Nowadays, no practical system has successfully been able to decode and predict pain in clinical settings. The inability of some patients to verbally express their pain creates the need for a tool that could objectively assess pain in these individuals. Neuroimaging techniques combined with machine learning are seen as possible candidates for the identification of pain biomarkers. This review aimed to address the potential use of electroencephalographic features as predictors of acute experimental pain. Twenty-six studies using only thermal stimulations were identified using a PubMed and Scopus search. Combinations of the following terms were used: “EEG,” “Electroencephalography,” “Acute,” “Pain,” “Tonic,” “Noxious,” “Thermal,” “Stimulation,” “Brain,” “Activity,” “Cold,” “Subjective,” and “Perception.” Results revealed that contact-heat-evoked potentials have been widely recorded over central areas during noxious heat stimulations. Furthermore, a decrease in alpha power over central regions was revealed, as well as increased theta and gamma powers over frontal areas. Gamma and theta rhythms were associated with connectivity between sensory and affective regions involved in pain processing. A machine learning analysis revealed that the gamma band is a predominant predictor of acute thermal pain. This review also addressed the need of supplementing current spectral features with techniques that allow the investigation of network dynamics.

如今，没有实用的系统能够成功地解码和预测临床环境中的疼痛。一些患者无法口头表达他们的疼痛，因此需要一种工具来客观地评估这些人的疼痛。神经影像技术与机器学习相结合被视为识别疼痛生物标志物的可能候选者。本综述旨在探讨脑电图特征作为急性实验性疼痛预测因子的潜在用途。使用 PubMed 和 Scopus 搜索确定了 26 项仅使用热刺激的研究。使用了以下术语的组合：“EEG”、“脑电图”、“急性”、“疼痛”、“补品”、“有毒”、“热”、“刺激”、“大脑”、“活动”、“冷” ”、“主观”和“知觉”。” 结果表明，在有害热刺激期间，接触热诱发电位已在中心区域广泛记录。此外，揭示了中央区域的 α 功率下降，以及额叶区域的θ 和伽马功率增加。Gamma 和 theta 节律与参与疼痛处理的感觉和情感区域之间的连接有关。机器学习分析表明，伽马波段是急性热痛的主要预测指标。该评论还解决了使用允许研究网络动态的技术来补充当前光谱特征的需求。以及在额叶区域增加的 theta 和 gamma 功率。Gamma 和 theta 节律与参与疼痛处理的感觉和情感区域之间的连接有关。机器学习分析表明，伽马波段是急性热痛的主要预测指标。该评论还解决了使用允许研究网络动态的技术来补充当前光谱特征的需求。以及在额叶区域增加的 theta 和 gamma 功率。Gamma 和 theta 节律与参与疼痛处理的感觉和情感区域之间的连接有关。机器学习分析表明，伽马波段是急性热痛的主要预测指标。该评论还解决了使用允许研究网络动态的技术来补充当前光谱特征的需求。