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首页> 美国卫生研究院文献>Brain Sciences >Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics
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Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

机译：基于大脑活动的指标评估VR在消防队员压力下的学习状态：神经变压器中的探索机器学习方法

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The nature of firefighters’ duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.
机译：消防员职责的性质要求他们在不利条件下长期工作。为了有效地执行他们的工作，他们必须忍受长时间的广泛压力训练。创建此类培训环境非常昂贵，很难保证受训者的安全性。在这项研究中，消防员培训在虚拟环境中，包括虚拟扰动，例如火灾，警报和烟雾。本文的目的是使用机器学习方法在探测空间内存记忆任务期间在消防员中辨别编码和检索状态，并探索大脑的哪个区域提供合适的信号来解决该分类问题。我们的结果表明，随机森林算法可用于区分信息编码和使用从Fnirs数据中提取的功能进行检索。我们的算法达到了0.844的F-1得分，如果在类似的环境条件下获得训练和测试数据，则精度为79.10％。然而，算法的性能下降到0.723的F-1得分，并且在不同环境条件下收集的数据的数据评估时，精度为60.61％。我们还发现，如果在相同的环境条件下记录培训和评估数据，则RPM，LDLPFC，RDLPFC分别是在非压力，压力和压力和非压力条件的混合下最相关的大脑区域。

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