TPC-AMDA: Augmented Multi-Source Domain Adaptation Based on TreePurgeCluster for Cross-Subject EEG Emotion Recognition

Due to substantial individual variability in EEG signals, cross-subject EEG emotion recognition often suffers from poor generalizability. Although domain adaptation is widely used, single-source domain adaptation neglects the heterogeneity among distinct source subjects, while multi-source domain adaptation suffers from domain conflicts and negative transfer effects when managing multiple heterogeneous source domains simultaneously. To address these issues, we propose an augmented multi-source domain adaptation model based on TreePurgeCluster (TPC-AMDA), which incorporates TreePurgeCluster for source domain clustering and combines augmented multisource domain adversarial learning to mitigate domain conflicts, reduce computational overhead, and improve model stability. Specifically, we first employ EEG-Mixup data augmentation to generate more diverse feature samples. Next, we propose the TreePurgeCluster method to cluster different source subjects into multiple source domains, preserving the core characteristics of each domain while reducing excessive inter-domain differences. Finally, we perform multi-source domain adaptation, aligning each source domain with the target domain to minimize domain divergence and enhance the generalization ability of the model. Experiments on three benchmark datasets validate the superior performance of our approach achieving an accuracy of 93.70 % on SEED, 80.80% on SEED-IV, and 65.01% and 70.36% on the valence and arousal dimensions of DEAP, respectively.