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

Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

60.5K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
60.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Contemporary utilization of ginseng among individuals with health needs: A nationwide questionnaire survey.

Journal of ginseng research·2026
Same author

Contemporary perceptions of ginseng among individuals with health needs: A nationwide questionnaire survey.

Journal of ginseng research·2026
Same author

Quercetin Delivered by Mesenchymal Stem Cell-Derived Exosomes Improves Liver Fibrosis via the PI3K/Akt Signaling Pathway.

ACS omega·2026
Same author

Mapping the neuroimaging landscape of inflammatory bowel disease: a bibliometric analysis and systematic scoping review.

Frontiers in neuroscience·2026
Same author

Emergence of Turing patterns in complex networks: A partial link activation approach.

Physical review. E·2026
Same author

Progressive Fusion of Multi-Scale Mamba Context and Local Detail Priors for Infrared Small Target Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks for In-Home Activity Learning of Mild Cognitive Impairment.

IEEE journal of biomedical and health informatics·2026
Same journal

Integrating Multi-View Residue Graph and Protein Language Model for Cell-Penetrating Peptide Prediction via Global-Local Graph Aggregation and Cross-Attentive Fusion.

IEEE journal of biomedical and health informatics·2026
Same journal

An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics·2026
Same journal

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

IEEE journal of biomedical and health informatics·2026
Same journal

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026
Same journal

Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

IEEE journal of biomedical and health informatics·2026
See all related articles

Related Experiment Video

Updated: Apr 19, 2026

Extraction of the EPP Component from the Surface EMG
07:16

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

13.0K

Surface EMG decomposition based on K-means clustering and convolution kernel compensation.

Yong Ning, Xiangjun Zhu, Shanan Zhu

    IEEE Journal of Biomedical and Health Informatics
    |December 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    A novel K-means clustering-Modified Convolution Kernel Compensation (KmCKC) method accurately decomposes multichannel surface electromyogram (EMG) signals. This approach reliably reconstructs motor unit innervation pulse trains (IPTs) from simulated and experimental data, even at low signal-to-noise ratios.

    More Related Videos

    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

    44.3K
    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

    1.5K

    Related Experiment Videos

    Last Updated: Apr 19, 2026

    Extraction of the EPP Component from the Surface EMG
    07:16

    Extraction of the EPP Component from the Surface EMG

    Published on: December 16, 2009

    13.0K
    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

    44.3K
    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

    1.5K

    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Signal Processing

    Background:

    • Multichannel surface electromyogram (EMG) decomposition is crucial for understanding motor control.
    • Accurate identification of motor unit (MU) activity from complex EMG signals remains a challenge.

    Purpose of the Study:

    • To develop and validate a novel approach for multichannel surface EMG decomposition.
    • To improve the accuracy and reliability of reconstructing motor unit innervation pulse trains (IPTs).

    Main Methods:

    • Combined K-mean clustering (KMC) for initial estimation of innervation pulse trains (IPTs).
    • Employed a modified convolution kernel compensation (CKC) method with a multistep iterative process for IPT refinement.
    • Evaluated the K-means clustering-Modified CKC (KmCKC) approach using simulated and experimental surface EMG data.

    Main Results:

    • Successfully reconstructed 10 IPTs from simulated EMG signals with >90% true positive rate (TPR) at -10 dB signal-to-noise ratio (SNR).
    • Extracted >10 motor units from 64-channel experimental EMG signals of the first dorsal interosseous (FDI) muscle.
    • Demonstrated high reliability and capability with >92% common motor units and pulses in a two-source test.

    Conclusions:

    • The proposed KmCKC approach offers high accuracy for multichannel surface EMG decomposition.
    • The method is robust and reliable across different contraction levels and signal qualities.
    • KmCKC shows significant potential for advancing EMG signal analysis in research and clinical applications.