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Multimodal insights into diverse pain experiences: PhysioPain dataset
Boran Toktay1, İkbal Işık Orhan1, Elif Yıldırım2
1Department of Computer Engineering, Istanbul Kültür University, Istanbul, Türkiye.
Data in Brief
|September 8, 2025
Summary
The PhysioPain dataset integrates physiological signals like EEG and EDA with subjective pain reports. This comprehensive approach aims to improve pain estimation and classification for various pain types.
Area of Science:
- Biomedical Engineering
- Neuroscience
- Pain Research
Background:
- Pain assessment is complex, often relying on subjective reports.
- Objective physiological measures can offer deeper insights into pain experiences.
Purpose of the Study:
- To introduce the PhysioPain dataset, a multimodal resource for pain research.
- To enable sophisticated pain representation by combining physiological and subjective data.
- To facilitate advancements in pain estimation and classification.
Main Methods:
- Collected real-time physiological data from 93 participants, including electroencephalogram (EEG), skin temperature, electrodermal activity (EDA), blood volume pulse (BVP), and accelerometer data.
- Integrated objective physiological data with subjective information from the McGill questionnaire and custom surveys.
- Covered diverse pain conditions: no pain, headache, menstrual cycle pain, and back/neck/waist pain.
Main Results:
- The PhysioPain dataset provides a rich, multimodal collection of pain-related data.
- The dataset enables comprehensive analysis of pain intensity and character.
- It supports the development of advanced pain assessment tools.
Conclusions:
- The PhysioPain dataset offers a novel perspective on pain intensity.
- Multimodal data integration enhances our understanding of complex pain experiences.
- This resource is valuable for pain estimation, classification, and observation.
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