不确定性原型学习的跨域情绪识别方法

doi:10.16451/j.cnki.issn1003-6059.202511002

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Abstract

Electroencephalogram(EEG)-based emotion recognition attracts increasing attention due to its broad applications in mental health monitoring and brain-computer interfaces. However, existing methods still suffer from inadequate utilization of electrode spatial information, limited labeled data, and cross-domain distribution shifts. To address these issues, an uncertainty-aware prototypical learning method for cross-domain emotion recognition is proposed. First, a position encoding-guided graph semi-supervised module is designed. The spatial topology is embedded into the adjacency matrix through incorporating sine-cosine positional encoding. Thus, the limitation of traditional graph constructions based solely on physical distance is broken through, and physiological priors of EEG signals are effectively integrated. Based on the above, the graph-based feature propagation mechanism is leveraged to collaboratively learn deep topological representations from both labeled and unlabeled samples, mitigating the challenge of label scarcity. Second, an uncertainty-aware prototype learning module is constructed to dynamically refine emotion prototypes by quantifying feature reliability. Thereby, noise and cross-domain shifts are suppressed while generalization ability of the model across different domains is enhanced. Experiments on multiple public EEG emotion datasets demonstrate that the proposed method outperforms state-of-the-art methods on cross-domain recognition tasks.

Key words： Graph Neural Networks Emotion Recognition Deep Learning Semi-supervised Learning Prototype Learning

Received: 04 October 2025

ZTFLH:

TP181

Fund:

Supported by National Natural Science Foundation of China(No.62536006), Natural Science Foundation of Zhejiang Province(No.LQN26F030010,LZ26F030003)

Corresponding Authors: CAO Feilong, Ph.D., professor. His research interests include deep learning and image processing.

About author:: WANG Yilin, Master student. Her re-search interests include deep learning and affec-tive computing.
YE Hailiang, Ph.D., associate professor. His research interests include deep learning and image processing.
GUO Wenhui, Ph.D., lecturer. Her re-search interests include deep learning and affec-tive computing.

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Cite this article:

WANG Yilin,YE Hailiang,GUO Wenhui等. Uncertainty-Aware Prototypical Learning Method for Cross-Domain Emotion Recognition[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(11): 974-985.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202511002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I11/974

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