用于脑电情绪识别的三子空间解耦聚类图神经网络研究

黄辰; 马浩博; 张龑; 杨超; 宋建华

doi:10.12263/DZXB.20250555

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用于脑电情绪识别的三子空间解耦聚类图神经网络研究

学术论文 | 更新时间：2026-02-10

- 用于脑电情绪识别的三子空间解耦聚类图神经网络研究
- Research on Tri-Subspace Decoupling Clustering Graph Neural Network for EEG-Based Emotion Recognition
- 电子学报 2025年53卷第11期页码：4065-4076
- 作者机构：
  
  1.湖北大学计算机学院，湖北武汉 430062
  2.智能感知系统与安全教育部重点实验室，湖北武汉 430062
  3.大数据智能分析与行业应用湖北省重点实验室，湖北武汉 430062
  4.湖北省高校人文社科重点研究基地-绩效评价信息管理研究中心，湖北武汉430062
  5.湖北大学网络空间安全学院，湖北武汉 430062
- 作者简介：
  
  [ "黄辰男，1983年生，福建龙岩人.湖北大学计算机学院教授.主要研究方向为人工智能、脑机接口.E-mail: huang@hubu.edu.cn" ]
  [ "马浩博男，2000年生，河北秦皇岛人.湖北大学计算机学院硕士研究生.主要研究方向为人工智能、脑科学、情感分析.E-mail: 202321116012629@stu.hubu.edu.cn" ]
  [ "张龑男，1974年生，湖北宜昌人.湖北大学计算机学院教授.主要研究方向为信息安全、大数据分析.中国电子学会会员编号：E190197582M.E-mail: zhangyan@hubu.edu.cn" ]
  [ "杨超男，1982年生，湖北武汉人.湖北大学计算机学院教授.主要研究方向为智能计算、信息安全等.E-mail: stevenyc@hubu.edu.cn" ]
  [ "宋建华女，1973年生，湖北襄阳人.湖北大学网络空间安全学院教授.主要研究方向为网络与信息安全.E-mail: sjhhubu@126.com" ]
- 基金信息：
- DOI：10.12263/DZXB.20250555
  中图分类号： TP391;TP399
- 收稿：2025-06-26，
  
  录用：2025-09-25，
  
  纸质出版：2025-11-25
- 稿件说明：
移动端阅览
黄辰, 马浩博, 张龑, 等. 用于脑电情绪识别的三子空间解耦聚类图神经网络研究[J]. 电子学报, 2025, 53(11): 4065-4076.

HUANG Chen, MA Hao-bo, ZHANG Yan, et al. Research on Tri-Subspace Decoupling Clustering Graph Neural Network for EEG-Based Emotion Recognition[J]. Acta Electronica Sinica, 2025, 53(11): 4065-4076.
黄辰, 马浩博, 张龑, 等. 用于脑电情绪识别的三子空间解耦聚类图神经网络研究[J]. 电子学报, 2025, 53(11): 4065-4076. DOI：10.12263/DZXB.20250555

HUANG Chen, MA Hao-bo, ZHANG Yan, et al. Research on Tri-Subspace Decoupling Clustering Graph Neural Network for EEG-Based Emotion Recognition[J]. Acta Electronica Sinica, 2025, 53(11): 4065-4076. DOI：10.12263/DZXB.20250555

摘要

图神经网络（Graph Neural Networks，GNNs）因其能够建模大脑区域间的时空依赖关系并捕捉上下文感知的神经模式，在基于脑电图（ElectroEncephaloGraphy，EEG）的情感识别中得到了广泛关注.然而，大多数基于GNN的EEG情感识别方法面临两个主要挑战：（1）许多现有模型未能考虑局部大脑区域间情感的共性和多样性，导致空间或功能相邻区域的节点嵌入过于同质化；（2）当前的方法通常依赖于简单的拼接或基于相关性的先验知识，这对于捕捉多个EEG通道和频带间复杂且分布的情感模式是不充分的.本文提出了一种三子空间解耦聚类图神经网络（Tri-Subspace-Decoupled Clustering Graph Neural Network，TS-DCGNN）来解决上述挑战.具体来说，TS-DCGNN将EEG信号解耦为三个子空间：显性情感子空间、隐性情感子空间和显性-隐性共振子空间，旨在捕捉可观察的情感体验（如“快乐”）、自动反应（如“惊吓”）及其耦合.此外，本文引入了一种双分支传播架构，其中图注意力网络（Graph Attention Networks，GATs）和图卷积网络（Graph Convolutional Networks，GCNs）并行操作，通过注意力驱动的交互和分层学习提取显性和隐性特征，从而增强区域情感表示.进一步地，本文提出了一个统一的表示学习模块，整合这些特征，并运用信息论方法来获得最小、充分和具有辨识度的情感表示.三个基准数据集上的实验表明，所提方法在性能上达到了最先进的水平，并提升了可解释性.

Abstract

Graph neural networks (GNNs) have gained significant attention in electroencephalography (EEG)-based emotion recognition for their ability to model spatial-temporal dependencies across brain regions and capture context-aware neural patterns. However

most GNN-based EEG emotion recognition methods encounter two primary challenges: (1) Many existing models fail to account for the emotional commonality and diversity across local brain regions

resulting in overly homogeneous node embeddings for spatially or functionally adjacent regions; (2) Current approaches often rely on simple concatenation or correlationbased priors

which are inadequate for capturing the complex and distributed emotional patterns across multiple EEG channels and frequency bands. In this paper

we propose a tri-subspace decoupling clustering graph neural network (TS-DCGNN) to address the above challenges. Specifically

TS-DCGNN decouples EEG signals into three subspaces: the explicit emotional

implicit emotional

and explicit-implicit resonance subspaces

aiming to capture observable experiences (e.g.

“happiness”)

automatic responses (e.g.

“startle”)

and their coupling. Moreover

we introduce a dual-branch propagation architecture where graph attention networks (GATs) and graph convolutional networks (GCNs) operate in parallel to extract explicit and implicit features via attention-driven interaction and hierarchical learning. This enhances regional emotional representations. Furthermore

we present a unified representation learning module that integrates these features and employs information theory to obtain a minimal

sufficient

and discriminative emotional representation. Experiments on three benchmark datasets demonstrate state-of-the-art performance and improved interpretability.

关键词

Keywords

references

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