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

Motor Units00:46

Motor Units

61.7K
A motor unit consists of two main components: a single efferent motor neuron (i.e., a neuron that carries impulses away from the central nervous system) and all of the muscle fibers it innervates. The motor neuron may innervate multiple muscle fibers, which are single cells, but only one motor neuron innervates a single muscle fiber.
61.7K
Motor Units01:13

Motor Units

7.5K
The motor unit is a fundamental component of the neuromuscular system and plays a crucial role in coordinating muscle contractions. It consists of a somatic motor neuron, which connects and controls multiple skeletal muscle fibers, forming a single functional segment. The axon of the motor neuron branches out and establishes synaptic connections known as neuromuscular junctions with individual muscle fibers within the motor unit.
Motor units come in different sizes, with smaller units...
7.5K
Motor Unit Stimulation01:20

Motor Unit Stimulation

3.5K
When the neuron of a motor unit fires an action potential, it triggers a series of events, leading to a twitch contraction in the muscle fibers. The process of excitation-contraction coupling is crucial in relaying the action potential to the muscle fibers.
The latent period of contraction marks the onset of excitation-contraction coupling, when the action potential propagates across the sarcolemma, preparing the muscle fibers for contraction. As the fibers enter the contraction phase, the...
3.5K

You might also read

Related Articles

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

Sort by
Same author

Spinal mechanisms in postactivation potentiation: facilitation of presynaptic inhibition contrasts H-reflex amplitude reduction.

Journal of neurophysiology·2026
Same author

Motoneuron excitability in Parkinson's disease: effects of dopaminergic medication.

Journal of neurophysiology·2026
Same author

Motor unit discharge properties of the vastii muscles and their modulation with contraction level depend on the knee-joint angle.

Journal of applied physiology (Bethesda, Md. : 1985)·2025
Same author

The Efficiency of Manual Editing of High-Density Surface Electromyogram Decomposition Depends on the Recorded Muscle and Contraction Level but Less on the Operator's Experience.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2025
Same author

Sex Differences in Motor Unit Behavior in Patients With Parkinson's Disease.

The European journal of neuroscience·2025
Same author

Twenty-one days of bed rest alter motor unit properties and neuromuscular junction transmission in young adults.

Journal of applied physiology (Bethesda, Md. : 1985)·2025

Related Experiment Video

Updated: Jan 18, 2026

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

44.0K

Real-time motor unit identification from high-density surface EMG.

Vojko Glaser, Ales Holobar, Damjan Zazula

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |March 12, 2013
    PubMed
    Summary
    This summary is machine-generated.

    A new real-time Convolution Kernel Compensation (CKC) method decomposes high-density surface electromyograms (EMG) efficiently. This technique accurately identifies motor unit (MU) discharges in real-time, offering a significant advancement for EMG analysis.

    More Related Videos

    Extracellularly Identifying Motor Neurons for a Muscle Motor Pool in Aplysia californica
    13:37

    Extracellularly Identifying Motor Neurons for a Muscle Motor Pool in Aplysia californica

    Published on: March 25, 2013

    12.1K
    Simultaneous Intracellular Recording of a Lumbar Motoneuron and the Force Produced by its Motor Unit in the Adult Mouse In vivo
    13:07

    Simultaneous Intracellular Recording of a Lumbar Motoneuron and the Force Produced by its Motor Unit in the Adult Mouse In vivo

    Published on: December 5, 2012

    15.2K

    Related Experiment Videos

    Last Updated: Jan 18, 2026

    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

    44.0K
    Extracellularly Identifying Motor Neurons for a Muscle Motor Pool in Aplysia californica
    13:37

    Extracellularly Identifying Motor Neurons for a Muscle Motor Pool in Aplysia californica

    Published on: March 25, 2013

    12.1K
    Simultaneous Intracellular Recording of a Lumbar Motoneuron and the Force Produced by its Motor Unit in the Adult Mouse In vivo
    13:07

    Simultaneous Intracellular Recording of a Lumbar Motoneuron and the Force Produced by its Motor Unit in the Adult Mouse In vivo

    Published on: December 5, 2012

    15.2K

    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Signal Processing

    Background:

    • High-density surface electromyograms (EMG) require advanced decomposition techniques for accurate analysis.
    • Existing methods like batch Convolution Kernel Compensation (CKC) are effective but not suitable for real-time applications.
    • Real-time decomposition is crucial for immediate feedback and dynamic physiological monitoring.

    Purpose of the Study:

    • To develop and validate a real-time online decomposition method for high-density surface EMG signals.
    • To adapt the Convolution Kernel Compensation (CKC) technique for iterative, real-time processing.
    • To compare the performance of the real-time CKC method against its batch version and an LMMSE estimator.

    Main Methods:

    • The real-time CKC method utilizes an initial batch processing of approximately 3 seconds of EMG data for initialization.
    • Subsequent processing involves iterative updating of motor unit (MU) discharge estimators as new EMG data blocks become available.
    • Performance was evaluated by comparing identified MU discharges with synthetic and experimental EMG data against batch CKC and LMMSE methods.

    Main Results:

    • The real-time CKC method demonstrated high agreement with batch CKC and LMMSE estimators in identifying MU discharges.
    • For synthetic EMG with 20 dB SNR, an average sensitivity of 98% was achieved in identifying MU discharges.
    • Real-time CKC achieved 90% agreement with batch CKC on experimental EMG data, with a processing time of 0.6 seconds per second of EMG signal.

    Conclusions:

    • The developed real-time CKC method provides an efficient and accurate approach for online decomposition of high-density surface EMG.
    • The method is computationally efficient, requiring minimal processing time on standard personal computers.
    • This real-time capability opens new avenues for immediate EMG analysis in clinical and research settings.