Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Macrophages Undergo M1-to-M2 Transition in Adipose Tissue Regeneration in a Rat Tissue Engineering Model.

Artificial organs·2016
Same author

Bone morphogenetic protein 9 (BMP9) induces effective bone formation from reversibly immortalized multipotent adipose-derived (iMAD) mesenchymal stem cells.

American journal of translational research·2016
Same author

The role of perineural invasion on head and neck adenoid cystic carcinoma prognosis: a systematic review and meta-analysis.

Oral surgery, oral medicine, oral pathology and oral radiology·2016
Same author

Heterotypic 3D tumor culture in a reusable platform using pneumatic microfluidics.

Lab on a chip·2016
Same author

Correction to 'Different effects of invader-native phylogenetic relatedness on invasion success and impact: a meta-analysis of Darwin's naturalization hypothesis'.

Proceedings. Biological sciences·2016
Same author

Real-time monitoring of oxidative injury of vascular endothelial cells and protective effect of quercetin using quartz crystal microbalance.

Analytical and bioanalytical chemistry·2016
Same journal

HF-SNVTA-FusionNet: high-frequency multi-domain EEG feature fusion from the substantia nigra and ventral tegmental area for Parkinson's disease classification.

Cognitive neurodynamics·2026
Same journal

Investigation of the effects of balance exercises on visuospatial skills using EEG brain oscillations.

Cognitive neurodynamics·2026
Same journal

MSCANet: a cross-attention-based multi-scale convolutional fusion neural network for EEG motor imagery classification.

Cognitive neurodynamics·2026
Same journal

Regulation of epileptiform discharges in thalamocortical model based on preview control theory.

Cognitive neurodynamics·2026
Same journal

Computational modeling of tyrosine hydroxylase pathway for dopamine synthesis in nerve cells: effect of tetrahydrobiopterin deficiency and oxidative stress.

Cognitive neurodynamics·2026
Same journal

From nonlinear neuronal dynamics to AI-optimized VLSI hardware: multiplier-free FPGA implementation of memristive FN-HR coupled neural networks for intelligent systems.

Cognitive neurodynamics·2026
See all related articles

Related Experiment Video

Updated: Jun 7, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.4K

Attention-based cross-frequency graph convolutional network for driver fatigue estimation.

Jianpeng An1, Qing Cai2, Xinlin Sun1

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China.

Cognitive Neurodynamics
|November 18, 2024
PubMed
Summary
This summary is machine-generated.

Driver fatigue estimation is critical for road safety. A new Attention-based Cross-Frequency Graph Convolutional Network (ACF-GCN) accurately predicts driver reaction times using electroencephalography (EEG) signals.

Keywords:
Dynamic connectivityElectroencephalography (EEG)Fatigue estimationGraph convolutional networkMulti-head attention mechanism

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.6K

Related Experiment Videos

Last Updated: Jun 7, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.4K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.6K

Area of Science:

  • Neuroscience
  • Machine Learning
  • Transportation Safety

Background:

  • Driver fatigue is a major cause of vehicle accidents and fatalities globally.
  • Electroencephalography (EEG) reliably predicts brain states, but its complex nature poses challenges for precise deep learning models.
  • Advancements in Deep Learning (DL) have improved brain state estimation, yet intricate EEG channel correlations require novel approaches.

Purpose of the Study:

  • To introduce an innovative Attention-based Cross-Frequency Graph Convolutional Network (ACF-GCN) for estimating driver reaction times.
  • To leverage EEG signals from theta, alpha, and beta bands for enhanced fatigue detection.
  • To explore brain dynamics and identify key frequency bands influencing fatigue estimation.

Main Methods:

  • Developed an Attention-based Cross-Frequency Graph Convolutional Network (ACF-GCN) model.
  • Utilized a multi-head attention mechanism to capture long-range dependencies across EEG channels and frequencies.
  • Employed a transformer encoder and Graph Convolutional Network (GCN) to learn feature maps and estimate driver reaction time.

Main Results:

  • The ACF-GCN model demonstrated superior performance compared to several state-of-the-art methods on a public dataset.
  • Analysis of the cross-frequency attention-score matrix revealed that theta and alpha bands are key influencers in fatigue estimation.
  • The study provides insights into the brain dynamics underlying multi-channel EEG signals for fatigue assessment.

Conclusions:

  • The ACF-GCN method offers a significant advancement in driver fatigue level estimation using EEG signals.
  • The findings highlight the importance of cross-frequency interactions and specific brainwave bands (theta, alpha) in predicting driver reaction times.
  • This research contributes to improving road safety through more accurate driver fatigue monitoring.