Automated Driver Drowsiness Detection from Single-Channel EEG Signals Using Convolutional Neural Networks and Transfer Learning

Ghadami, Ali; Mohammadzadeh, Mohammad; Taghimohammadi, Mohammadreza; Taheri, Alireza

doi:10.1109/itsc55140.2022.9922199

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Hybrid Approach for Driving Behavior Recognition: Integration of CNN and Transformer-Encoder with EEG data

Wang,

Liu,

Qin

et al. 2023

2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall)

View full text Add to dashboard Cite

A Hybrid Approach for Driving Behavior Recognition: Integration of CNN and Transformer-Encoder with EEG data

Wang,

Liu,

Qin

et al. 2023

2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall)

View full text Add to dashboard Cite

A Hybrid Deep Neural Network Approach to Recognize Driving Fatigue Based on EEG Signals

Alghanim,

Attar,

Rezaee

et al. 2024

International Journal of Intelligent Systems

View full text Add to dashboard Cite

Electroencephalography (EEG) data serve as a reliable method for fatigue detection due to their intuitive representation of drivers’ mental processes. However, existing research on feature generation has overlooked the effective and automated aspects of this process. The challenge of extracting features from unpredictable and complex EEG signals has led to the frequent use of deep learning models for signal classification. Unfortunately, these models often neglect generalizability to novel subjects. To address these concerns, this study proposes the utilization of a modified deep convolutional neural network, specifically the Inception‐dilated ResNet architecture. Trained on spectrograms derived from segmented EEG data, the network undergoes analysis in both temporal and spatial‐frequency dimensions. The primary focus is on accurately detecting and classifying fatigue. The inherent variability of EEG signals between individuals, coupled with limited samples during fatigue states, presents challenges in fatigue detection through brain signals. Therefore, a detailed structural analysis of fatigue episodes is crucial. Experimental results demonstrate the proposed methodology’s ability to distinguish between alertness and sleepiness, achieving average accuracy rates of 98.87% and 82.73% on Figshare and SEED‐VIG datasets, respectively, surpassing contemporary methodologies. Additionally, the study examines frequency bands’ relative significance to further explore participants’ inclinations in states of alertness and fatigue. This research paves the way for deeper exploration into the underlying factors contributing to mental fatigue.

show abstract

Automated Driver Drowsiness Detection from Single-Channel EEG Signals Using Convolutional Neural Networks and Transfer Learning

Cited by 2 publications

References 23 publications

A Hybrid Approach for Driving Behavior Recognition: Integration of CNN and Transformer-Encoder with EEG data

A Hybrid Approach for Driving Behavior Recognition: Integration of CNN and Transformer-Encoder with EEG data

A Hybrid Deep Neural Network Approach to Recognize Driving Fatigue Based on EEG Signals

Contact Info

Product

Resources

About