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Singh 2026 — FEEL: Quantifying Heterogeneity in Physiological Signals for Generalizable Emotion Recognition

Citation: Singh, P., Gupta, A., Jalan, S., et al. (2026). arXiv:2604.05926.
DOI: 10.48550/arxiv.2604.05926
Type: Benchmark paper (arXiv preprint)

Key Contribution

First large-scale multi-dataset benchmark for EDA + PPG emotion recognition. Unified evaluation of 16 architectures across 19 datasets. Provides the FEEL framework for standardized benchmarking.

Key Findings

Result Value
Best architecture CLSP (contrastive signal-language pretraining)
Simple models competitive RF, LDA, MLP in 36/114 top scores
Features vs raw Handcrafted features consistently beat raw signals
Real-life → lab transfer F1 = 0.79
Lab → E4 transfer F1 = 0.73

Relevance

Pre-built evaluation pipeline. Can directly run Meltdown Minder feature sets through the FEEL framework for standardized benchmarking.

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