School of Mathematics, Southeast University, Nanjing 210096, China; Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, Southeast University, Nanjing 210096, China.
School of Mathematics, Southeast University, Nanjing 210096, China; Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, Southeast University, Nanjing 210096, China; Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea.
Neural Netw. 2022 Jan;145:308-318. doi: 10.1016/j.neunet.2021.10.023. Epub 2021 Nov 3.
Emotion classification based on neurophysiology signals has been a challenging issue in the literature. Recent neuroscience findings suggest that brain network structure underlying the different emotions provides a window in understanding human affection. In this paper, we propose a novel method to capture the distinct minimum spanning tree (MST) topology underpinning the different emotions. Specifically, we propose a hierarchical aggregation-based graph neural network to investigate the MST structure in emotion recognition. Extensive experiments on the public available DEAP dataset demonstrate the superior performance of the model in emotion classification as compared to existing methods. In addition, the results show that the theta, lower beta and gamma frequency band network information are more sensitive to emotions, suggesting a multi-frequency interaction in emotion processing.
基于神经生理学信号的情绪分类一直是文献中的一个难题。最近的神经科学发现表明，不同情绪背后的大脑网络结构为理解人类情感提供了一个窗口。在本文中，我们提出了一种新的方法来捕捉不同情绪下的独特最小生成树（MST）拓扑结构。具体来说，我们提出了一种基于分层聚合的图神经网络来研究情绪识别中的 MST 结构。在公共可用的 DEAP 数据集上的广泛实验表明，与现有方法相比，该模型在情绪分类方面具有更好的性能。此外，结果表明，θ、低β和γ频段的网络信息对情绪更为敏感，这表明情绪处理中存在多频互动。
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