Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30831
The Enhancement of Training of Military Pilots Using Psychophysiological Methods

Authors: G. Kloudova, M. Stehlik


Optimal human performance is a key goal in the professional setting of military pilots, which is a highly challenging atmosphere. The aviation environment requires substantial cognitive effort and is rich in potential stressors. Therefore, it is important to analyze variables such as mental workload to ensure safe conditions. Pilot mental workload could be measured using several tools, but most of them are very subjective. This paper details research conducted with military pilots using psychophysiological methods such as electroencephalography (EEG) and heart rate (HR) monitoring. The data were measured in a simulator as well as under real flight conditions. All of the pilots were exposed to highly demanding flight tasks and showed big individual response differences. On that basis, the individual pattern for each pilot was created counting different EEG features and heart rate variations. Later on, it was possible to distinguish the most difficult flight tasks for each pilot that should be more extensively trained. For training purposes, an application was developed for the instructors to decide which of the specific tasks to focus on during follow-up training. This complex system can help instructors detect the mentally demanding parts of the flight and enhance the training of military pilots to achieve optimal performance.

Keywords: human performance, Psychophysiological Methods, cognitive effort, military pilots

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 668


[1] Barnett, V., & Lewis, T. (1994). Probability and Mathematical Statistics: Outliers in Statistical Data. Wiley.
[2] Bayevsky, R. M., Ivanova, G. G., Chireykin, L. V., Gavrilushkin, A. P., Dovgalevsky, K. U., Mironova, T. F., et al. (2002). HRV Analysis under the usage of different electrocardiography systems (methodical recommendations).
[3] 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, pp. 58-75.
[4] Budzynski, T., Budzynski, H., Evans, J., & Abarbanel, A. (2009). Introduction to Quantitative EEG and Neurofeedback: Advanced Theory and Applications. Elsevier.
[5] Coppersmith, D. S., Hong, J., & Hosking, J. (1999). Partitioning Nominal Attributes in Decision Trees. Data Mining and Knowledge Discovery (3), pp. 197-217.
[6] Costa, P. T., & MacCrae, R. R. (1992). Revised NEO personality inventory (NEO PI-R) and NEO five-factor inventory (NEO FFI): Professional manual. Psychological Assessment Resources.
[7] Dahlstrom, M., Nahlinder, S., Wilson, G. F., & Svensson, E. (2011). Recording of Psychophysiological Data. The International Journal of Aviation Psychology, 21 (2) pp. 105-122.
[8] Di Stasi, L. L., Antoli, A., & Canas, J. J. (2011). Main sequence: An index for detecting mental workload variation in complex tasks. Applied Ergonomics (42), pp. 807-813.
[9] Dussault, C., Guezennec, C., & Jouanin, J. (2004). EEG and ECG Changes During Selected Flight Sequences. Aviation, Space, and Environmental Medicine, 75(10), pp. 889-897.
[10] Hanson, E. K., & Bazanski, J. (2001). Ecological momentary assessments in aviation: The development of a Pilot Experience Evaluating Device (PEED) for the in-flight registration of flight phases, mental effort and reaction time. In J. Fahrenberg, & M. Myrtek, Progress in Ambulatory Assessment. Computer-assisted Psychological and Psychophysiological Methods in Monitoring and Field Studies. Bern, Göttingen: Hogrefe & Huber Publishers.
[11] Jacobs, G. D., & Friedman, R. (2004). EEG spectral analysis of relaxation techniques. Applied Psychophysiology and Biofeedback, pp. 245-254.
[12] Karthikeyan, P., Murugappan, M., & Yaacob, S. (2013). Detection of human stress using short-term ECG and HRV signals. Journal Of Mechanics In Medicine & Biology, 13(2), p. 1.
[13] Kloudova, G., & Stehlik, M. (2016). Mental workload of military pilots as measured in a tactical simulator. Interservice/Industry Training, Simulation & Education Conference (I/ITSEC) 2016 (pp. 2057-2065). Orlando, FL: NTSA.
[14] Lal, S., & Craig, A. (2002). Driver fatigue: Electroencephalography and psychological assessment. Psychophysiology, pp. (39) 313-321.
[15] Rauch, H. H., Karpul, D., Derman, W., & Prinsloo, G. (2013, 38 (1)). The Effect of a Single Session of Short Duration Heart Rate Variability Biofeedback on EEG: A Pilot Study. Applied Psychophysiology & Biofeedback, pp. 45-56.
[16] Sauvet, F., Bougard, C., Coroenne, M., Lely, L., Van Beers, P., Elbaz, M., et al. (2017). In-flight automatic detection of vigilance states using a single EEG channel. In-flight automatic detection of vigilance states using a single EEG channel, 61(12), pp. 2840-2847.
[17] Sokhadze, E. T. (2012). Peak Performance Training Using Prefrontal EEG Biofeedback. Biofeedback, pp. 40 (1), 7-15.
[18] Solovey, E. T., Zec, M., Garcia Perez, E. A., Reimer, B., & Mehler, B. (2014). Classifying driver workload using physiological and driving performance data: Two field studies. Proceedings of the 32nd annual ACM conference on human factors in computing systems (pp. 4057-4066). Toronto: ACM.
[19] Thomas, M. L., & Russo, M. B. (2007). Neurocognitive monitors: Toward the prevention of cognitive performance decrements and catastrophic failures in the operational environment. Aviation, Space, and Environmental Medicine (78), pp. 144-152.
[20] Tjolleng, A., Jung, K., Hong, W., Lee, W., Lee, B., You, H., et al. (2017). Classification of a driver's cognitive workload levels using an artificial neural network on ECG signals. Applied Ergonomics (59), pp. 326-332.
[21] Veltman, J. A., & Gaillard, A. W. (1993). Indices of mental workload in a complex task environment. Neuropsychobiology, pp. 28(1-2), 72-75.
[22] Wilson, G. F. (2002). An analysis of mental workload in pilots during flight using multiple psychophysiological measures. The International Journal of Aviation Psychology, 12, pp. 3-18.
[23] Yi, B., Matzel, S., Feuerecker, M., Hörl, M., Ladinig, C., Abeln, V., et al. (2015). The impact of chronic stress burden of 520-d isolation and confinement on the physiological response to subsequent acute stress challenge. Behavioural brain research (281), pp. 111-115.