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Aerospace
Volume 13
Issue 2
10.3390/aerospace13020114
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Research on Pilot Workload Identification Based on EEG Time Domain and Frequency Domain

by
Weiping Yang
1,
Yixuan Li
2,
Lingbo Liu
3,
Haiqing Si
3,*,
Haibo Wang
3,*,
Ting Pan
2,
Yan Zhao
3 and
Gen Li
2
1
Aviation Industries Corporation of China Xi'an Flight Automatic Control Research Institute, Xi’an 710065, China
2
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
3
College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Liyang 213300, China
*
Authors to whom correspondence should be addressed.
Aerospace 2026, 13(2), 114; https://doi.org/10.3390/aerospace13020114
Submission received: 16 December 2025 / Revised: 16 January 2026 / Accepted: 21 January 2026 / Published: 23 January 2026
(This article belongs to the Special Issue Human Factors and Performance in Aviation Safety)
Versions Notes

Abstract

Pilot workload is a critical factor influencing flight safety. This study collects both subjective and objective data on pilot workload using the NASA-TLX questionnaire and electroencephalogram acquisition systems during simulated flight tasks. The raw EEG signals are denoised through preprocessing techniques, and relevant EEG features are extracted using time-domain and frequency-domain analysis methods. One-way ANOVA is employed to examine the statistical differences in EEG indicators under varying workload levels. A fusion model based on CNN-Bi-LSTM is developed to train and classify the extracted EEG features, enabling accurate identification of pilot workload states. The results demonstrate that the proposed hybrid model achieves a recognition accuracy of 98.2% on the test set, confirming its robustness. Additionally, under increased workload conditions, frequency-domain features outperform time-domain features in discriminative power. The model proposed in this study effectively recognizes pilot workload levels and offers valuable insights for civil aviation safety management and pilot training programs.
Keywords: pilot workload identification; EEG data analysis; fusion model; CNN-Bi-LSTM model; pilot cognitive state pilot workload identification; EEG data analysis; fusion model; CNN-Bi-LSTM model; pilot cognitive state

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MDPI and ACS Style

Yang, W.; Li, Y.; Liu, L.; Si, H.; Wang, H.; Pan, T.; Zhao, Y.; Li, G. Research on Pilot Workload Identification Based on EEG Time Domain and Frequency Domain. Aerospace 2026, 13, 114. https://doi.org/10.3390/aerospace13020114

AMA Style

Yang W, Li Y, Liu L, Si H, Wang H, Pan T, Zhao Y, Li G. Research on Pilot Workload Identification Based on EEG Time Domain and Frequency Domain. Aerospace. 2026; 13(2):114. https://doi.org/10.3390/aerospace13020114

Chicago/Turabian Style

Yang, Weiping, Yixuan Li, Lingbo Liu, Haiqing Si, Haibo Wang, Ting Pan, Yan Zhao, and Gen Li. 2026. "Research on Pilot Workload Identification Based on EEG Time Domain and Frequency Domain" Aerospace 13, no. 2: 114. https://doi.org/10.3390/aerospace13020114

APA Style

Yang, W., Li, Y., Liu, L., Si, H., Wang, H., Pan, T., Zhao, Y., & Li, G. (2026). Research on Pilot Workload Identification Based on EEG Time Domain and Frequency Domain. Aerospace, 13(2), 114. https://doi.org/10.3390/aerospace13020114

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