Toktay Boran, Orhan İkbal Işık, Yıldırım Elif, Akbulut Fatma Patlar, Catal Cagatay
Department of Computer Engineering, Istanbul Kültür University, Istanbul, Türkiye.
Department of Computer Engineering, Istanbul Technical University, Istanbul, Türkiye.
Data Brief. 2025 Aug 19;62:111992. doi: 10.1016/j.dib.2025.111992. eCollection 2025 Oct.
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.
无痛、头痛、月经周期疼痛以及背部/颈部/腰部疼痛，旨在寻找一种复杂而完整的疼痛表征方法。该研究包括99名个体，其中93名参与者提供了实时生理数据。这些参与者经历了实验过程，以收集包括脑电图（EEG）、皮肤温度、皮肤电活动（EDA）、血容量脉搏（BVP）和加速度计数据在内的实时生理数据。将客观生理数据与通过使用麦吉尔问卷和定制问题进行的调查所获得的主观信息相结合，产生了一个适合与疼痛估计、疼痛分类以及其他疼痛观察方法相关任务的完整数据集。这种方法旨在为疼痛强度提供一个新的视角，并更全面地了解疼痛体验的复杂特性。
