Keshmiri Soheil
Advanced Telecommunications Research Institute International (ATR), Kyoto 619-0237, Japan.
Entropy (Basel). 2021 Feb 26;23(3):286. doi: 10.3390/e23030286.
Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.
近几十年来，在利用大脑活动识别压力数字标记方面取得了重大进展。特别是，熵测量方法在这方面的成功非常吸引人，原因如下：（1）它们适合捕捉大脑活动记录的线性和非线性特征；（2）它们与大脑信号变异性直接相关。这些发现依赖于外部刺激来诱发大脑应激反应。另一方面，研究表明，使用不同类型的实验诱导心理和生理应激源可能会对大脑对压力的反应产生不同的影响，因此应该与更一般的模式区分开来。本研究朝着解决这个问题迈出了一步，引入条件熵（CE）作为一种基于脑电图（EEG）的潜在静息态压力数字标记。为此，我们使用了20名个体的静息态多通道EEG记录，这些个体对压力相关问卷的回答显示出明显较高和较低的压力水平。通过应用表征相似性分析（RSA）和K近邻（KNN）分类，我们验证了使用CE为寻找有效的压力数字标记的解决方案概念提供的潜力。
