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Updated: Feb 7, 2026
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Modeling the Functional Network for Spatial Navigation in the Human Brain
Published on: October 13, 2023
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Assessment of driver drowsiness using electroencephalogram signals based on multiple functional brain networks
Jichi Chen1, Hong Wang1, Chengcheng Hua1
1Department of Mechanical Engineering and Automation, Northeastern University, 110819 Shenyang, Liaoning, China.
Summary
This study reveals significant changes in functional brain networks (FBN) between alert and drowsy driving states. Combining synchronization likelihood and minimum spanning tree methods accurately detects driver drowsiness using EEG data.
Area of Science:
- Neuroscience
- Cognitive Science
- Biomedical Engineering
Background:
- Driver drowsiness is a major safety concern.
- Detecting drowsiness in real-time is crucial for preventing accidents.
- Understanding the neurophysiological underpinnings of drowsiness is essential.
Purpose of the Study:
- To investigate functional brain network (FBN) changes from alert to drowsy states.
- To identify neurophysiological indicators for detecting driver drowsiness.
- To evaluate the effectiveness of novel FBN analysis methods.
Main Methods:
- A driving simulation experiment with 15 participants.
- Electroencephalography (EEG) signal decomposition using wavelet packet transform (WPT).
- Application of combined synchronization likelihood (SL) and minimum spanning tree (MST) for FBN analysis and feature extraction.
Main Results:
- Significant differences in FBN features between alert and drowsy states were observed.
- The combined SL and MST approach enhanced classification accuracy for drowsiness detection.
- The K Nearest Neighbors (KNN) classifier achieved the highest accuracy (98.6%), precision (98.3%), sensitivity (98.8%), and specificity (98.9%).
Conclusions:
- FBN analysis provides valuable insights into driver drowsiness mechanisms.
- The proposed methodology, combining SL and MST, is a promising tool for real-time drowsiness detection systems.
- This approach can serve as a reference for future research in neurophysiological monitoring.
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