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Neuroergonomics: A Perspective from Neuropsychology, with a Proposal about Workload
Article

Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

1
Department of Industrial & Systems Engineering, Texas A & M University, College Station, TX 77843, USA
2
Department of Mechanical Engineering, Texas A & M University, College Station, TX 77843, USA
3
Department of Civil and Coastal Engineering, Engineering School of Sustainable Infrastructure and Environment (ESSIE), Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, USA
*
Authors to whom correspondence should be addressed.
Academic Editor: Gianluca Borghini
Brain Sci. 2021, 11(7), 885; https://doi.org/10.3390/brainsci11070885
Received: 10 June 2021 / Revised: 26 June 2021 / Accepted: 28 June 2021 / Published: 30 June 2021
(This article belongs to the Special Issue Current Perspectives on Neuroergonomics)
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. View Full-Text
Keywords: VR; fNIRS; machine learning; firefighters; emergency responders; learning; stress; episodic memory; encoding; retrieval; neuroergonomics VR; fNIRS; machine learning; firefighters; emergency responders; learning; stress; episodic memory; encoding; retrieval; neuroergonomics
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MDPI and ACS Style

Abujelala, M.; Karthikeyan, R.; Tyagi, O.; Du, J.; Mehta, R.K. Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics. Brain Sci. 2021, 11, 885. https://doi.org/10.3390/brainsci11070885

AMA Style

Abujelala M, Karthikeyan R, Tyagi O, Du J, Mehta RK. Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics. Brain Sciences. 2021; 11(7):885. https://doi.org/10.3390/brainsci11070885

Chicago/Turabian Style

Abujelala, Maher, Rohith Karthikeyan, Oshin Tyagi, Jing Du, and Ranjana K. Mehta 2021. "Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics" Brain Sciences 11, no. 7: 885. https://doi.org/10.3390/brainsci11070885

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