞2
1.上海工程技术大学电子电气工程学院， 上海 201600； 2.郑州大学网络空间安全学院， 河南 郑州 450002
Author(s):
WU Yanze1;  WANG Hailing1;  GAO Yufei2
1. School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201600, China 2. School of Cyberspace Security, Zhengzhou University, Zhengzhou 450002, China
关键词:
脑电图; 情绪识别; 脑网络模式; 锁相值; 图论; 最小生成树; 机器学习
Keywords:
Keywords: electroencephalogram emotion recognition brain network model phase locking value graph theory minimum spanning tree machine learning
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2023.06.010
文献标志码:
A
摘要:
利用锁相值构建不同时间段的情绪脑网络，探讨不同时间情绪相关脑网络模式的稳定性，提出基于脑网络二阶特征的情绪识别框架。结果表明，在跨被试研究和单被试研究中最高准确率分别为79.17%和82.92%，ANOVA分析3个时间段的识别结果无显著性差异，证明本研究提出的情绪识别框架是稳定的。在不同时间段，同类别的情绪均具有相同的脑网络连接模式和最小生成树的结构，说明相同情绪在不同时间存在稳定的脑网络模式。使用脑网络特征进行情绪识别是稳定且可靠的，这为人机交互中情绪识别提供一种新途径。
Abstract:
Abstract: The emotional brain networks at different time periods are constructed using phase locking values for exploring the stability of emotion-related brain network patterns at different time periods and an emotion recognition framework based on the second-order features of brain network is proposed. The results show that the highest accuracy are 79.17% and 82.92% in cross-subject study and single-subject study, and there is no significant difference in the recognition results of the 3 time periods in ANOVA analysis, which proves that the emotion recognition framework proposed in the study is stable. In different time periods, the emotions of the same category have the same brain network connection pattern and minimum spanning tree structure, indicating that the same emotion has a stable brain network pattern in different time periods. Using brain network features for emotion recognition is stable and reliable, which provides a new approach for emotion recognition in human-computer interaction.

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备注/Memo

备注/Memo:
【收稿日期】2023-01-18 【基金项目】国家自然科学基金（62001284, 62006210）；郑州大学高层次人才科研启动基金（32340306） 【作者简介】吴彦泽，硕士，研究方向：情绪识别，E-mail: 15613394243@163.com 【通信作者】王海玲，博士，研究方向：神经生理信号处理与分析，E-mail: wanghailing@sues.edu.cn
更新日期/Last Update: 2023-06-28