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Real Time Driver's Drowsiness Detection by Processing the EEG Signals Stimulated with External Flickering Light
Amjad Hashemi1, Valiallah Saba2, Seyed Navid Resalat3
1Institute for Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences, Tehran, Iran.
Basic and Clinical Neuroscience
|December 2, 2014
Summary
This study developed a method to detect driver sleepiness using Steady State Visual Evoked Potentials (SSVEPs) from EEG signals. The system achieved 97% accuracy in distinguishing between open and closed eyes, crucial for driver safety.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Driver sleepiness is a significant safety concern.
- Electroencephalography (EEG) signals, particularly Steady State Visual Evoked Potentials (SSVEPs), offer a promising avenue for objective sleepiness detection.
- SSVEPs are reliable EEG signals generated in response to visual stimuli, commonly used in human-computer interface systems.
Purpose of the Study:
- To develop a system for detecting driver sleepiness.
- To classify driver states (eyes open vs. eyes closed) using SSVEPs.
- To evaluate the efficacy of different machine learning models for this classification task.
Main Methods:
- Utilized EEG signals to compute Steady State Visual Evoked Potentials (SSVEPs).
- Extracted signal features using Fourier transforms and power spectrum density.
- Employed Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural networks for classification.
Main Results:
- The classification method successfully discriminated between open and closed eye states.
- Achieved a high accuracy of 97% on test data.
- Demonstrated the effectiveness of SSVEP analysis for detecting physiological changes related to drowsiness.
Conclusions:
- The developed SSVEP-based classification method is highly accurate for detecting eye closure, a key indicator of driver sleepiness.
- This approach holds potential for real-time driver monitoring systems to enhance road safety.
- Further research can explore more advanced features and models for improved sleepiness detection.
