TAHAG: Two-Stage Domain Adaptation With Hybrid Adaptive Graph Learning for EEG Emotion Recognition

EEG-based emotion recognition is crucial for understanding human affective states, offering valuable insights into diverse fields like mental health monitoring and humancomputer interaction. Recent advancements in graph learning have significantly impacted EEG emotion recognition due to their ability to model the complex, dynamic relationships within brain networks. However, current methods often neglect the interplay between shared and individual correlations among EEG channels. Furthermore, individual variations in EEG patterns lead to distributional shifts that hinder the generalization of existing approaches. This paper proposes a novel Two-stage domain Adaptation with Hybrid Adaptive Graph learning (TAHAG) for EEG emotion recognition. TAHAG first employs hybrid adaptive graph learning to capture both shared and individual spatial characteristics of the EEG signals, dynamically integrating their contributions. Feature attention mechanisms are then incorporated to refine node features and enhance the model's discriminability. To address distributional variations, TAHAG utilizes a two-stage domain adaptation strategy. This strategy involves aligning the refined node features across different domains through discrepancy alignment. Subsequently, adversarial training captures domain-invariant summarized features of the entire graph. Extensive experiments on three public datasets demonstrate the superiority of TAHAG compared to existing methods. Furthermore, visualization of neuronal activity reveals significant brain regions and inter-channel relationships relevant to EEG emotion recognition.