RS-STGCN: Regional-Synergy Spatio-Temporal Graph Convolutional Network for emotion recognition.

Decoding emotional states from electroencephalography (EEG) signals is a fundamental goal in affective neuroscience. This endeavor requires accurately modeling the complex spatio-temporal dynamics of brain activity. However, prevailing approaches for defining brain connectivity often fail to reconcile predefined neurophysiological priors with task-specific functional dynamics. This paper presents the Regional-Synergy Spatio-Temporal Graph Convolutional Network (RS-STGCN), a novel framework designed to bridge this gap. The core innovation is the Regional Synergy Graph Learner (RSGL), which integrates known physiological brain-region priors with a task-driven optimization process. It constructs a sparse, adaptive graph by modeling connectivity at two distinct levels. At the intra-regional level, it establishes core information backbones within functional areas. This ensures efficient and stable local information processing. At the inter-regional level, it adaptively identifies critical, sparse long-range connections. These connections are essential for global emotional integration. This dual-level, dynamically learned graph then serves as the foundation for the spatio-temporal network. This network effectively captures evolving emotional features. The proposed framework demonstrates superior recognition accuracy, achieving state-of-the-art results of 88.00% and 85.43% on the public SEED and SEED-IV datasets, respectively, under a strict subject-independent protocol. It also produces a neuroscientifically interpretable map of functional brain connectivity, identifying key frontal-parietal pathways consistent with established attentional networks. This work offers a powerful computational approach to investigate the dynamic network mechanisms underlying human emotion, providing new data-driven insights into functional brain organization. The code and datasets are available at https://github.com/YUNQI1014/RS-STGCN.