Department of Applied Sciences, Université du Québec à Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada.
Laboratoire de Recherche Biomécanique et Neurophysiologique en Réadaptation Neuro-Musculo-Squelettique (Lab BioNR), Université du Québec à Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada.
Sensors (Basel). 2022 Aug 20;22(16):6272. doi: 10.3390/s22166272.
This proof-of-concept study explores the potential of developing objective pain identification based on the analysis of electroencephalography (EEG) signals. Data were collected from participants living with chronic fibromyalgia pain ( = 4) and from healthy volunteers ( = 7) submitted to experimental pain by the application of capsaicin cream (1%) on the right upper trapezius. This data collection was conducted in two parts: (1) baseline measures including pain intensity and EEG signals, with the participant at rest; (2) active measures collected under the execution of a visuo-motor task, including EEG signals and the task performance index. The main measure for the objective identification of the presence of pain was the coefficient of variation of the upper envelope (CVUE) of the EEG signal from left fronto-central (FC5) and left temporal (T7) electrodes, in alpha (8-12 Hz), beta (12-30 Hz) and gamma (30-43 Hz) frequency bands. The task performance index was also calculated. CVUE (%) was compared between groups: those with chronic fibromyalgia pain, healthy volunteers with "No pain" and healthy volunteers with experimentally-induced pain. The identification of the presence of pain was determined by an increased CVUE in beta (CVUE) from the EEG signals captured at the left FC5 electrode. More specifically, CVUE increased up to 20% in the pain condition at rest. In addition, no correlation was found between CVUE and pain intensity or the task performance index. These results support the objective identification of the presence of pain based on the quantification of the coefficient of variation of the upper envelope of the EEG signal.
本概念验证研究旨在探索基于脑电图 (EEG) 信号分析开发客观疼痛识别的潜力。研究数据来自患有慢性纤维肌痛的参与者（n=4）和健康志愿者（n=7），志愿者的右上部斜方肌涂抹辣椒素乳膏（1%）以产生实验性疼痛。该数据采集分为两部分：（1）基线测量，包括疼痛强度和 EEG 信号，参与者处于休息状态；（2）在执行视动任务期间采集的主动测量，包括 EEG 信号和任务表现指数。客观识别疼痛存在的主要指标是左额中央（FC5）和左颞部（T7）电极 EEG 信号的上包络（CVUE）的变异系数，频率范围为阿尔法（8-12Hz）、贝塔（12-30Hz）和伽马（30-43Hz）。还计算了任务表现指数。比较了慢性纤维肌痛组、无疼痛的健康志愿者组和实验性疼痛的健康志愿者组之间的 CVUE（%）。通过左 FC5 电极记录的 EEG 信号中β频段（CVUE）的增加来确定疼痛的存在。具体来说，在休息时的疼痛状态下，CVUE 增加了 20%。此外，CVUE 与疼痛强度或任务表现指数之间没有相关性。这些结果支持基于 EEG 信号上包络变异系数的定量来客观识别疼痛的存在。
