Objective: Mental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of bio-markers that not only identify stress but also predict the conditions (or tasks) that cause stress. Electroencephalograms (EEGs) have been used for a long time to study and identify bio-markers. While these bio-markers have successfully predicted stress in EEG studies for binary conditions, their performance is suboptimal for multiple conditions of stress. Methods: To overcome this challenge, we propose using latent based representations of the bio-markers, which have been shown to significantly improve EEG performance compared to traditional bio-markers alone. We evaluated three commonly used EEG based bio-markers for stress, the brain load index (BLI), the spectral power values of EEG frequency bands (alpha, beta and theta), and the relative gamma (RG), with their respective latent representations using four commonly used classifiers. Results: The results show that spectral power value based bio-markers had a high performance with an accuracy of 83%, while the respective latent representations had an accuracy of 91%.

中文翻译：

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使用机器学习自动编码器对护士和非卫生专业人员脑皮质脑电图的多级压力反应进行分类


目的：精神压力是我们社会的一个主要问题，并已成为许多精神病学研究人员感兴趣的领域。一个主要的研究重点领域是识别生物标记，这些生物标记不仅可以识别压力，还可以预测导致压力的条件（或任务）。脑电图（EEG）长期以来一直被用来研究和识别生物标记。虽然这些生物标记物在二元条件下的脑电图研究中成功预测了压力，但它们的性能对于多种压力条件来说并不是最佳的。方法：为了克服这一挑战，我们建议使用基于潜在的生物标记表示，与单独的传统生物标记相比，该表示已被证明可以显着提高脑电图性能。我们评估了三种常用的基于脑电图的压力生物标志物、脑负荷指数 (BLI)、脑电图频段的频谱功率值（α、β 和 θ）以及相对伽马 (RG) 及其各自的潜在表示使用四个常用的分类器。结果：结果表明，基于光谱功率值的生物标记物具有较高的性能，准确度为 83%，而各自的潜在表示的准确度为 91%。