Emotion Recognition From Physiological Responses: Accessibility and Usability of Publicly Available Datasets

Emotion recognition from biological signals provides valuable complementary information that may support clinical monitoring, symptom tracking, and assessment of affective processes, although it is not used as an independent diagnostic method. Despite its potential, dataset usability is hindered by challenges in accessibility, licensing, and inclusion of neutral stimuli. Existing datasets primarily focus on either AI training or eliciting emotional responses, both crucial for advancing the field. This review systematically analyzes current datasets and offers recommendations for their improvement. Using the PRISMA methodology, six databases were searched with predefined keywords, yielding 2,679 records. After filtering, 43 studies were included. Datasets were assessed based on four criteria: inclusion of neutral stimuli, downloadability, licensing for commercial use, and emotion assessment methods. Analyzed modalities include visual, auditory, physiological, and multimodal datasets. Methodological rigor determined their relevance to emotion recognition research. Findings reveal significant limitations in dataset usability, such as restrictions to academic use, inconsistent inclusion of neutral stimuli, and variability in assessment methods. Multimodal datasets show promise but require refinement for real-time applications in clinical and technological contexts. This review highlights the urgent need for open-access datasets and standardized methodologies to drive innovation and practical applications in emotion recognition technologies.