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

MsGCN: a multi-stream graph convolutional network for multiband PLV graph fusion in EEG-based biometric identification.

Frontiers in computational neuroscience·2026
Same author

Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations.

Nature communications·2026
Same author

Dual controllability de-differentiation of functional brain networks in major depressive disorder: Insights from large-scale neuroimaging and transcriptomic integration.

Journal of affective disorders·2026
Same author

Functional connectivity-based classification and subtyping of major depression for precision mental health: An ensemble graph neural network approach.

PLOS digital health·2026
Same author

Multi-Site Transfer Classification of Major Depressive Disorder: An fMRI Study in 3335 Subjects.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Transfer learning from 2D natural images to 4D fMRI brain images via geometric mapping.

Medical image analysis·2026

Related Experiment Video

Updated: Dec 25, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.6K

Nonparametric Bayesian Prior Inducing Deep Network for Automatic Detection of Cognitive Status.

Edmond Q Wu, Dewen Hu, Ping-Yu Deng

    IEEE Transactions on Cybernetics
    |March 24, 2020
    PubMed
    Summary

    This study introduces a novel gamma deep belief network for recognizing pilot brain fatigue by extracting complex cognitive features from electroencephalogram (EEG) data. The proposed methods enhance accuracy and stability in fatigue identification.

    More Related Videos

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    16.1K
    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    7.8K

    Related Experiment Videos

    Last Updated: Dec 25, 2025

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    1.6K
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    16.1K
    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    7.8K

    Area of Science:

    • Neuroscience
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Pilot fatigue poses significant safety risks.
    • Accurate recognition of cognitive fatigue is crucial for aviation safety.
    • Existing methods struggle with extracting and identifying complex brain fatigue characteristics.

    Purpose of the Study:

    • To develop a robust method for recognizing pilot brain fatigue.
    • To extract multilayer deep representations of high-dimensional cognitive data.
    • To improve the accuracy and stability of fatigue identification.

    Main Methods:

    • Proposed a gamma deep belief network (GBN) for feature extraction.
    • Utilized Dirichlet distributed connection weights and Gibbs sampling for network structure reasoning.
    • Introduced a smoothed pseudo affine Wigner-Ville distribution for 3-D time-frequency analysis of electroencephalogram (EEG) signals to prevent modal aliasing.

    Main Results:

    • The gamma deep belief network effectively extracted deep representations from cognitive data.
    • The smoothed pseudo affine Wigner-Ville distribution successfully extracted 3-D instantaneous time-frequency spectra.
    • Experimental results demonstrated satisfactory recognition accuracy and stability of the proposed model.

    Conclusions:

    • The developed GBN and Wigner-Ville distribution method offer a promising approach for pilot brain fatigue recognition.
    • The findings contribute to enhancing aviation safety through improved fatigue monitoring.
    • The model shows potential for real-world application in monitoring pilot cognitive states.