Elevated mental workload (MWL) experienced by pilots can result in increased reaction times or incorrect actions, potentially compromising flight safety. This study aims to develop a functional system to assist administrators in identifying and detecting pilots’ real-time MWL and evaluate its effectiveness using designed airfield traffic pattern tasks within a realistic flight simulator. The perceived MWL in various situations was assessed and labeled using NASA Task Load Index (NASA-TLX) scores. Physiological features were then extracted using a fast Fourier transformation with 2-s sliding time windows. Feature selection was conducted by comparing the results of the Kruskal-Wallis (K-W) test and Sequential Forward Floating Selection (SFFS). The results proved that the optimal input was all PSD features. Moreover, the study analyzed the effects of electroencephalography (EEG) features from distinct brain regions and PSD changes across different MWL levels to further assess the proposed system’s performance. A 10-fold cross-validation was performed on six classifiers, and the optimal accuracy of 87.57% was attained using a multi-class K-Nearest Neighbor (KNN) classifier for classifying different MWL levels. The findings indicate that the wireless headset-based system is reliable and feasible. Consequently, numerous wireless EEG device-based systems can be developed for application in diverse real-driving scenarios. Additionally, the current system contributes to future research on actual flight conditions.

Pilot factor is worth considering when analyzing the causes of civil aviation accidents. This study introduces a data-driven Bayesian network (BN) approach to investigating the joint causal effects of pilot and other factors on civil aviation safety. A total number of 163 individual pilot-related accidents in the National Transportation Safety Board (NTSB) aviation accident database from 2008 to 2020 are analyzed, focusing on eliciting the causal effects of various potential risk factors, including pilot factors, on civil aviation accidents. The modeling of the interdependency among the risk influencing factors (RIFs) and their causal contributory effect on the accident outcome is structured by a tree augmented network (TAN) and validated by sensitivity analysis. The novelty of this study is to incorporate pilot factors derived from the civil aviation accident database into risk analysis, combined with other external factors. The results indicate that weather conditions and flight phases are more correlated with casualty types of civil aviation accidents than pilot action and decision, and three other pilot factors only contribute to fatal injury in civil aviation accidents.

Motion sickness is a common disease encountered in traditional vehicles as well as autonomous vehicles, which will negatively affect user acceptance. To make clear the pathogenesis of motion sickness, this study focused on drivers’ brain activity changes before and after motion sickness happens. Based on the six-degree-of-freedom driving simulator and noninvasive functional near-infrared spectroscopy (fNIRS), a database containing driving operation data synchronized with drivers’ brain activity record from 52 participants was collected under straight and curved driving conditions. The correlation analysis between motion sickness and changes of cerebral oxyhemoglobin concentration in the cerebral cortex was carried out based on this database. Results suggest that brain activity associated with motion sickness may differ under different driving conditions. However, the emergence of motion sickness responses is related to the occipital lobe under both driving conditions. Experimental results corroborate with several theoretical hypothesis about motion sickness in neuroscience. Consequently, this study proposes a new approach to research the mechanism of the correlation between motion sickness and cerebral cortex activity, which will contribute to developing the driving assistance system for preventing or alleviating motion sickness in autonomous vehicles.

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.

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