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: Jun 22, 2026

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of

Steven M Chase1, Andrew B Schwartz, Robert E Kass

  • 1Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15203, USA. schase@andrew.cmu.edu

Neural Networks : the Official Journal of the International Neural Network Society
|June 9, 2009
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

In vivo microelectrode arrays for neuroscience.

Nature reviews. Methods primers·2026
Same author

Relative timing and coupling of neural population bursts in large-scale recordings from multiple neuron populations.

Frontiers in computational neuroscience·2026
Same author

Cross-population amplitude coupling in high-dimensional oscillatory neural time series.

Frontiers in computational neuroscience·2026
Same author

A Population Coupling Model Identifies Reduced Propagation from V1 to Higher Visual Areas During Locomotion.

bioRxiv : the preprint server for biology·2026
Same author

A posture subspace in the primary motor cortex.

Neuron·2025
Same author

Tuning of task-relevant stiffness in multiple directions.

Scientific reports·2025
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Aggregating global-scale pixel-wise forgery cues within a graph.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Motor neural prosthetics use brain activity to control devices. This study models closed-loop performance, showing real-world gains are less extreme than predicted and users can adapt to decoder biases.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Robotics

Background:

  • Motor neural prosthetics decode neural activity for device control.
  • Population Vector Algorithm (PVA) and Optimal Linear Estimator (OLE) are key decoding algorithms.
  • Open-loop performance differs from closed-loop, real-time control.

Purpose of the Study:

  • To develop a framework for modeling closed-loop performance of neural decoding algorithms.
  • To compare the closed-loop performance of PVA and OLE.
  • To investigate user adaptation to decoder characteristics.

Main Methods:

  • Developed a modeling framework for closed-loop neural decoding.
  • Conducted simulations and experiments with human participants.
  • Analyzed decoder bias and estimation error effects.

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Related Experiment Videos

Last Updated: Jun 22, 2026

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Main Results:

  • Closed-loop performance gains for some decoders are smaller than open-loop predictions.
  • Subjects can compensate for specific decoder biases.
  • Estimation error can significantly degrade optimal decoder performance.

Conclusions:

  • Closed-loop dynamics and user adaptation are crucial for predicting neural prosthetic performance.
  • Simple models may overestimate algorithm benefits in real-world applications.
  • Careful consideration of estimation error is necessary for effective decoder design.