We introduce FEEL, a benchmarking study evaluating 19 emotion datasets based on physiological signals, uncovering key insights into their generalizability and cross-dataset transferability.

Multi-dataset joint pre-training with covariance centroid alignment enables cross-dataset generalization of EEG-based emotion recognition.