基于脑电网络图特征的情绪识别研究
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作者单位:

1.电子科技大学生命科学与技术学院神经信息教育部重点实验室,成都 611731;2.重庆邮电大学生物信息学院,重庆 400065

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基金项目:

国家自然科学基金(U19A2082, 61961160705, 61901077);国家重点研发计划(2017YFB1002501)。


Emotion Recognition Based on Graph Features Extracted from EEG Networks
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Affiliation:

1.MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731,China;2.School of Bioinfomatics, Chongqing University of Posts and Telecommunications, Chongqing 400065,China

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    摘要:

    针对情绪脑电信号提出一种网络图特征学习与情绪识别算法。首先,利用情绪脑电数据构建对应的情绪脑电网络;其次,在由情绪脑电网络尺度定义的高维空间构建脑电网络样本间的局部邻接关系图以挖掘样本集的分布特性,进而得到样本集的图拉普拉斯矩阵;在此基础上,进一步利用谱图理论对情绪脑电网络的最优低维空间映射进行求解,在保留原始样本局部邻接关系的前提下实现对情绪脑电网络的降维与重新表达,并将每个情绪脑电网络样本表示成1组脑电网络特征集;最后利用提取到的情绪脑电网络特征集,结合支持向量机分类学习算法,针对情绪识别任务进行识别模型的训练和学习,实现对情绪状态的准确解码与识别。在国际公开情绪脑电数据集的实验结果表明:相较于传统情绪识别算法,本文所提方法能有效提升情绪识别准确率,在基于公开数据集的多类情绪识别任务中分别达到91.85%(SEED数据集, 3类)、79.36%(MAHNOB-HCI数据集,3类)和79%(DEAP数据集,4类)的稳健识别效果。

    Abstract:

    To accurately evaluate individual emotional states, we propose a graph feature learning and recognition algorithm for electroencephalogram(EEG)-based emotion recognition. In the proposed algorithm, the original EEG data are first used to construct the corresponding EEG network. And then, the local adjacency graph between different emotional EEG network samples is constructed in the high-dimensional EEG brain network space, which aims to capture the distribution of the emotional EEG brain networks, and the graph Laplacian matrix can be estimated with the adjacency graph. Thirdly, the optimal low-dimensional graph embeddings of emotional EEG brain networks are obtained by the spectral graph theory, and the emotional EEG brain network samples can be represented in the low-dimensional space, in which the initial emotional EEG brain networks can be represented with a set of network features. Finally, based on the extracted emotional EEG brain network features, the optimal support vector machine classifier is trained and utilized in the emotion recognition. The verification experiment is carried out on the international public emotional EEG datasets, and experimental results show that compared with traditional emotion recognition algorithms, the proposed method can effectively improve the accuracy of emotion recognition, and achieve a robust recognition effect of 91.85% (SEED dataset, 3-class), 79.36% (MAHNOB-HCI dataset, 3-class) and 79% (DEAP dataset, 4-class) on three public datasets, respectively.

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李存波,杨蕾,陈昭瑾,汪义锋,李沛洋,李发礼,尧德中,徐鹏.基于脑电网络图特征的情绪识别研究[J].数据采集与处理,2023,38(4):815-823

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  • 收稿日期:2022-05-05
  • 最后修改日期:2022-10-13
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  • 在线发布日期: 2023-07-25