Multimodal insights into diverse pain experiences: PhysioPain dataset

Boran Toktay, İkbal Işık Orhan, Elif Yıldırım, Fatma Patlar Akbulut, Cagatay Catal*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

PhysioPain dataset comprises several physiological data of different kinds of pain: no pain, headache, menstrual cycle pain and back/neck/waist pain in search of a sophisticated and complete approach to pain representation. The study comprised 99 individuals, of whom 93 participants contributed real-time physiological data. These participants underwent experiment process to gather real-time physiological data including electroencephalogram (EEG), skin temperature, electrodermal activity (EDA), blood volume pulse (BVP), and accelerometer data. Combining objective physiological data with subjective information acquired by the survey using the McGill questionnaire and customized questions produces a complete dataset fit for the tasks related to pain estimate, pain classification, and other approaches to pain observation. This method seeks to offer a fresh viewpoint on pain intensity and catch a more complete knowledge of the intricate character of pain experiences.

Original languageEnglish
Article number111992
JournalData in Brief
Volume62
DOIs
Publication statusPublished - Oct 2025

Bibliographical note

Publisher Copyright:
© 2025 The Authors

Keywords

  • Biosignals
  • Multimodal pain assessment
  • Pain classification
  • Pain intensity estimation
  • Physiological signal analysis

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