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Dual-Domain Fusion Graph Convolutional Network for EEG-Based Driving Fatigue Detection.

Hui Xiong^1,2, Shuaiqi Chang^1,2, Jinzhen Liu^1,2

¹School of Control Science and Engineering, Tiangong University, Tianjin, China.

The European Journal of Neuroscience

|November 18, 2025

Summary

This summary is machine-generated.

A new Dual-Domain Fusion Graph Convolutional Network (DDFGCN) model improves driving fatigue detection using electroencephalography (EEG) by analyzing brain topology. This advanced method enhances road safety through more accurate fatigue state prediction.

Keywords:

driving fatigue detection dual‐domain fusion electroencephalography graph convolution network

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Area of Science:

Neuroscience
Machine Learning
Road Safety Engineering

Background:

Driving fatigue is a major cause of traffic accidents.
Electroencephalography (EEG) offers objective and accurate fatigue detection.
Existing EEG methods underutilize brain topology and inter-electrode information.

Purpose of the Study:

To propose a novel Dual-Domain Fusion Graph Convolutional Network (DDFGCN) model for enhanced driving fatigue detection.
To leverage both local and global brain connectivity for multi-level feature aggregation.
To improve the accuracy and reliability of EEG-based fatigue detection systems.

Main Methods:

Utilized multi-scale temporal convolution for extracting dynamic EEG features.
Developed two brain map construction methods to capture local and global channel dependencies.
Integrated features and employed classification modules for fatigue state prediction.

Main Results:

Achieved high accuracy rates of 94.67% on the SADT dataset and 95.6% on the SEED-VIG dataset.
Demonstrated superior classification performance compared to existing methods.
Validated the model's ability to integrate local brain activity and remote dependencies.

Conclusions:

The DDFGCN model effectively enhances relational modeling of the entire scalp for improved fatigue detection.
This approach offers a promising new method for fatigue driving detection technology.
The findings highlight the importance of considering brain topology in EEG-based fatigue analysis.