Region-aware Spatiotemporal Modeling with Collaborative Domain Generalization for Cross-Subject EEG Emotion Recognition

2026-01-22

AI Summary

Cross-subject EEG-based emotion recognition faces significant challenges due to inter-subject variability in signal distributions and the spatiotemporal complexity of neural representations of emotion. To address these issues, this work proposes a unified framework that integrates region-level spatial modeling informed by functional brain area priors with multi-scale temporal modeling to capture the dynamic evolution of emotional states. For the first time, a collaborative domain generalization mechanism is introduced to jointly optimize cross-subject comparability, dynamic representation learning, and mitigation of subject-specific biases—all under the challenging setting of completely unseen target subjects. Evaluated on the SEED benchmark datasets, the proposed approach substantially outperforms existing methods, demonstrating enhanced generalization performance and robustness in cross-subject emotion recognition.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Cross-subject EEG-based emotion recognition (EER) remains challenging due to strong inter-subject variability, which induces substantial distribution shifts in EEG signals, as well as the high complexity of emotion-related neural representations in both spatial organization and temporal evolution. Existing approaches typically improve spatial modeling, temporal modeling, or generalization strategies in isolation, which limits their ability to align representations across subjects while capturing multi-scale dynamics and suppressing subject-specific bias within a unified framework. To address these gaps, we propose a Region-aware Spatiotemporal Modeling framework with Collaborative Domain Generalization (RSM-CoDG) for cross-subject EEG emotion recognition. RSM-CoDG incorporates neuroscience priors derived from functional brain region partitioning to construct region-level spatial representations, thereby improving cross-subject comparability. It also employs multi-scale temporal modeling to characterize the dynamic evolution of emotion-evoked neural activity. In addition, the framework employs a collaborative domain generalization strategy, incorporating multidimensional constraints to reduce subject-specific bias in a fully unseen target subject setting, which enhances the generalization to unknown individuals. Extensive experimental results on SEED series datasets demonstrate that RSM-CoDG consistently outperforms existing competing methods, providing an effective approach for improving robustness. The source code is available at https://github.com/RyanLi-X/RSM-CoDG.

Problem

• cross-subject EEG emotion recognition
• inter-subject variability
• spatiotemporal modeling
• domain generalization
• distribution shift

Innovation

• Region-aware modeling
• Spatiotemporal modeling
• Collaborative domain generalization
• Cross-subject EEG emotion recognition
• Neuroscience priors