Aviation and neurophysiology: A systematic review

• Published in: Applied ergonomics Vol. 105; p. 103838
• Main Authors: van Weelden, Evy, Alimardani, Maryam, Wiltshire, Travis J., Louwerse, Max M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2022
• Subjects: Aviation; Electroencephalography (EEG); Engagement; Flight training; Functional near-infrared spectroscopy (fNIRS); Mental fatigue; Multimodality; Neurophysiology; Simulation fidelity; Social dynamics; Workload; Engagement; Functional near-infrared spectroscopy (fNIRS); Electroencephalography (EEG); Aviation; Simulation fidelity; Multimodality; Social dynamics; Mental fatigue; Flight training; Neurophysiology; Workload
• ISSN: 0003-6870; 1872-9126
• DOI: 10.1016/j.apergo.2022.103838

This paper systematically reviews 20 years of publications (N = 54) on aviation and neurophysiology. The main goal is to provide an account of neurophysiological changes associated with flight training with the aim of identifying neurometrics indicative of pilot's flight training level and task relevant mental states, as well as to capture the current state-of-art of (neuro)ergonomic design and practice in flight training. We identified multiple candidate neurometrics of training progress and workload, such as frontal theta power, the EEG Engagement Index and the Cognitive Stability Index. Furthermore, we discovered that several types of classifiers could be used to accurately detect mental states, such as the detection of drowsiness and mental fatigue. The paper advances practical guidelines on terminology usage, simulator fidelity, and multimodality, as well as future research ideas including the potential of Virtual Reality flight simulations for training, and a brain-computer interface for flight training. •EEG metrics can be used to evaluate learning progress in aviation training.•EEG theta and beta power have a relationship with flight training and workload.•EEG/fNIRS classifiers can detect pilot's mental states and provide feedback.•Multimodal data can increase accuracy, but only EEG or fNIRS should be sufficient.