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

Inflammatory and Molecular Mechanisms of Adenomyosis Associated Pain: Insights from Multiple Analytic Approaches.

Journal of pain research·2026
Same author

The developing tendon and enthesis are hypoxic and rely on hypoxia-inducible factor 1a during postnatal development.

Development (Cambridge, England)·2026
Same author

Generation of spermatid-like cells from testicular biopsies obtained from prepubertal boys.

F&S science·2026
Same author

Computational investigation of intermittent pneumatic compression operating parameters and tissue mechanics in lower-limb venous hemodynamics.

Computers in biology and medicine·2026
Same author

Lipid metabolism dysregulation in solar lentigo: a multi-system-level analysis reveals membrane instability and energy homeostasis disruption.

Frontiers in cell and developmental biology·2026
Same author

Six-month randomized, double-blind trial of transcranial direct current stimulation in mild Alzheimer's dementia: domain-specific cognitive and neuropsychiatric signals.

Frontiers in neurology·2026

Related Experiment Video

Updated: Dec 6, 2025

The Combination of Transcranial Alternating Current Stimulation and Electroencephalogram
06:14

The Combination of Transcranial Alternating Current Stimulation and Electroencephalogram

Published on: October 10, 2025

231

EEG Artifact Removal by Bayesian Deep Learning & ICA.

Sangmin S Lee, Kiwon Lee, Guiyeom Kang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary

    This study introduces a Bayesian deep learning method with an attention module to accurately remove artifacts from electroencephalogram (EEG) signals, improving data quality for analysis.

    More Related Videos

    A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
    08:23

    A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

    Published on: November 13, 2016

    11.6K
    Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
    10:23

    Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

    Published on: June 23, 2023

    2.4K

    Related Experiment Videos

    Last Updated: Dec 6, 2025

    The Combination of Transcranial Alternating Current Stimulation and Electroencephalogram
    06:14

    The Combination of Transcranial Alternating Current Stimulation and Electroencephalogram

    Published on: October 10, 2025

    231
    A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
    08:23

    A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

    Published on: November 13, 2016

    11.6K
    Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
    10:23

    Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

    Published on: June 23, 2023

    2.4K

    Area of Science:

    • Neuroscience
    • Signal Processing
    • Machine Learning

    Background:

    • Artifacts in electroencephalogram (EEG) signals can significantly distort analysis results.
    • Current artifact removal methods, often based on independent component analysis (ICA), rely on classifying independent components (ICs) as artifacts for removal.

    Purpose of the Study:

    • To develop an improved method for classifying artifacts in EEG signals.
    • To enhance the accuracy and reliability of artifact removal in EEG signal processing.

    Main Methods:

    • A novel approach utilizing Bayesian deep learning combined with an attention module was developed.
    • This method computes a probability value to classify ICs as artifacts, addressing ambiguous cases.
    • The attention module was incorporated to enhance classification performance and identify key regions of focus.

    Main Results:

    • The proposed method demonstrated improved classification accuracy for identifying artifactual ICs.
    • The attention module effectively guided the classifier's focus, contributing to higher accuracy.
    • The system provides a probability output, allowing for nuanced handling of potentially artifactual components.

    Conclusions:

    • The integration of Bayesian deep learning and attention modules offers a promising advancement in EEG artifact removal.
    • This technique enhances the precision of artifact identification, leading to cleaner EEG data.
    • The method's ability to handle ambiguity and provide focus maps represents a significant step forward in EEG signal processing.