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

Time-frequency-spatial channel attention network for semantic decoding: an exploratory EEG study.

Medical & biological engineering & computing·2026
Same author

Disentangled Multimodal Spatiotemporal Learning for Hybrid EEG-fNIRS Brain-Computer Interface.

IEEE transactions on bio-medical engineering·2026
Same author

Heart Rate Variability via Poincaré Mapping as an Early Biomarker Post-Cardiac Arrest.

IEEE transactions on bio-medical engineering·2025
Same author

Investigating the dynamics of intracranial pressure and cerebral autoregulation during extracorporeal cardiopulmonary resuscitation using a porcine model.

Resuscitation plus·2025
Same author

High-Granularity Machine Learning Prediction of Acute Brain Injury in Patients Receiving Venoarterial Extracorporeal Membrane Oxygenation.

ASAIO journal (American Society for Artificial Internal Organs : 1992)·2025
Same author

Tiny Data Is Sufficient: A Generalizable CNN Architecture for Temporal Domain Long Sequence Identification.

IEEE transactions on neural networks and learning systems·2025

Related Experiment Video

Updated: Jul 21, 2025

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.3K

Self-Attentive Channel-Connectivity Capsule Network for EEG-Based Driving Fatigue Detection.

Chuangquan Chen, Zhouyu Ji, Yu Sun

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |July 26, 2023
    PubMed
    Summary

    This study introduces a new Self-Attentive Channel-Connectivity Capsule Network (SACC-CapsNet) for detecting driving fatigue using electroencephalography (EEG) signals. The model effectively identifies key brain regions and inter-channel relationships, even with limited data.

    More Related Videos

    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
    08:51

    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

    Published on: November 1, 2019

    5.7K
    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

    4.5K

    Related Experiment Videos

    Last Updated: Jul 21, 2025

    Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
    05:19

    Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

    Published on: July 7, 2023

    2.3K
    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
    08:51

    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

    Published on: November 1, 2019

    5.7K
    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

    4.5K

    Area of Science:

    • Neuroscience
    • Machine Learning
    • Biomedical Engineering

    Background:

    • Deep neural networks show promise for electroencephalography (EEG)-based driving fatigue detection.
    • Existing models often neglect crucial inter-channel EEG relationships and require extensive training data.
    • Data collection for EEG studies is costly and time-consuming, limiting model development.

    Purpose of the Study:

    • To develop a novel deep learning model, SACC-CapsNet, for improved EEG-based driving fatigue detection.
    • To address limitations of existing models by incorporating inter-channel relations and reducing data dependency.
    • To identify informative brain regions and temporal dynamics associated with driving fatigue.

    Main Methods:

    • Proposed Self-Attentive Channel-Connectivity Capsule Network (SACC-CapsNet) for EEG fatigue detection.
    • Employed a temporal-channel attention module to refine EEG signals and identify critical channels.
    • Utilized a channel covariance matrix and selective kernel attention to capture inter-channel relationships.
    • Incorporated a capsule neural network for effective learning with limited data.

    Main Results:

    • SACC-CapsNet significantly outperformed state-of-the-art methods in EEG-based driving fatigue detection.
    • The frontal pole was identified as the most informative brain region for fatigue detection, followed by parietal and central regions.
    • The temporal-channel attention module enhanced the significance of critical brain regions.
    • The model effectively preserved valuable information regarding brain region connectivity.

    Conclusions:

    • SACC-CapsNet offers a robust and data-efficient solution for EEG-based driving fatigue detection.
    • The model's ability to capture inter-channel relations and focus on critical brain regions enhances detection accuracy.
    • Findings highlight the importance of frontal pole activity in driving fatigue and the potential of attention mechanisms in EEG analysis.