Yang Kai, Tong Li, Shu Jun, Zhuang Ning, Yan Bin, Zeng Ying
PLA Strategy Support Force Information Engineering University, Zhengzhou, China.
MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China.
Front Hum Neurosci. 2020 Mar 24;14:89. doi: 10.3389/fnhum.2020.00089. eCollection 2020.
High-frequency electroencephalography (EEG) signals play an important role in research on human emotions. However, the different network patterns under different emotional states in the high gamma band (50-80 Hz) remain unclear. In this paper, we investigate different emotional states using functional network analysis on various frequency bands. We constructed multiple functional networks on different frequency bands and performed functional network analysis and time-frequency analysis on these frequency bands to determine the significant features that represent different emotional states. Furthermore, we verified the effectiveness of these features by using them in emotion recognition. Our experimental results revealed that the network connections in the high gamma band with significant differences among the positive, neutral, and negative emotional states were much denser than the network connections in the other frequency bands. The connections mainly occurred in the left prefrontal, left temporal, parietal, and occipital regions. Moreover, long-distance connections with significant differences among the emotional states were observed in the high frequency bands, particularly in the high gamma band. Additionally, high gamma band fusion features derived from the global efficiency, network connections, and differential entropies achieved the highest classification accuracies for both our dataset and the public dataset. These results are consistent with literature and provide further evidence that high gamma band EEG signals are more sensitive and effective than the EEG signals in other frequency bands in studying human affective perception.
高频脑电图（EEG）信号在人类情绪研究中发挥着重要作用。然而，高伽马频段（50 - 80赫兹）中不同情绪状态下的不同网络模式仍不清楚。在本文中，我们使用功能网络分析对不同频段的不同情绪状态进行研究。我们在不同频段构建了多个功能网络，并对这些频段进行功能网络分析和时频分析，以确定代表不同情绪状态的显著特征。此外，我们通过将这些特征用于情绪识别来验证其有效性。我们的实验结果表明，在积极、中性和消极情绪状态之间存在显著差异的高伽马频段中的网络连接比其他频段中的网络连接密集得多。这些连接主要发生在左前额叶、左颞叶、顶叶和枕叶区域。此外，在高频段，特别是高伽马频段中观察到了情绪状态之间存在显著差异的长距离连接。此外，从全局效率、网络连接和微分熵导出的高伽马频段融合特征在我们的数据集和公共数据集上都达到了最高的分类准确率。这些结果与文献一致，并进一步证明了在研究人类情感感知方面，高伽马频段脑电图信号比其他频段的脑电图信号更敏感、更有效。
