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 Videos

Naive coadaptive cortical control.

Gregory J Gage1, Kip A Ludwig, Kevin J Otto

  • 1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA. gagegreg@umich.edu

Journal of Neural Engineering
|June 2, 2005
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

Identification of low threshold off-target activation pathways during stimulation of carotid baroreceptor afferents in swine.

Journal of neural engineering·2026
Same author

Prevalence of sympathetic fibers within the rat cervical vagus, and functional consequence on physiological effects mediated by vagus nerve stimulation (VNS).

Journal of neural engineering·2026
Same author

Electrochemical impedance spectroscopy for characterizing neural electrodes.

Current opinion in electrochemistry·2026
Same author

Application of kilohertz-frequency block to mitigate off-target motor effects of vagus nerve stimulation in swine.

Nature communications·2025
Same author

Adaptive neuromodulation dialogues: navigating current challenges and emerging innovations in neuromodulation system development.

Journal of neural engineering·2025
Same author

Towards optimizing target engagement in non-invasive trigeminal nerve stimulation: anatomical characterization of the human trigeminal nerve.

Journal of neural engineering·2025

Naive subjects learned to control a neural prosthetic device using brain signals from the motor cortex. This demonstrates that individuals can generate effective neural control without prior training or device characterization.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Neural prosthetics enable device control via brain activity.
  • Previous research required motor training or device calibration.
  • Cortical control in naive users and controllers remained unexplored.

Purpose of the Study:

  • To investigate neural control of a prosthetic device by untrained subjects.
  • To assess if subjects can learn to control a device without prior motor training.
  • To determine if a naive controller can adapt to unknown neural encoding.

Main Methods:

  • Used silicon micro-electrodes in the motor cortex of Long-Evans rats.
  • Employed a Kalman filter for decoding neural activity into an auditory cursor.

Related Experiment Videos

  • Implemented a novel adaptive algorithm for real-time decoding filter training.

    • Main Results:

    • All six rats learned to control the auditory device significantly above chance within 7 sessions.
    • Behavioral performance and signal-to-noise ratio improved significantly over 24 sessions.
    • One rat achieved over 90% accuracy in a complex two-target control task.

    Conclusions:

    • Subjects can learn to generate effective neural control signals without prior experience.
    • The adaptive decoding strategy facilitated mutual learning between user and prosthetic.
    • This study advances the potential for intuitive brain-computer interfaces.