Integrating Functional Connectivity and Domain Adaptation for Generalizable EEG Emotion Recognition

Recognizing emotions using EEG signals is difficult because EEG data is not stationary, has a low signal-to-noise ratio, and varies a lot between subjects. We present a new hybrid framework called CDA-GAF (Cross-Domain Adaptive Graph Attention Fusion) in this work. It combines the strengths of Graph Attention Networks (GATs), Temporal Transformers, and Domain Adaptation to make emotion classification models more robust and generalizable. To make brain connectivity graphs for each frequency band, our method first gets functional connectivity features from EEG channels. A GAT module processes these to find spatial dependencies in EEG activity. Then, a Temporal Transformer module is used to model long-range dependencies between EEG sequences. To address cross-subject variations, we implement a domain adaptation layer utilizing CORAL loss or Domain-Adversarial Training (DANN), which aligns feature distributions between source and target subjects. We also use extra emotion supervision signals, like HRV or micro-expressions, to improve the quality of the labels by anchoring the emotional state in multiple ways. We test our model on standard datasets like DEAP, SEED, and WESAD. It does much better than baseline models at recognizing emotions in both within-subject and cross-subject settings. Our findings underscore the efficacy of integrating graph-based spatial encoding, temporal attention mechanisms, and domain adaptation for emotion recognition from EEG data.