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Driver drowsiness estimation using EEG signals with a dynamical encoder-decoder modeling framework.

Sadegh Arefnezhad¹, James Hamet^2,3, Arno Eichberger⁴

¹Institute of Automotive Engineering, Graz University of Technology, 8010, Graz, Austria. s.arefnezhad@tugraz.at.

Scientific Reports

|February 17, 2022

Summary

This summary is machine-generated.

This study introduces a new dynamical model using EEG signals to estimate driver drowsiness by tracking eyelid closure (PERCLOS). The model reliably identifies neural biomarkers like Theta and Delta powers, enabling real-time drowsiness detection for safer driving.

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Area of Science:

Neuroscience
Automotive Safety
Signal Processing

Background:

Driver drowsiness is a major cause of road accidents.
Electroencephalography (EEG) signals are key physiological indicators of drowsiness.
Existing methods for drowsiness detection require improvement in real-time accuracy.

Purpose of the Study:

To develop a novel dynamical modeling solution for estimating instantaneous driver drowsiness using EEG signals.
To identify neural features in EEG data that correlate with the Percentage of Eyelid Closure (PERCLOS).
To validate the model's performance in real-world driving conditions.

Main Methods:

Utilized a dynamical modeling framework to analyze EEG signals.
Employed PERCLOS as the ground truth measure for driver drowsiness.
Applied Bayesian filtering for real-time PERCLOS estimation.
Investigated a dataset from 18 driving tests with 13 participants.

Main Results:

The model achieved robust and repeatable PERCLOS estimation with an average RMSE of 0.117.
Identified Theta and Delta power as potential neural biomarkers for drowsiness.
Found that Theta and Delta powers increase with rising PERCLOS levels (73% and 66% correlation, respectively).

Conclusions:

The proposed EEG-based dynamical model offers a reliable method for real-time driver drowsiness estimation.
The identified neural biomarkers (Theta and Delta powers) can enhance drowsiness detection systems.
This approach has the potential to significantly improve road safety by enabling proactive driver monitoring.