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Vehicle driver drowsiness detection method using wearable EEG based on convolution neural network.

Miankuan Zhu¹, Jiangfan Chen¹, Haobo Li¹

¹School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China.

Neural Computing & Applications

|May 10, 2021

Summary

This summary is machine-generated.

This study introduces a novel method for detecting driver drowsiness using wearable electroencephalography (EEG) and deep learning. The system accurately identifies drowsy driving, enhancing vehicle safety through early warnings.

Keywords:

Convolution neural network (CNN)Drowsiness detection Electroencephalographic (EEG)

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Area of Science:

Neuroscience
Computer Science
Automotive Safety

Background:

Drowsy driving poses a significant risk, leading to severe traffic accidents.
Existing driver monitoring systems often lack accuracy and real-time capabilities.

Purpose of the Study:

To develop and validate a reliable method for detecting vehicle driver drowsiness using wearable electroencephalography (EEG).
To implement a deep learning-based approach for classifying EEG signals indicative of drowsiness.

Main Methods:

Collected EEG data from drivers in simulated drowsy and awake driving states using a wearable brain-computer interface (BCI).
Trained Convolutional Neural Networks (CNNs), including Inception and modified AlexNet modules, to classify EEG signals.
Developed an early warning strategy to alert drivers when drowsiness is detected.

Main Results:

The Inception module-based CNN achieved 95.59% classification accuracy.
The modified AlexNet module-based CNN reached 94.68% classification accuracy.
Both models demonstrated high accuracy within a one-second time window.

Conclusions:

The proposed EEG-based drowsiness detection method using CNNs is feasible and effective for enhancing vehicle driving safety.
Wearable EEG technology combined with deep learning offers a promising solution for real-time driver monitoring.