Zhao Xiangyue, Zhou Jingyao, Zhang Libo, Zhuang Yun, Duan Haoqing, Wei Shiyu, Yao Suchen, Lu Xuejing, Bi Yanzhi, Hu Li
State Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.
Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
Sci Data. 2025 Sep 2;12(1):1536. doi: 10.1038/s41597-025-05900-1.
Understanding the neural mechanisms of pain is key to the development of novel pain diagnostic and treatment strategies. Here, we present a large-scale, comprehensive electroencephalogram (EEG) dataset of laser-evoked potentials (LEPs) from 678 healthy participants. This dataset comprises high-density EEG recordings and single-trial self-reported pain ratings in response to nociceptive laser stimuli of varying intensities (from 2.5 J to 4.5 J) delivered to either the left or right dorsum of the hand, without any physical or psychological interventions. As the largest nociceptive-evoked EEG dataset to date, it can serve as a valuable resource for mechanistic pain studies such as investigating electrophysiological underpinnings of within-individual and between-individual pain variations, for clinical studies such as providing normal values of LEPs to assess the possible lesions of pain pathways, and for methodological innovations in EEG signal processing such as optimizing preprocessing pipelines and developing new analytical tools.
了解疼痛的神经机制是开发新型疼痛诊断和治疗策略的关键。在此，我们展示了一个来自678名健康参与者的大规模、全面的激光诱发电位（LEP）脑电图（EEG）数据集。该数据集包括高密度脑电图记录以及针对不同强度（2.5焦耳至4.5焦耳）的伤害性激光刺激（施加于左手或右手背部）的单次试验自我报告疼痛评分，且未进行任何物理或心理干预。作为迄今为止最大的伤害性诱发脑电图数据集，它可作为一个有价值的资源，用于疼痛机制研究，如研究个体内部和个体之间疼痛差异的电生理基础；用于临床研究，如提供LEP的正常值以评估疼痛通路的可能病变；以及用于脑电图信号处理的方法创新，如优化预处理流程和开发新的分析工具。
