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

Neural decoding from surface high-density EMG signals: influence of anatomy and synchronization on the number of identified motor units.

Journal of neural engineering·2022
Same author

Mathematical relationships between spinal motoneuron properties.

eLife·2022
Same author

Standard intensities of transcranial alternating current stimulation over the motor cortex do not entrain corticospinal inputs to motor neurons.

The Journal of physiology·2022
Same author

Correlation networks of spinal motor neurons that innervate lower limb muscles during a multi-joint isometric task.

The Journal of physiology·2022
Same author

Reducing the Calibration Time in Somatosensory BCI by Using Tactile ERD.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2022
Same author

The control and training of single motor units in isometric tasks are constrained by a common input signal.

eLife·2022

Related Experiment Video

Updated: Mar 27, 2026

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

1.4K

Individual finger classification from surface EMG: Influence of electrode set.

Nicolo Celadon, Strahinja Dosen, Marco Paleari

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary

    This study optimized surface electromyography (sEMG) electrode configurations for decoding finger movements. Two linear arrays of eight electrodes achieved high accuracy, offering an efficient solution for classifying isometric flexion and extension.

    More Related Videos

    Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
    09:16

    Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

    Published on: April 5, 2019

    11.7K
    Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction
    09:14

    Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction

    Published on: September 28, 2019

    12.3K

    Related Experiment Videos

    Last Updated: Mar 27, 2026

    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

    1.4K
    Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
    09:16

    Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

    Published on: April 5, 2019

    11.7K
    Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction
    09:14

    Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction

    Published on: September 28, 2019

    12.3K

    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Rehabilitation Technology

    Background:

    • Decoding individual finger movements is crucial for advanced prosthetics and human-computer interfaces.
    • Surface electromyography (sEMG) offers a non-invasive method for muscle activity detection.
    • Optimizing sEMG electrode placement is essential for efficient and accurate signal acquisition.

    Purpose of the Study:

    • To minimize the number of sEMG channels required for accurate decoding of individual finger isometric contractions.
    • To determine optimal electrode locations and recording configurations.
    • To evaluate different electrode configurations for classifying isometric flexion and extension movements.

    Main Methods:

    • Nine healthy subjects performed cyclical isometric contractions of individual fingers.
    • Surface electromyography (sEMG) signals were recorded from forearm muscles using a 192-channel matrix.
    • Classification accuracy was assessed using linear discriminant analysis (LDA) with four electrode configurations: single array, dual arrays, barycenter-based, and full channel set.

    Main Results:

    • Classification accuracy varied significantly across different electrode configurations (F=14.67, p<0.001).
    • The barycenter approach and dual linear arrays of 8 electrodes yielded the highest classification accuracies, exceeding 82% success rate.
    • These optimized configurations performed comparably to using all recorded channels.

    Conclusions:

    • Dual linear arrays of 8 electrodes represent an optimal configuration for classifying individual finger isometric flexion and extension.
    • This configuration balances high classification accuracy with reduced computational time and simplified electrode positioning.
    • The findings contribute to developing more efficient and practical sEMG-based control systems.