计算机工程与应用2025，Vol.61Issue(22)：205-214,10.DOI:10.3778/j.issn.1002-8331.2501-0094
多自监督学习任务结合图神经网络的EEG情感识别
EEG Emotion Recognition with Multi-Self-Supervised Learning Tasks Combined with Graph Neural Networks
摘要
Abstract
To address the issues of the insufficient model generalization due to label scarcity and the overfitting tendency of single-task self-supervised learning in electroencephalogram(EEG)emotion recognition,this paper proposes a graph neural network model based on EEG frequency-domain features,within a self-supervised multi-task learning framework to enhance representation learning and emotion recognition.Differential entropy features are extracted from EEG data and represented as graph structures.Multi-task learning is performed through four self-supervised tasks:channel masking,fre-quency masking,spatial jigsaw,and frequency jigsaw.A Chebyshev graph neural network is utilized to extract deep-level features,where the channel masking and frequency masking tasks compute losses via a reconstruction module,while the spatial jigsaw and frequency jigsaw tasks compute losses via a classification module.After training,the feature extractor parameters are frozen and applied to downstream emotion recognition tasks.Experimental results on the SEED and DEAP datasets demonstrate that the subject-dependent emotion classification accuracy reaches 89.87%(three-class)on the SEED dataset,while on the DEAP dataset,the binary classification accuracies for the arousal and valence dimensions are 88.03%and 89.70%,respectively.For subject-independent scenarios,the accuracies are 72.03%on the SEED dataset and 65.38%and 61.29%on the DEAP dataset.These results indicate that the proposed method effectively improves classification performance,mitigates overfitting,and outperforms existing approaches.关键词
脑电信号/情感识别/图神经网络/自监督学习/多任务Key words
electroencephalogram/emotion recognition/graph neural network/self-supervised learning/multi-task分类
计算机与自动化引用本文复制引用
陈景霞,李小池,王倩,张鹏伟..多自监督学习任务结合图神经网络的EEG情感识别[J].计算机工程与应用,2025,61(22):205-214,10.基金项目
国家自然科学基金(61806118) （61806118）
陕西科技大学博士科研启动基金(2020BJ-30). （2020BJ-30）
