Cao Rui, Shi Huiyu, Wang Xin, Huo Shoujun, Hao Yan, Wang Bin, Guo Hao, Xiang Jie
Department of Software Engineering, College of Software, Taiyuan University of Technology, Taiyuan 030600, China.
Department of Computer Science and Technology, College of Information and Computer, Taiyuan University of Technology, Taiyuan 030600, China.
Entropy (Basel). 2020 Aug 26;22(9):939. doi: 10.3390/e22090939.
Despite many studies reporting hemispheric asymmetry in the representation and processing of emotions, the essence of the asymmetry remains controversial. Brain network analysis based on electroencephalography (EEG) is a useful biological method to study brain function. Here, EEG data were recorded while participants watched different emotional videos. According to the videos' emotional categories, the data were divided into four categories: high arousal high valence (HAHV), low arousal high valence (LAHV), low arousal low valence (LALV) and high arousal low valence (HALV). The phase lag index as a connectivity index was calculated in theta (4-7 Hz), alpha (8-13 Hz), beta (14-30 Hz) and gamma (31-45 Hz) bands. Hemispheric networks were constructed for each trial, and graph theory was applied to quantify the hemispheric networks' topological properties. Statistical analyses showed significant topological differences in the gamma band. The left hemispheric network showed significantly higher clustering coefficient (), global efficiency () and local efficiency () and lower characteristic path length () under HAHV emotion. The right hemispheric network showed significantly higher and and lower under HALV emotion. The results showed that the left hemisphere was dominant for HAHV emotion, while the right hemisphere was dominant for HALV emotion. The research revealed the relationship between emotion and hemispheric asymmetry from the perspective of brain networks.
尽管许多研究报告了情绪表征和处理中的半球不对称性，但这种不对称性的本质仍存在争议。基于脑电图（EEG）的脑网络分析是研究脑功能的一种有用的生物学方法。在此，参与者观看不同情绪视频时记录了EEG数据。根据视频的情绪类别，数据被分为四类：高唤醒高效价（HAHV）、低唤醒高 效价（LAHV）、低唤醒低效价（LALV）和高唤醒低效价（HALV）。在theta（4 - 7Hz）、alpha（8 - 13Hz）、beta（14 - 30Hz）和gamma（31 - 45Hz）频段计算作为连接性指标的相位滞后指数。为每个试验构建半球网络，并应用图论来量化半球网络的拓扑特性。统计分析表明在gamma频段存在显著的拓扑差异。在HAHV情绪下，左半球网络显示出显著更高的聚类系数（）、全局效率（）和局部效率（）以及更低的特征路径长度（）。在HALV情绪下，右半球网络显示出显著更高的 和 以及更低的 。结果表明，左半球在HAHV情绪中占主导，而右半球在HALV情绪中占主导。该研究从脑网络的角度揭示了情绪与半球不对称性之间的关系。
