Neuroadaptive Closed-Loop Training in Aviation: A Dual-Stage fNIRS/EEG Framework for Real-Time Workload Regulation and Diagnostic Remediation
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Nicolas Jean Lejeune
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Cognitive factors account for approximately 26% of civilian aviation incidents, yet simulator-based training programs operate without real-time measurement of trainee cognitive state. This article introduces a dual-stage neuroadaptive closed-loop training framework in which a within-session fNIRS/EEG workload controller and a session-level LSTM diagnostic classifier are coupled through a rolling per-subject workload envelope re-estimation procedure. The online stage fuses anterior prefrontal HbO₂ concentration with frontal theta power density to modulate scenario complexity continuously; the offline stage segments completed sessions by flight phase and identifies recurring performance deficiency patterns to construct individualized remediation paths for subsequent sessions. A 56-participant flight simulator study (four sessions, 24-hour inter-session intervals) showed a 33.3% reduction in sessions-to-competency relative to an iso-difficulty control, with single-trial workload classification accuracy of 76.4% under 16-channel low-motion conditions. The LSTM achieved an F1-score of 0.81 on phase-specific deficiency detection, outperforming a session-averaged logistic regression baseline by 53.5% on primary-phase identification rate.
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Authors
Nicolas Jean Lejeune
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Aricò, P., Borghini, G., Di Flumeri, G., Colosimo, A., Bonelli, S., Golfetti, A., Pozzi, S., Imbert, J.-P., Granger, G., Benhacene, R., & Babiloni, F. (2016). Adaptive automation triggered by EEG-based mental workload index: A passive brain-computer interface application in realistic air traffic control environment. Frontiers in Human Neuroscience, 10, 539. https://doi.org/10.3389/fnhum.2016.00539
Borghini, G., Astolfi, L., Vecchiato, G., Mattia, D., & Babiloni, F. (2014). Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neuroscience & Biobehavioral Reviews, 44, 58–75. https://doi.org/10.1016/j.neubiorev.2012.10.003
Callan, D. E., Terzibas, C., Cassel, D. B., Sato, M.-A., & Parasuraman, R. (2016). The brain is faster than the hand in split-second intentions to respond to an impending hazard: A simulation of neuroadaptive automation to speed recovery to perturbation in flight attitude. Frontiers in Human Neuroscience, 10, 187. https://doi.org/10.3389/fnhum.2016.00187
Causse, M., Chua, Z., Peysakhovich, V., Del Campo, N., & Matton, N. (2017). Mental workload and neural efficiency quantified in the prefrontal cortex using fNIRS. Scientific Reports, 7, 5222. https://doi.org/10.1038/s41598-017-05378-x
Dehais, F., Dupres, A., Blum, S., Drougard, N., Scannella, S., Roy, R. N., & Lotte, F. (2019). Monitoring pilot's mental workload using ERPs and spectral power with a six-dry-electrode EEG system in real flight conditions. Sensors, 19(6), 1324. https://doi.org/10.3390/s19061324
Dehais, F., Lafont, A., Roy, R. N., & Fairclough, S. H. (2020). A neuroergonomics approach to mental workload, engagement and human performance. Frontiers in Neuroscience, 14, 268. https://doi.org/10.3389/fnins.2020.00268
Feltman, K. A., Vogl, J. F., McAtee, A., & Kelley, A. M. (2024). Measuring aviator workload using EEG: An individualized approach to workload manipulation. Frontiers in Neuroergonomics, 5, 1397586. https://doi.org/10.3389/fnrgo.2024.1397586
Hinss, M. F., Jahanpour, E. S., Somon, B., Pluchon, L., Dehais, F., & Roy, R. N. (2023). Open multi-session and multi-task EEG cognitive dataset for passive brain-computer interface applications. Scientific Data, 10, 85. https://doi.org/10.1038/s41597-022-01898-y
Klaproth, O. W., Halbrügge, M., Krol, L. R., Vernaleken, C., Zander, T. O., & Russwinkel, N. (2020). A neuroadaptive cognitive model for dealing with uncertainty in tracing pilots' cognitive state. Topics in Cognitive Science, 12(3), 1012–1029. https://doi.org/10.1111/tops.12515
Mark, J. A., Kraft, A. E., Ziegler, M. D., & Ayaz, H. (2022). Neuroadaptive training via fNIRS in flight simulators. Frontiers in Neuroergonomics, 3, 820523. https://doi.org/10.3389/fnrgo.2022.820523
Parasuraman, R. (2011). Neuroergonomics: Brain, cognition, and performance at work. Current Directions in Psychological Science, 20(3), 181–186. https://doi.org/10.1177/0963721411409176
Roy, R. N., Hinss, M. F., Darmet, L., Ladouce, S., Jahanpour, E. S., Somon, B., Xu, X., Drougard, N., Dehais, F., & Lotte, F. (2022). Retrospective on the first passive brain-computer interface competition on cross-session workload estimation. Frontiers in Neuroergonomics, 3, 838342. https://doi.org/10.3389/fnrgo.2022.838342
van Weelden, E., Alimardani, M., Wiltshire, T. J., & Louwerse, M. M. (2022). Aviation and neurophysiology: A systematic review. Applied Ergonomics, 105, 103838. https://doi.org/10.1016/j.apergo.2022.103838
Zander, T. O., & Kothe, C. (2011). Towards passive brain-computer interfaces: Applying brain-computer interface technology to human-machine systems in general. Journal of Neural Engineering, 8(2), 025005. https://doi.org/10.1088/1741-2560/8/2/025005
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