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Detection of Driver Braking Intention Using EEG Signals During Simulated Driving.

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This study introduces a novel system using electroencephalogram (EEG) signals to detect emergency braking intentions. The brain-computer interface accurately predicts driver braking 600ms in advance, paving the way for brain-controlled vehicles.

Keywords:
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Area of Science:

  • Neuroscience
  • Automotive Engineering
  • Biomedical Engineering

Background:

  • Driver behavior analysis is crucial for automotive safety.
  • Detecting emergency situations early can prevent accidents.
  • Brain-computer interfaces (BCIs) offer potential for advanced driver assistance systems.

Purpose of the Study:

  • To develop and validate a novel system for detecting emergency braking intention using electroencephalogram (EEG) signals.
  • To investigate the efficacy of different feature extraction methods for EEG-based intention detection.
  • To assess the system's accuracy and prediction time in a simulated driving environment.

Main Methods:

  • Acquisition of eight-channel EEG and motion-sensing data via a custom headset during simulated driving.
  • Introduction of a novel method for precise labeling of training data shortly after emergency stimulus onset.
  • Investigation of EEG band power-based and autoregressive (AR)-based features.
  • Utilizing an artificial neural network classifier for intention detection.

Main Results:

  • Autoregressive (AR)-based features combined with an artificial neural network classifier yielded superior detection accuracy.
  • The system achieved a high accuracy of 91% in detecting emergency braking intention.
  • The proposed system successfully predicted braking intention approximately 600 ms before the executed braking event across ten subjects.

Conclusions:

  • The developed EEG-based system demonstrates high accuracy and early detection of emergency braking intention.
  • Autoregressive features show significant promise for real-time BCI applications in vehicles.
  • The findings support the feasibility of implementing brain-controlled vehicle systems for enhanced road safety.