MSDA-Net: Multiscale Spatiotemporal Dual-Attention Network for EEG-Based Driver Fatigue Detection

Isah Bello ¹, Moeed Sehnan ¹, Weidong Dang ¹

⁰School of Electrical and Information Engineering, Tianjin University, Tianjin, China.

Journal of Sleep Research +

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October 30, 2025

View abstract on PubMed

Summary

Driver fatigue poses a significant road safety risk. A new multiscale spatiotemporal dual-attention network (MSDA-Net) effectively detects fatigue using electroencephalogram (EEG) signals.

Area Of Science

Neuroscience
Road Safety
Machine Learning

Background

Driver fatigue is a major cause of road accidents, responsible for about 20% of fatal crashes globally.
Electroencephalogram (EEG) signals are crucial for fatigue detection, but existing methods struggle with complex spatiotemporal patterns.
Advanced signal processing and deep learning are needed to accurately model EEG for fatigue assessment.

Purpose Of The Study

To develop an advanced deep learning model for accurate driver fatigue detection.
To address the limitations of current methods in capturing complex spatiotemporal EEG dynamics.
To enhance road safety by providing a reliable fatigue monitoring solution.

Main Methods

Proposed MSDA-Net: a multiscale spatiotemporal dual-attention network.
Integrated multiscale CNNs, GRUs, and dual-attention mechanisms for dynamic prioritization of spatial and temporal features.
Utilized a 4D differential entropy feature extraction method from raw EEG signals.

Main Results

MSDA-Net achieved state-of-the-art performance on the SEED-VIG dataset.
The model significantly outperformed existing fatigue detection methods.
Demonstrated effective capture of critical spatiotemporal patterns in EEG data.

Conclusions

MSDA-Net offers a novel and effective approach to driver fatigue detection using EEG.
The findings provide valuable insights for brain fatigue research and development in the field.
This technology has the potential to significantly improve road safety.

Keywords:

4D feature representation EEG differential entropy driver fatigue detection dual attention mechanism