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

Brain network construction and analysis for epilepsy: A methodology review.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Long survival of PD-L1-positive mediastinal sarcomatoid carcinoma after immunotherapy and anti-angiogenic target therapy: A case report.

Oncology letters·2026
Same author

A Novel <i>MAP3K7</i> Variant Causing Loss of Function Identified in a Family With Cardiospondylocarpofacial Syndrome: Functional Validation and Molecular Insights.

Human mutation·2026
Same author

Fluid overload and competing risk: overlooked factors in ICU muscle ultrasound studies - Letter on Burgel et al.

Intensive & critical care nursing·2026
Same author

Heterocyclic modified paclitaxel prodrug nanoassemblies for stimuli-responsive delivery <i>via</i> lysosomal escape.

Acta pharmaceutica Sinica. B·2026
Same author

Phase-gradient-based initialization for lensless quantitative phase imaging.

Optics letters·2026
Same journal

Magnetic Resonance Spectroscopy Deep Learning with Magnetic Resonance Background Generator Enables In Vivo Metabolite Quantification of Hepatic Encephalopathy.

IEEE transactions on bio-medical engineering·2026
Same journal

Use of RPNIs and Implanted Electrodes for Prosthetic Wrist and Multi-Grip Hand Control during Functional Tasks: A Case Study.

IEEE transactions on bio-medical engineering·2026
Same journal

Healthy Limb Driven Prediction for Real Time Control of Unilateral Exoskeletons in Gait Rehabilitation.

IEEE transactions on bio-medical engineering·2026
Same journal

A Miniature Wearable Ultrasound System for Continuous Bladder Monitoring with Sleeping-Position-Robust Modeling Strategies.

IEEE transactions on bio-medical engineering·2026
Same journal

A Bi-objective Array Optimization Framework for Magnetocardiographic Source Imaging.

IEEE transactions on bio-medical engineering·2026
Same journal

A Dynamic Mutual Information Measure of Phase-Amplitude Coupling with Uncertainty Quantification.

IEEE transactions on bio-medical engineering·2026
See all related articles

Related Experiment Video

Updated: Jul 10, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K

Grouped Multivariate Variational Mode Decomposition With Application to EEG Analysis.

Jiawei Jian, Duanpo Wu, Jiuwen Cao

    IEEE Transactions on Bio-Medical Engineering
    |November 20, 2023
    PubMed
    Summary
    This summary is machine-generated.

    A new grouped multivariate variational mode decomposition (GMVMD) method effectively extracts common frequencies from multigroup data. This advanced technique improves mode alignment and reduces signal error compared to standard multivariate variational mode decomposition (MVMD).

    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
    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
    11:15

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

    Published on: June 27, 2013

    33.8K

    Related Experiment Videos

    Last Updated: Jul 10, 2025

    Basics of Multivariate Analysis in Neuroimaging Data
    06:35

    Basics of Multivariate Analysis in Neuroimaging Data

    Published on: July 24, 2010

    16.9K
    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
    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
    11:15

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

    Published on: June 27, 2013

    33.8K

    Area of Science:

    • Signal Processing
    • Data Analysis
    • Biomedical Engineering

    Background:

    • Multivariate variational mode decomposition (MVMD) is a powerful tool for analyzing complex signals.
    • Existing MVMD methods face challenges in effectively processing multigroup data with correlated regional channels.

    Purpose of the Study:

    • To introduce a novel grouped multivariate variational mode decomposition (GMVMD) method for enhanced multigroup data analysis.
    • To address the limitations of MVMD in extracting common frequencies among correlated channels.

    Main Methods:

    • Developed a frequencies grouping algorithm for classifying common frequencies into distinct groups.
    • Formulated a generic variational optimization model extended from MVMD.
    • Employed the alternating direction method of multipliers (ADMM) for optimal solution derivation.

    Main Results:

    • GMVMD successfully groups real-world electroencephalogram (EEG) data based on similar center frequencies.
    • Experimental results demonstrate the effectiveness and superiority of GMVMD over MVMD.
    • The method introduces a user-defined parameter for controlling the number of clusterings.

    Conclusions:

    • GMVMD offers improved mode-alignment and reduced signal error compared to MVMD.
    • The proposed method achieves more accurate center frequency extraction.
    • GMVMD provides a significant advancement for analyzing correlated multigroup data.