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

Direct photodegradation of aromatic carbamate pesticides: Kinetics and mechanisms in aqueous vs. non-aqueous media.

Journal of hazardous materials·2025
Same author

Alterations of Motor Unit Characteristics Associated With Muscle Fatigue.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2023
Same author

An integrated approach for quantifying trace metal sources in surface soils of a typical farmland in the three rivers plain, China.

Environmental pollution (Barking, Essex : 1987)·2023
Same author

Organic ligands activate the dark formation of hydroxyl radicals (HO<sup>•</sup>) in surface soil/sediment: Yields, mechanisms, and applications.

Journal of hazardous materials·2023
Same author

Actional Mechanisms of Active Ingredients in Functional Food Adlay for Human Health.

Molecules (Basel, Switzerland)·2022
Same author

[A new method for high-density surface electromyography decomposition in dynamic muscle contraction].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi·2021

Related Experiment Video

Updated: Jul 17, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.4K

Enhanced Dynamic Surface EMG Decomposition Using the Non-Negative Matrix Factorization and Three-Dimensional Motor

Jinbao He, Yang Liu, Sheng Li

    IEEE Transactions on Bio-Medical Engineering
    |September 1, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for decomposing surface electromyography (sEMG) signals, improving accuracy in analyzing muscle activity during dynamic movements. The approach enhances the understanding of neuromuscular function and diseases.

    More Related Videos

    Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality
    08:09

    Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

    Published on: September 3, 2015

    11.0K
    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
    08:15

    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

    Published on: March 28, 2025

    632

    Related Experiment Videos

    Last Updated: Jul 17, 2025

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
    11:25

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

    Published on: July 26, 2013

    43.4K
    Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality
    08:09

    Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

    Published on: September 3, 2015

    11.0K
    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
    08:15

    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

    Published on: March 28, 2025

    632

    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Signal Processing

    Background:

    • Surface electromyography (sEMG) signal decomposition is crucial for neuromuscular research and diagnosing neuromuscular diseases.
    • Dynamic sEMG decomposition presents significant technical challenges due to signal complexity and movement artifacts.

    Purpose of the Study:

    • To develop and evaluate a novel two-step approach for dynamic surface EMG decomposition.
    • To enhance the accuracy and reliability of motor unit (MU) decomposition from sEMG signals.

    Main Methods:

    • A two-step decomposition method combining Non-negative Matrix Factorization (NMF) and linear minimum mean square error estimation.
    • Extraction of estimated firing trains (EFTs) using NMF and linear minimum mean square error estimation.
    • Classification of EFTs into motor units (MUs) based on their three-dimensional (3D) spatial positions.

    Main Results:

    • Simulated sEMG data showed high reconstruction accuracy for MUAPTs (89-95%) across different signal-to-noise ratios.
    • Experimental sEMG data achieved high decomposition accuracy (90-91%) in various evaluation scenarios.
    • The method demonstrated robust performance in dynamic sEMG decomposition.

    Conclusions:

    • The proposed NMF-based approach effectively reduces dimensionality while preserving information for sEMG decomposition.
    • Incorporating 3D spatial information of MUs improves classification accuracy, particularly during dynamic contractions.
    • The developed algorithm shows good performance and reliability for dynamic surface EMG decomposition.