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| license: mit |
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| # Pre-trained Models for EDA, PPG, and EDA+PPG |
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| This repository provides pre-trained CLSP models for the EEVR dataset and the FEEL benchmarking framework. |
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| If you use these models in your research, please cite the following papers: |
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| EEVR (NeurIPS 2024) |
| https://proceedings.neurips.cc/paper_files/paper/2024/hash/1cba8502063fab9df252a63968691768-Abstract-Datasets_and_Benchmarks_Track.html |
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| FEEL (NeurIPS 2025) |
| Quantifying Heterogeneity in Physiological Signals for Generalizable Emotion Recognition |
| https://neurips.cc/virtual/2025/loc/san-diego/poster/121715 |
| Model Details |
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| The released models support the following physiological modalities: |
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| Electrodermal Activity (EDA), Photoplethysmography (PPG), Multimodal (EDA + PPG) |
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| They are trained for: |
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| 2-class classification (arousal, valence), 4-class classification (Russell's (1980) Circumplex Models) |
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| Usage: |
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| You can load and experiment with these models using the accompanying code and resources available in the original repositories (see below). |
| Note: The 2-class results reported in the original EEVR paper were obtained using Leave-One-Subject-Out (LOSO) cross-validation. For the FEEL work, the 2-class models were retrained on the full EEVR dataset. |
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| References: |
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| EEVR Repository: https://github.com/alchemy18/EEVR |
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| FEEL Repository: https://github.com/alchemy18/FEEL |
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| license: mit |
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