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Bridging the Gap in ECG-Based Emotion Recognition: A Unified Evaluation of Deep Learning Models

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

Deep learning has led to numerous proposed architectures for Automated Emotion Recognition (AER) from electrocardiogram (ECG) data, but inconsistencies in preprocessing, training, and evaluation make direct comparisons difficult. Most studies train and validate models on individual datasets collected under homogeneous conditions, limiting variability and raising concerns about generalizability. Cross-dataset validation is sometimes used but primarily assesses model adaptability rather than true generalization. This study presents a comparative analysis of prominent deep learning architectures

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