Using electroencephalography to analyse drivers' different cognitive workload characteristics based on on-road...

Area of Science:

• Neuroscience
• Cognitive Psychology
• Automotive Safety

Background:

• Driver's cognitive workload significantly impacts driving safety.
• Understanding brain activity patterns associated with varying cognitive loads is crucial for developing advanced driver-assistance systems.
• Existing research often lacks detailed analysis of EEG signal distributions and their correlation with driving performance under diverse workloads.

Purpose of the Study:

• To analyze the impact of cognitive workload on driving safety using electroencephalogram (EEG) signals.
• To investigate the significance analysis of EEG under different cognitive workloads.
• To explore the distribution of EEG maps across different frequency signals and their influence on driving safety.

Main Methods:

• Conducted an on-road experiment to collect EEG data during driving.
• Processed EEG signals to obtain delta, theta, alpha, and beta frequencies.
• Utilized short-time Fourier transform, power spectral density, event-related spectral perturbation, and inter-trial coherence for time-frequency analysis and signal correlation.

Main Results:

• Identified differences in brain activity between the left and right hemispheres.
• Revealed resource occupancy trends in monitoring, perception, visual, and auditory channels under varying driving conditions.
• Demonstrated a direct correlation between increased cognitive workload and decreased driving safety.

Conclusions:

• Changes in cognitive workload distinctly affect brain signals, impacting driving safety.
• EEG signal characteristics provide a theoretical basis for enhancing driving safety strategies.
• Mastering EEG patterns can enable more targeted driver supervision and safety warnings.