Coarse-to-Fine Open-Set Semantic Adaptation for EEG Emotion Recognition in 6G-Oriented Semantic Communication Systems

Electroencephalogram (EEG)-based emotion recognition has emerged as a key enabler for semantic communication systems in next-generation networks (5G-Advanced/6G), where the goal is to transmit task-relevant semantic information rather than raw signals. However, domain adaptation approaches for EEG emotion recognition typically assume closed-set label spaces and fail when unseen emotional classes arise, leading to negative transfer and degraded semantic fidelity. To address this challenge, we propose a Coarse-to-Fine Open-set Domain Adaptation (C2FDA) framework, which aligns with the semantic communication paradigm by extracting and transmitting only the emotion-related semantics necessary for task performance. C2FDA integrates a cognition-inspired spatio-temporal graph encoder with a coarse-to-fine sample separation pipeline and instance-weighted adversarial alignment. The framework distinguishes between known and unknown emotional states in the target domain, ensuring that only semantically relevant information is communicated, while novel states are flagged as unknown. Experiments on SEED, SEED-IV, and SEED-V datasets demonstrate that C2FDA achieves superior open-set adaptation performance, with average accuracies of 41.5% (SEED → SEED-IV), 42.6% (SEED → SEED-V), and 48.9% (SEED-IV → SEED-V), significantly outperforming state-of-the-art baselines. These results confirm that C2FDA provides a semantic communication-driven solution for robust EEG-based emotion recognition in 6G-oriented human–machine interaction scenarios.