Evaluation of Mental Load of Drivers in Long Highway Tunnel Based on Electroencephalograph

An exploratory study of pilot EEG features during the climb and descent phases of flight

OBJECTIVES

The actions and decisions of pilots are directly related to aviation safety. Therefore, understanding the neurological and cognitive processes of pilots during flight is essential. This study aims to investigate the EEG signals of pilots to understand the characteristic changes during the climb and descent stages of flight.

METHODS

By performing wavelet packet decomposition on the EEG signals, we examined EEG maps during these critical phases and analyzed changes in signal intensity. To delve deeper, we calculated the log-transformed power of electroencephalograms to investigate the EEG responses under different flight conditions. Additionally, we conducted EEG spectral coherence analysis to evaluate the degree of synchronization between different electrodes during climb and descent.

RESULTS

This analysis helps us understand the functional connectivity changes in various brain regions during these phases. Understanding these complex interactions is crucial, as it provides insights into the cognitive processes of pilots during the critical climb and descent stages of flight, contributing to enhanced aviation safety.

CONCLUSIONS

By identifying how brain activity fluctuates during these phases, we can better comprehend pilots' decision-making processes, ultimately leading to the development of more effective training programs and safety protocols. This research underscores the importance of neurological studies in safety.

Cognitive Response of Underground Car Driver Observed by Brain EEG Signals

In auxiliary transportation within mines, accurately assessing the cognitive and response states of drivers is vital for ensuring safety and operational efficiency. This study investigates the effects of various vehicle interaction stimuli on the electroencephalography (EEG) signals of mine transport vehicle drivers, analyzing the cognitive and response states of drivers under different conditions to evaluate their impact on safety performance. Through experimental design, we simulate multiple scenarios encountered in real operations, including interactions with dynamic and static vehicles, personnel, and warning signs. EEG technology records brain signals during these scenarios, and data analysis reveals changes in the cognitive states and responses of drivers to different stimuli. The results indicate significant variations in EEG signals with interactions involving dynamic and static vehicles, personnel, and warning signs, reflecting shifts in the cognitive and response states of drivers. Additionally, the study examines the overall impact of different interaction objects and environments. The detailed analysis of EEG signals in different scenarios sheds light on changes in perception, attention, and responses related to drivers, which is critical for advancing safety and sustainability in mining operations.

Detection of Pilots' Psychological Workload during Turning Phases Using EEG Characteristics

Pilot behavior is crucial for aviation safety. This study aims to investigate the EEG characteristics of pilots, refine training assessment methodologies, and bolster flight safety measures. The collected EEG signals underwent initial preprocessing. The EEG characteristic analysis was performed during left and right turns, involving the calculation of the energy ratio of beta waves and Shannon entropy. The psychological workload of pilots during different flight phases was quantified as well. Based on the EEG characteristics, the pilots' psychological workload was classified through the use of a support vector machine (SVM). The study results showed significant changes in the energy ratio of beta waves and Shannon entropy during left and right turns compared to the cruising phase. Additionally, the pilots' psychological workload was found to have increased during these turning phases. Using support vector machines to detect the pilots' psychological workload, the classification accuracy for the training set was 98.92%, while for the test set, it was 93.67%. This research holds significant importance in understanding pilots' psychological workload.