Jiajun Yuan

Pilot workload is a critical factor affecting flight safety, particularly in emergency situations such as engine failure during forced landings. This study aims to assess pilot workload during engine failure-induced forced landings compared to normal landings using EEG signals. EEG data were recorded from 21 pilots using a flight simulator and EEG acquisition equipment during both forced and normal landings. Subjective workload scores were obtained using a subjective workload scale. EEG features were extracted, and nonparametric tests were applied to assess the significance of these features at different workload levels. Subsequently, machine learning algorithms (SVC, KNN, RF and LightGBM) were employed to develop models for workload evaluation. The LightGBM model achieved a peak accuracy of 99.5% using the top 80% of all features (time-domain, frequency-domain and nonlinear features) as inputs. This study provides an effective quantitative approach for assessing pilot workload during emergency situations and offers valuable insights for improving flight safety and optimising pilot training programs.

Excessive workload caused by weather conditions may increase pilot errors and flight risk. The assessment of pilot workload using pilots' physiological data is a potential method to address this issue. We conducted a flight simulator study involving 18 cadet pilots with real flight experiences ranging from 232 to 250 hours and used non-invasive functional Near-Infrared Spectroscopy (fNIRS) to measure the pilots' brain activity. Pilots' subjective National Aeronautics and Space Administration-task load index (NASA-TLX) ratings of 0000-0000 © 2022 IEEE workload were also recorded. The tested flight maneuvers included a total of 54 takeoff and climb tasks under different weather conditions. Over 1100 features covering three hemoglobin signals and four brain cortexes were extracted from the fNIRS data. A statistical analysis was carried out on the subjective ratings and fNIRS features, and a weighting analysis was applied to the selected fNIRS features that were highly sensitive to pilot workload levels. A Convolutional Neural Network (CNN) with an attention mechanism (CNN-Attention) was built as a classifier for assessing pilot workload and compared with CNN, Deep Neural Network (DNN), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF) models. The results suggested that pilot workload was highly associated with three hemoglobin measures as well as the activities of different brain regions, including the prefrontal cortex, motor cortex, and occipital cortex, and the CNN-Attention performed best. The findings from this study could provide a reference for optimizing pilot training systems and improving flight safety under different weather conditions

Pilots’ excessive mental workload could reduce their ability to perform concurrent tasks during emergency flights, which is one of the most critical aviation safety concerns. Several past efforts have attempted to investigate the underlying issues, but all had limited success owing to the challenge of collecting representative data under realistic operating conditions. This study aimed to address this challenge by conducting a flight simulator study involving a comparatively large number of participants, who were pilot cadets with flight experience, and using noninvasive functional near-infrared spectroscopy (fNIRS) to collect the pilots’ brain activity data. Pilots’ subjective ratings and brain activity records were collected over a total of 75 simulated flights under three subtask scenarios comprising different equipment failures. A statistical analysis was carried out on the subjective ratings and on the changes observed in the saturation of the oxyhemoglobin (ΔOxyHb) of individual fNIRS channels. The mental workload of the pilots was classified using a support vector machine hierarchical combination classifier, focusing on the question of whether it is feasible to classify pilots’ mental workload using brain activity signals (i.e., ΔOxyHb). The results suggested that the pilots’ mental workload levels were highly associated with the ΔOxyHb measures as well as with the activities of different brain regions, including the prefrontal-, motor-, and occipital cortex. The findings from this study could provide a reference for optimizing pilot training systems and improving pilot performance during emergency flight operations.

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.