Boundary-aware Prototype-driven Adversarial Alignment for Cross-Corpus EEG Emotion Recognition

TL;DR

This paper introduces a boundary-aware prototype-driven adversarial framework for cross-corpus EEG emotion recognition, improving generalization across heterogeneous datasets by refining class-conditional alignment and decision boundaries.

Contribution

It proposes a unified, relation-driven adversarial architecture with prototype-guided alignment, contrastive regularization, and boundary-aware classifiers for robust cross-corpus EEG emotion recognition.

Findings

Achieves state-of-the-art performance on SEED, SEED-IV, and SEED-V datasets.

Demonstrates significant improvements in cross-corpus evaluation protocols.

Effectively generalizes to clinical depression identification scenarios.

Abstract

Electroencephalography (EEG)-based emotion recognition suffers from severe performance degradation when models are transferred across heterogeneous datasets due to physiological variability, experimental paradigm differences, and device inconsistencies. Existing domain adversarial methods primarily enforce global marginal alignment and often overlook class-conditional mismatch and decision boundary distortion, limiting cross-corpus generalization. In this work, we propose a unified Prototype-driven Adversarial Alignment (PAA) framework for cross-corpus EEG emotion recognition. The framework is progressively instantiated in three configurations: PAA-L, which performs prototype-guided local class-conditional alignment; PAA-C, which further incorporates contrastive semantic regularization to enhance intra-class compactness and inter-class separability; and PAA-M, the full boundary-aware configuration that integrates dual relation-aware classifiers within a three-stage adversarial optimization scheme to explicitly refine controversial samples near decision boundaries. By combining prototype-guided subdomain alignment, contrastive discriminative enhancement, and boundary-aware aggregation within a coherent adversarial architecture, the proposed framework reformulates emotion recognition as a relation-driven representation learning problem, reducing sensitivity to label noise and improving cross-domain stability. Extensive experiments on SEED, SEED-IV, and SEED-V demonstrate state-of-the-art performance under four cross-corpus evaluation protocols, with average improvements of 6.72\%, 5.59\%, 6.69\%, and 4.83\%, respectively. Furthermore, the proposed framework generalizes effectively to clinical depression identification scenarios, validating its robustness in real-world heterogeneous settings. The source code is available at \textit{https://github.com/WuCB-BCI/PAA}