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Related Experiment Video

Updated: Jul 30, 2025

Driving Under the Influence: How Music Listening Affects Driving Behaviors
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Using electroencephalography to analyse drivers' different cognitive workload characteristics based on on-road

Ruiwei Liu1, Shouming Qi2,3, Siqi Hao4

  • 1Department of Naval Architecture and Marine Engineering, Guangzhou Maritime University, Guangzhou, China.

Frontiers in Psychology
|May 11, 2023
PubMed
Summary

This study analyzed electroencephalogram (EEG) signals during driving to understand cognitive workload

Keywords:
EEG signalsEEG topography mapcognitive workloadevent-related spectral perturbationinter-trial coherencetime-frequency transformation

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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.