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 Experiment Video

Updated: Feb 28, 2026

Measurement of Spatial Stability in Precision Grip
09:36

Measurement of Spatial Stability in Precision Grip

Published on: June 4, 2020

3.6K

Pilot Study for Grip Force Prediction Using Neural Signals from Different Brain Regions.

Mohammad Bataineh1, David McNiel2, John Choi2

  • 1Department of Biomedical Engineering, Cullen College of Engineering, University of Houston, Houston, TX 77204-6022.

Proceedings of the ... Southern Biomedical Engineering Conference. Southern Biomedical Engineering Conference
|June 20, 2017
PubMed
Summary
This summary is machine-generated.

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

Utility of repeat stereotactic EEG in pediatric patients with tuberous sclerosis.

Epilepsia open·2026
Same author

APOL1-risk alleles modulate T-cell receptor signaling to promote allograft rejection.

The Journal of clinical investigation·2026
Same author

Cross-species transcriptomic integration reveals a MIRO1-mediated macrophage-T cell axis in glioma.

Life science alliance·2026
Same author

Next-generation immunotherapy biologics for glioblastoma.

Frontiers in immunology·2026
Same author

Obesity and Heart Failure With Preserved Ejection Fraction: A Clinical Nexus for Exercise Intolerance.

Obesity (Silver Spring, Md.)·2026
Same author

Precision Immunotherapeutics for Glioblastoma: Current Approaches and Emerging Strategies in 2026.

Cells·2026
Same journal

Rapid Prototyping of a Smart Device-based Wireless Reflectance Photoplethysmograph.

Proceedings of the ... Southern Biomedical Engineering Conference. Southern Biomedical Engineering Conference·2017
Same journal

Classifier Performance in Primary Somatosensory Cortex Towards Implementation of a Reinforcement Learning Based Brain Machine Interface.

Proceedings of the ... Southern Biomedical Engineering Conference. Southern Biomedical Engineering Conference·2017
Same journal

Local Histograms for Classifying H&E Stained Tissues.

Proceedings of the ... Southern Biomedical Engineering Conference. Southern Biomedical Engineering Conference·2014
See all related articles

This study predicts maximum grip force using neural signals from four brain regions in nonhuman primates. Findings aid in developing advanced brain machine interfaces (BMI) for prosthetic limb control.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Robotics

Background:

  • Brain machine interfaces (BMI) have advanced limb restoration and robotic control.
  • Current BMI technology requires further development for precise force control in artificial limbs.
  • Direct neural control is essential for accurate grip force in prosthetic devices.

Purpose of the Study:

  • To investigate neural signal processing from specific brain regions for predicting maximum grip force.
  • To compare the predictive accuracy of neural signals from different cortical areas.
  • To inform the development of robust BMI systems for controlling grip force.

Main Methods:

  • Recorded neural signals from the primary motor (M1), primary somatosensory (S1), dorsal premotor (PmD), and ventral premotor (PmV) cortices in nonhuman primates.

More Related Videos

Studying Food Reward and Motivation in Humans
12:09

Studying Food Reward and Motivation in Humans

Published on: March 19, 2014

24.2K
Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

558

Related Experiment Videos

Last Updated: Feb 28, 2026

Measurement of Spatial Stability in Precision Grip
09:36

Measurement of Spatial Stability in Precision Grip

Published on: June 4, 2020

3.6K
Studying Food Reward and Motivation in Humans
12:09

Studying Food Reward and Motivation in Humans

Published on: March 19, 2014

24.2K
Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

558
  • Developed regression models utilizing these neural signals to predict maximum grip force.
  • Compared the prediction accuracy across the investigated brain regions.

    • Main Results:

    • Neural signals from M1, S1, PmD, and PmV cortices were processed to predict grip force.
    • Comparative analysis of prediction accuracy from each brain region was performed.
    • Results highlight the potential of specific cortical areas for grip force control.

    Conclusions:

    • The study provides insights into the neural basis of grip force control for BMI applications.
    • Relative prediction accuracy across brain regions guides future research in robust BMI design.
    • Integrating signals from multiple brain regions may lead to improved prosthetic limb functionality.