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基于EEG-fNIRS的情绪识别使用图形卷积和囊注意力网络

Guijun Chen¹, Yue Liu¹, Xueying Zhang¹

¹College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

|August 29, 2024

概括

这项研究引入了一种新的图形卷积和囊注意力网络 (GCN-CA-CapsNet),用于使用脑电图 (EEG) 和功能近红外光谱 (fNIRS) 数据改进情绪识别. 该模型增强了特征融合和学习,精度提高了3-11%.

科学领域:

神经科学是一个神经科学.
机器学习机器学习
生物医学工程生物医学工程

背景情况:

脑电图 (EEG) 和功能近红外光谱 (fNIRS) 是评估情绪状态的客观测量方法,在情绪识别研究中至关重要.
从合并的EEG-fNIRS数据中有效的特征融合和区分学习仍然是提高情绪识别准确性的重大挑战.

研究的目的:

提出和验证一个新的图形卷积和囊注意力网络 (GCN-CA-CapsNet) 模型,以使用多式EEG-fNIRS数据改进情绪识别.
为应对功能融合和学习在多式联机大脑计算机接口对情感计算的挑战.

主要方法:

从50名暴露于情绪视频刺激的受试者收集了同时的EEG-fNIRS信号.
使用Pearson相关性邻矩阵的图形卷积,将EEG-fNIRS特征合并到初级囊中.
利用囊注意力模块和动态路由来选择高质量的囊并生成强大的分类囊.

主要成果:

拟议的 GCN-CA-CapsNet 模型与现有的最先进的方法相比,表现出更高的性能.
废除研究验证了GCN-CA-CapsNet方法在定制情绪EEG-fNIRS数据集上的有效性.
该方法在情绪识别方面平均准确度增加了3-11%.

结论:

该GCN-CA-CapsNet模型有效地整合了EEG和fNIRS数据,以增强情绪识别.
提出的注意力机制和图形卷积方法显著改善了歧视性特征学习.
这项工作在多式联机脑电脑接口方面提供了有前途的进展,用于情感计算和情感识别.

关键词:

囊注意力网络的注意力网络电脑电图 (EEG) 是一种电脑电图.情感识别情感识别情感识别功能近红外光谱学 (fNIRS) 是一种图形卷积网络的图形卷积网络.

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