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Nociception

Nociception

Nociception—the ability to feel pain—is essential for an organism’s survival and overall well-being. Noxious stimuli such as piercing pain from a sharp object, heat from an open flame, or contact with corrosive chemicals are first detected by sensory receptors, called nociceptors, located on nerve endings. Nociceptors express ion channels that convert noxious stimuli into electrical signals. When these signals reach the brain via sensory neurons, they are perceived as pain.

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相关实验视频

Updated: Jan 14, 2026

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

应用机器学习用于使用EEG光谱特征进行感知性疼痛检测.

Rogelio Sotero Reyes-Galaviz¹, Luis Villaseñor-Pineda², Camilo E Valderrama^3,4

¹Department of Biomedical Sciences and Technologies, INAOE, Puebla, Mexico.

Biomedical physics & engineering express

|October 23, 2025

概括

这项研究表明,脑电图 (EEG) 结合机器学习可以预测疼痛. 反应时间,而不仅仅是刺激强度,是准确测量主观疼痛感知的关键.

关键词:

电脑脑电图 (EEG) 是一种电脑电图.频段频段频段频段频段频段机器学习是机器学习.无感觉性疼痛是一种疼痛感.信号处理信号处理信号处理

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Electrophysiological Measurement of Noxious-evoked Brain Activity in Neonates Using a Flat-tip Probe Coupled to Electroencephalography

Published on: November 29, 2017

Open-Source Real-Time Closed-Loop Electrical Threshold Tracking for Translational Pain Research

Open-Source Real-Time Closed-Loop Electrical Threshold Tracking for Translational Pain Research

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Last Updated: Jan 14, 2026

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

Electrophysiological Measurement of Noxious-evoked Brain Activity in Neonates Using a Flat-tip Probe Coupled to Electroencephalography

Electrophysiological Measurement of Noxious-evoked Brain Activity in Neonates Using a Flat-tip Probe Coupled to Electroencephalography

Published on: November 29, 2017

Open-Source Real-Time Closed-Loop Electrical Threshold Tracking for Translational Pain Research

Open-Source Real-Time Closed-Loop Electrical Threshold Tracking for Translational Pain Research

Published on: April 21, 2023

科学领域:

神经科学是一个神经科学.
疼痛研究疼痛研究
生物医学工程生物医学工程

背景情况:

传统的疼痛量表有局限性,原因是疼痛耐受性的个体间变化.
客观地测量 nociceptive 疼痛是一个挑战.
脑电图 (EEG) 为客观疼痛评估提供了一个潜在的途径.

研究的目的:

开发一种更可靠的方法来使用EEG信号测量激光诱导的感知性疼痛.
为了比较数据标记策略 (反应时间与激光强度) 和EEG通道配置用于疼痛预测.
通过结合主观疼痛变化来解决固定疼痛尺度的局限性.

主要方法:

利用公开的EEG记录数据库从51名受试者接触到受控激光刺激.
从EEG信号中提取六个频段 (α,β,gamma) 的功率.
应用机器学习算法来预测疼痛水平,比较反应时间和激光强度标记,以及62频道与20频道EEG配置.

主要成果:

脑电图频段功率和机器学习以86%的准确度区分了前刺激和刺激条件.
疼痛水平分类在二进制歧视 (高与低疼痛) 中达到最多63%的准确性.
基于反应时间的标签显著优于基于强度的标签 (p < 0.001).

结论:

与刺激强度相比,反应时间是预测 nociceptive 疼痛水平的优越标记系统.
疼痛感知是主观的,仅仅依靠刺激强度进行分类可能是不可靠的.
脑电图和机器学习显示出客观,个性化的疼痛测量有前途.