Fine-grained label propagation via density-based prototype matching for cross-subject EEG emotion recognition

Emotion recognition based on electroencephalography (EEG) signals has become a prominent research focus in affective computing. However, challenges such as individual differences and label noise have significantly impeded the generalization and accuracy of models. To address these challenges, this study proposes a novel fine-grained label propagation framework based on Density-Based Prototype Matching (DBPM). By leveraging density-based clustering to capture fine-grained subdomain structures, the framework enables robust prototype matching and reliable label propagation across domains. Furthermore, a Sequential Multi-Source Training Strategy is devised to progressively incorporate multiple source domains, thereby ensuring stable one-to-one prototype matching and mitigating inter-source interference. Extensive experiments are conducted on two publicly available EEG emotion datasets (SEED and SEED-IV) under a leave-one-subject-out cross-validation evaluation protocol. The results demonstrate that the proposed DBPM achieves state-of-the-art performance, offering a promising solution for addressing individual differences and label noise in EEG emotion recognition. The source code is publicly available at: https://github.com/qwangwl/DBPM