混合注意力机制下胶囊网络的脑电情绪识别方法
作者单位:

南京信息工程大学

基金项目:

国家重点研发计划项目 面向5G通信系统GaN功率器件电磁干扰关键技术研究(2022YFE0122700)


EEG-based emotion recognition using capsule network with hybrid attention mechanism
Author:
Affiliation:

Nanjing University of Information Science and Technology

Fund Project:

National Key Research and Development Program of China

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    摘要:

    为了充分提取多通道EEG信号的频率信息和空间拓扑信息,提出了一种混合注意力机制下胶囊网络的脑电情绪识别模型——CBAM-CapsNet。首先,获取不同频带的脑电信号并提取其微分熵特征;然后,将该特征映射到根据导联空间分布的三维紧凑型特征矩阵中;最后,将三维特征矩阵通过带有混合注意力机制的胶囊网络来进行模型训练和预测。实验结果表明,高频带对于情绪识别影响更大,且使用四频带三维矩阵可以显著提高情绪识别准确率。CBAM-CapsNet在DEAP数据集Arousal和Valence上的二分类识别准确率为95.42%和95.52%,在DEAP数据集上Arousal_Valence联合四分类准确率为95.00%,在SEED数据集上三分类准确率为93.19%。相比现有主流基于深度学习的脑电情绪识别模型,准确率显著提升。

    Abstract:

    To fully extract the frequency information and spatial topology information of multi-channel EEG signals, this paper introduces an EEG emotion recognition model——CBAM-CapsNet, which utilizes a capsule network with a hybrid attention module. Firstly, EEG signals from different frequency bands are acquired, and their differential entropy features are extracted. Secondly, these features are mapped into a three-dimensional compact feature matrix based on spatial lead distribution. Finally, the three-dimensional feature matrix is processed through a capsule network with a hybrid module (CBAM-CapsNet) for training and prediction. Experimental results indicate that higher frequency bands have a more significant impact on emotion recognition, and the use of four-frequency band three-dimensional matrix can significantly enhance the accuracy of emotion recognition. CBAM-CapsNet achieved a binary classification accuracy of 95.42% and 95.52% for Arousal and Valence, respectively, on the DEAP dataset, a four-class accuracy of 95.00% for Arousal_Valence combined on the DEAP dataset, and a three-class accuracy of 93.19% on the SEED dataset. Compared to existing mainstream brain emotion recognition models based on deep learning, the accuracy is significantly improved.

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陈继鑫,朱艳萍,万发雨,陈铖,陈家楠,张慕林.混合注意力机制下胶囊网络的脑电情绪识别方法[J].南京信息工程大学学报,,():

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  • 收稿日期:2024-04-25
  • 最后修改日期:2024-07-08
  • 录用日期:2024-07-09

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