Wenbing Zhu
ABSTRACT
Mental workload (MWL) recognition of carrier-based aircraft pilots benefits understanding the pilots' performance, which is crucial for enhancing the combat capability of carrier-based aircraft and ensuring flight safety. This paper aims to investigate the real-time MWL of pilots in carrier flight tasks, including takeoff, cruise, and landing. Firstly, we acquired the National Aeronautics and Space Administration-Task Load Index scales (NASA-TLX) and electroencephalogram (EEG) signals of 6 participants under different flight phases based on a flight simulator and a portable EEG. Secondly, the NASA-TLX scores were analyzed by statistical analysis. It is shown that the MWL levels are ranked as follows: landing > cruise > takeoff. Thirdly, the EEG features in the time domain, frequency domain, and nonlinear were extracted and these features were selected by statistical analysis and random forest feature importance evaluation. Finally, the performances of the k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) were compared for the MWL identification. EEG Features with significant differences and high importance in different flight phases were employed as the inputs to three machine learning models. The results show that machine learning models can effectively identify pilots’ MWL, and the best identification of MWL was achieved using the selected fusion features as inputs to the RF classifier, with an average accuracy of 90.5%. The research results indicated that the portable EEG device is feasible for collecting high-quality EEG signals, and the RF classifier and selected fusion features are more suitable for identifying carrier-based pilots’ MWL.
- Zhang, C., , Incorporation of Pilot Factors into Risk Analysis of Civil Aviation Accidents from 2008 to 2020: A Data-Driven Bayesian Network Approach. Aerospace, 2023. 10(1): p. 9.Google Scholar
- Sun, G., A quantitative evaluation method of pilot errors during landing process of carrier-based aircraft. in 2015 First International Conference on Reliability Systems Engineering (ICRSE). 2015. IEEE.Google ScholarCross Ref
- Tao, D., , A systematic review of physiological measures of mental workload. International journal of environmental research and public health, 2019. 16(15): p. 2716.Google ScholarCross Ref
- Wei, Z., Review of research on pilots’ mental workload evaluation indices and measurement methods. Science Technology and Engineering, 2019.19( 24): p. 1-8. (in Chinese)Google Scholar
- Huynh, P., G. Warner, and H. Lin, Effects of EMD and Feature Extraction on EEG Analysis. Journal of Advances in Information Technology Vol, 2020. 11(1).Google ScholarCross Ref
- Gjoreski, M., , Cognitive load monitoring with wearables–lessons learned from a machine learning challenge. IEEE Access, 2021. 9: p. 103325-103336.Google ScholarCross Ref
- Sciaraffa, N., On the use of machine learning for EEG-based Workload assessment: Algorithms comparison in a realistic task. in International Symposium on Human Mental Workload: Models and Applications. 2019. Springer.Google ScholarCross Ref
- Guo, Z., Recognition method of driving mental workload based on EEG entropy. Journal of Southeast University (Natural Science Edition) 2015, 45(05): p. 980-984. (in Chinese)Google Scholar
- Borghini, G., , Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neuroscience & Biobehavioral Reviews, 2014. 44: p. 58-75.Google ScholarCross Ref
- Rubio, S., , Evaluation of subjective mental workload: A comparison of SWAT, NASA‐TLX, and workload profile methods. Applied psychology, 2004. 53(1): p. 61-86.Google Scholar
- Gramfort, A., , MNE software for processing MEG and EEG data. Neuroimage, 2014. 86: p. 446-460.Google ScholarCross Ref
- Wang, S., J. Gwizdka, and W.A. Chaovalitwongse, Using wireless EEG signals to assess memory workload in the $ n $-back task. IEEE Transactions on Human-Machine Systems, 2015. 46(3): p. 424-435.Google Scholar
- Samima, S. and M. Sarma. EEG-based mental workload estimation. in 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC). 2019. IEEE.Google ScholarCross Ref
- Guo, Z., SVM recognition model of drivers’ mental workload. Journal of Harbin Institute of Technology 2016, 48(03): p. 154-158. (in Chinese)Google Scholar
- Jiao, Y., , Physiological responses and stress levels of high-speed rail train drivers under various operating conditions-a simulator study in China. International Journal of Rail Transportation, 2022: p. 1-16.Google Scholar
- Liu, K, Railway train driver stress detection using ECG signal. China Safety Science Journal, 2022, 32(06): p. 31-37. (in Chinese)Google Scholar
- Liu, Q., Subjective assessment of mental workload of the pilots in simulated carrier flight. Chinese Journal of Aerospace Medicine, 2017, 28(1): p. 11-14. (in Chinese)Google Scholar
- Strmiska, M., Z. Koudelková, and M. Žabčíková, Measuring brain signals using emotiv devices. WSEAS Transactions on Systems and Control, 2018.Google Scholar
- Pratama, S.H., Signal Comparison of Developed EEG Device and EMOTIV INSIGHT Based on Brainwave Characteristics Analysis. in Journal of Physics: Conference Series. 2020. IOP Publishing.Google Scholar
- Hernández-Sabaté, A., , Recognition of the mental workloads of pilots in the cockpit using eeg signals. Applied Sciences, 2022. 12(5): p. 2298.Google ScholarCross Ref
- Chakladar, D.D., , EEG-based mental workload estimation using deep BLSTM-LSTM network and evolutionary algorithm. Biomedical Signal Processing and Control, 2020. 60: p. 101989.Google ScholarCross Ref
- Zhang, Y., , Recognising drivers’ mental fatigue based on EEG multi-dimensional feature selection and fusion. Biomedical Signal Processing and Control, 2023. 79: p. 104237.Google ScholarCross Ref
- Chu, H., , Research on Personalized EEG Response Law and Classification of Mental Workload. Manned Spaceflight, 2021. 27(06): p. 710-718.Google Scholar
Index Terms
- Recognition of Pilot Mental workload in the Simulation Operation of Carrier-based Aircraft Using the Portable EEG
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