Graph Convolutional Neural Network Based Emotion Recognition with Brain Functional Connectivity Network

Q2 Decision Sciences International Journal of Crowd Science Pub Date : 2024-09-16 DOI:10.26599/IJCS.2024.9100022
Pengzhi Gao;Xiangwei Zheng;Tao Wang;Yuang Zhang
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引用次数: 0

Abstract

Emotion recognition plays an important role in Human Computer Interaction (HCI) and the evaluation of human behavior based on emotional state is an important research topic. The purpose of emotion recognition is to automatically identify human's emotional states by analyzing physiological or non-physiological signals. The conventional emotion classification methods cannot comprehensively leverage global and local features which are extracted from Electroencephalogram (EEG) signal generated after being stimulated. Therefore, we propose the graph convolutional neural network based emotion recognition with brain functional connectivity network (GERBN). Firstly, raw EEG data of the public DEAP and SEED datasets is preprocessed and adopted in this study. Secondly, emotion-related brain functional connection pattern is constructed using Phase-Locking Value (PLV) adjacency matrix to measure connectivity between the signals of different EEG channels according to phase synchronization. A novel graph structure is constructed where the EEG electrode channels are defined as the vertex, and the edge is strong connection of the binary brain network. Thirdly, the GERBN model that includes six layers is designed to classify and recognize emotional states on the two-dimensional emotional models of valence and arousal. Finally, extensive experiments are conducted on DEAP and SEED datasets. Experimental results demonstrate that the proposed method can improve classification accuracies, in which average accuracies of 80.43% and 88.47% on DEAP are attained on valence and arousal dimensions, respectively. On the SEED dataset, the accuracy reaches 92.37% higher than some of the other methods.
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基于图卷积神经网络与大脑功能连接网络的情感识别
情绪识别在人机交互(HCI)中发挥着重要作用,而基于情绪状态的人类行为评估也是一个重要的研究课题。情绪识别的目的是通过分析生理或非生理信号自动识别人的情绪状态。传统的情绪分类方法无法全面利用从受到刺激后产生的脑电图(EEG)信号中提取的全局和局部特征。因此,我们提出了基于图卷积神经网络与脑功能连接网络(GERBN)的情感识别方法。首先,本研究预处理并采用了公开的 DEAP 和 SEED 数据集的原始脑电图数据。其次,利用相位锁定值(PLV)邻接矩阵构建与情绪相关的脑功能连接模式,根据相位同步性测量不同脑电图通道信号之间的连接性。我们构建了一个新颖的图结构,其中脑电图电极通道被定义为顶点,而边则是二元脑网络的强连接。第三,设计了包含六层的 GERBN 模型,用于在二维情绪模型(情绪价值和唤醒)上对情绪状态进行分类和识别。最后,在 DEAP 和 SEED 数据集上进行了大量实验。实验结果表明,所提出的方法可以提高分类准确率,其中在 DEAP 数据集上,情绪维度和唤醒维度的平均准确率分别达到了 80.43% 和 88.47%。在 SEED 数据集上,准确率达到 92.37%,高于其他一些方法。
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来源期刊
International Journal of Crowd Science
International Journal of Crowd Science Decision Sciences-Decision Sciences (miscellaneous)
CiteScore
2.70
自引率
0.00%
发文量
20
审稿时长
24 weeks
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