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This study recorded driver behavior and brain activity using electroencephalography (EEG) during a driving simulation. The data can help develop methods to detect driving fatigue and drowsiness.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Human-Computer Interaction

Background:

  • Sustained attention is critical for safe driving.
  • Detecting driver fatigue and drowsiness is a significant safety challenge.
  • Brain dynamics during driving tasks are not fully understood.

Purpose of the Study:

  • To present a novel dataset of driver behavior and concurrent electroencephalography (EEG) data.
  • To facilitate the development of new methods for analyzing brain cortical dynamics.
  • To enable the detection of driving fatigue and drowsiness.

Main Methods:

  • Acquired 62 sessions of 32-channel EEG data from 27 subjects in an immersive driving simulator.
  • Subjects performed a 90-minute sustained-attention task on a four-lane highway.
  • Random lane-departure events were induced to elicit driver responses.

Main Results:

  • Collected comprehensive data on driver behavior and neural activity during a simulated driving task.
  • The dataset captures detailed event-related brain dynamics, including deviation and response onsets/offsets.
  • The data provides a rich resource for understanding neural correlates of sustained attention and potential fatigue.

Conclusions:

  • The described dataset is a valuable resource for the neuroscience and brain-computer interface communities.
  • This data will support the development of advanced neural processing methodologies.
  • The dataset has the potential to significantly advance the detection of driving fatigue and drowsiness.