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

Long-term Intracortical Neural activity and Kinematics (LINK): An intracortical neural dataset for chronic brain-machine interfaces, neuroscience, and machine learning.

Advances in neural information processing systems·2026
Same author

Use of RPNIs and Implanted Electrodes for Prosthetic Wrist and Multi-Grip Hand Control during Functional Tasks: A Case Study.

IEEE transactions on bio-medical engineering·2026
Same author

A Roadmap to Navigate the Future of Neural Engineering.

Journal of neural engineering·2026
Same author

Mechanisms that create a sequence of feeding-related behaviors in the mollusk <i>Aplysia</i>.

Journal of neurophysiology·2026
Same author

Regenerative peripheral nerve interfaces (RPNIs) and implanted electrodes improve online control of prostheses for hand and wrist<sup></sup>.

Journal of neural engineering·2026
Same author

A Sub-mm<sup>3</sup> Wireless Neural Stimulator IC for Visual Cortical Prosthesis With Optical Power Harvesting and 7.5-kb/s Data Telemetry.

IEEE journal of solid-state circuits·2025

Related Experiment Video

Updated: Oct 29, 2025

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

9.7K

Brain-Machine Interfaces: Lessons for Prosthetic Hand Control.

Alex K Vaskov1, Cynthia A Chestek2

  • 1Robotics Institute, University of Michigan, 2505 Hayward St, Ann Arbor, MI 48109, USA.

Hand Clinics
|July 13, 2021
PubMed
Summary
This summary is machine-generated.

Brain-machine interfaces and myoelectric prostheses restore limb function by decoding neural signals. This review compares these technologies, highlighting shared challenges and potential common solutions for improved control.

Keywords:
Brain-machine interfacesCalibration methodsFine motor controlPattern recognitionRegression algorithms

More Related Videos

The Bionic Clicker Mark I & II
08:23

The Bionic Clicker Mark I & II

Published on: August 14, 2017

16.6K
Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
05:21

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

8.1K

Related Experiment Videos

Last Updated: Oct 29, 2025

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

9.7K
The Bionic Clicker Mark I & II
08:23

The Bionic Clicker Mark I & II

Published on: August 14, 2017

16.6K
Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
05:21

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

8.1K

Area of Science:

  • Neuroscience and Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Brain-machine interfaces (BMI) and myoelectric prostheses aim to restore upper limb function.
  • These technologies are crucial for individuals with spinal cord injury and motor degenerative diseases.
  • Both systems extract neural information for high-fidelity prosthetic control.

Purpose of the Study:

  • To review the current state-of-the-art in BMI and myoelectric prostheses.
  • To identify similarities and differences between these two advanced technologies.
  • To guide the development of common solutions for shared challenges in neural control systems.

Main Methods:

  • Comparative review of existing literature on brain-machine interfaces.
  • Analysis of current research in implantable sensors for myoelectric prostheses.
  • Identification of transferable control algorithms and common technical issues.

Main Results:

  • Significant overlap exists in control algorithms between BMI and myoelectric prostheses.
  • Both technologies face similar challenges in signal processing and long-term stability.
  • Key differences in neural signal extraction and application contexts were identified.

Conclusions:

  • The convergence of BMI and myoelectric prosthesis research offers opportunities for integrated solutions.
  • Addressing common challenges can accelerate the development of advanced neuroprosthetics.
  • Further research into shared technical hurdles will enhance functional restoration for users.