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.INDEX TERMS Noninvasive functional near-infrared spectroscopy (fNIRS), motion sickness, driving simulator, brain activity.
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.
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