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 Experiment Video

Updated: May 14, 2026

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces
05:36

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

SE-Driven Dynamic Convolution for Adaptive EEG-Based Driver Fatigue Detection Across Spectral, Spatial, and Temporal

Tianle Zhou1, Jin Cheng2, Jinbiao Zhang2

  • 1Mengxi Honors College, Jiangsu University, Zhenjiang 212013, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

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

Nr1d1 Mediates Microglial Inflammatory Activation Induced by Intermittent Hypoxia: A Transcriptomic and Machine Learning Study.

Journal of molecular neuroscience : MN·2026
Same author

Illuminating the Interface: In Situ Optical Insights Into Two-Electron ORR Pathways for H<sub>2</sub>O<sub>2</sub> Electrosynthesis.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Instability in sleep architecture and intermittent hypoxia underlie cognitive impairment in patients with obstructive sleep apnea.

Sleep & breathing = Schlaf & Atmung·2026
Same author

Magnesium leachates suppress inflammatory phenotype of vascular smooth muscle cells by downregulating the NF-κB p50/KLF5 signaling.

Acta biomaterialia·2026
Same author

Biomimetic core-shell nanofibrous scaffolds with an organic montmorillonite interlayer for tissue engineering.

Frontiers in bioengineering and biotechnology·2026
Same author

Associations of probiotics, prebiotics, synbiotics, and yogurt supplements with oxidative balance scores and all-cause and cardiovascular mortalities in neuropsychiatric disorders.

American heart journal plus : cardiology research and practice·2026

This study introduces DCAMNet, a novel deep learning model for electroencephalogram (EEG)-based driver fatigue detection. DCAMNet achieves high accuracy in detecting driver fatigue, even for unseen drivers, paving the way for safer transportation.

Area of Science:

  • Neuroscience
  • Machine Learning
  • Transportation Safety

Background:

  • Driver fatigue is a major cause of road accidents.
  • Existing EEG-based fatigue detection methods struggle with inter-subject variability and complex data dynamics.
  • Current approaches often process spectral, spatial, and temporal features independently, limiting performance.

Purpose of the Study:

  • To develop a unified, lightweight deep learning model for robust EEG-based driver fatigue detection.
  • To address signal-level challenges including spectral variability and coupled dynamics.
  • To improve the accuracy and efficiency of fatigue detection for real-world deployment.

Main Methods:

  • Introduced DCAMNet, a compact Convolutional Neural Network (CNN) with 12.3K parameters.
Keywords:
EEG sensor signal processingSEED-VIGcross-subject generalizationdriver fatigue detectiondynamic convolutionlightweight neural networksqueeze-and-excitation attention

More Related Videos

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

Related Experiment Videos

Last Updated: May 14, 2026

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces
05:36

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

  • Utilized an SE-driven dynamic convolution block for adaptive spectral sensitivity.
  • Employed a spatial convolution block for cortical pattern encoding and a temporal attention block for fatigue dynamics.
  • Evaluated on SEED-VIG and MESD datasets using subject-mixed and leave-one-subject-out (LOSO) protocols.

    • Main Results:

    • Under LOSO cross-validation, DCAMNet achieved 85.43% accuracy on SEED-VIG and 79±5% on MESD, outperforming baselines.
    • Subject-mixed protocols yielded upper-bound accuracies of 97.47% (SEED-VIG) and 96.52% (MESD).
    • Demonstrated a low inference latency of 1.35 ms on a standard GPU.

    Conclusions:

    • DCAMNet effectively addresses key challenges in EEG-based driver fatigue detection.
    • The model's lightweight architecture and high accuracy suggest potential for real-time embedded systems in vehicles.
    • Further on-device validation is recommended for automotive hardware integration.