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Area of Science:

  • Neuroscience
  • Mining Engineering
  • Human Factors Engineering

Background:

  • Ensuring safety and efficiency in mine auxiliary transportation requires accurate assessment of driver cognitive and response states.
  • Electroencephalography (EEG) offers a non-invasive method to monitor brain activity and infer cognitive states.

Purpose of the Study:

  • To investigate the impact of various vehicle interaction stimuli on electroencephalography (EEG) signals of mine transport drivers.
  • To analyze how different interaction scenarios affect driver cognitive and response states, and consequently, safety performance.

Main Methods:

  • Experimental simulation of real-world mine transport scenarios involving interactions with vehicles, personnel, and warning signs.
  • Recording electroencephalography (EEG) signals from drivers during these simulated interactions.
  • Analyzing EEG data to identify changes in cognitive and response states related to specific stimuli.

Main Results:

  • Significant variations in EEG signals were observed in response to interactions with dynamic/static vehicles, personnel, and warning signs.
  • These EEG variations indicate distinct shifts in drivers' cognitive and response states under different interaction conditions.
  • The study identified the influence of various interaction objects and environments on driver brain activity.

Conclusions:

  • EEG signal analysis provides critical insights into driver perception, attention, and response dynamics in mining environments.
  • Understanding these EEG-based changes is essential for developing targeted safety interventions and improving operational efficiency in mines.
  • This research contributes to advancing safety and sustainability in mining operations through better driver state monitoring.