Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics.
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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基于大脑活动的指标，用于评估消防员压力下VR的学习状态：一种探索性机器学习方法。
DOI：
10.3390/brainsci11070885
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发表时间：
2021-06-30
期刊：
影响因子：
3.3
通讯作者：
Mehta RK
中科院分区：
文献类型：
--
作者：
Abujelala M;Karthikeyan R;Tyagi O;Du J;Mehta RK
消防员的职责性质要求他们在不利的条件下长时间工作。为了有效地完成工作，他们需要忍受长时间的大量紧张训练。创造这样的训练环境是非常昂贵的，而且难以保证受训者的安全。在这项研究中，消防员在虚拟环境中接受训练，其中包括火灾，警报和烟雾等虚拟扰动。本文的目的是使用机器学习方法来辨别视觉空间情景记忆任务中消防员的编码和检索状态，并探索大脑的哪些区域提供合适的信号来解决这个分类问题。我们的研究结果表明，随机森林算法可以用来区分信息编码和检索使用功能提取的fNIRS数据。如果训练和测试数据是在相似的环境条件下获得的，我们的算法实现了F-1得分和准确度。然而，当对在不同环境条件下收集的数据进行评估时，该算法的性能下降到F-1评分和准确度。我们还发现，如果在相同的环境条件下记录训练和评估数据，RPM，LDLPFC，RDLPFC分别是非压力，压力以及压力和非压力条件下最相关的大脑区域。
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 and an accuracy of if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of and accuracy of 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.
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影响因子：
3.4
作者：
Engelbrecht H;Lindeman RW;Hoermann S
通讯作者：
Hoermann S
影响因子：
2.9
作者：
Hu, Xin;Zhuang, Chu;Zhang, Dan
通讯作者：
Zhang, Dan
影响因子：
--
作者：
Ding, Hui;Feng, Peng-Mian;Lin, Hao
通讯作者：
Lin, Hao
DOI：
10.1111/mono.12033
发表时间：
2013-08
影响因子：
9.5
作者：
Bauer PJ;Dikmen SS;Heaton RK;Mungas D;Slotkin J;Beaumont JL
通讯作者：
Beaumont JL
影响因子：
5.7
作者：
Hampshire A;Chamberlain SR;Monti MM;Duncan J;Owen AM
通讯作者：
Owen AM