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

Feedback control systems01:26

Feedback control systems

433
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
433

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Selection of functional electrical stimulation patterns affects hip and knee mechanical loads during semi-recumbent cycling.

Scientific reports·2026
Same author

A neural network for predicting knee contact forces from clinic-friendly data.

Journal of biomechanics·2026
Same author

A TPMS-integrated paediatric proximal femoral osteotomy implant demonstrates structural feasibility and improved load sharing: An in silico proof-of-concept study.

Computers in biology and medicine·2026
Same author

An Evidence-Based and Mechanistic Approach to Reducing the Risk of Anterior Cruciate Ligament Injury: An Exercise and Sport Science Australia Position Statement.

Sports medicine (Auckland, N.Z.)·2026
Same author

Artificial intelligence predictions of knee kinematics, kinetics, and internal biomechanics during walking in people with knee osteoarthritis: A systematic review and meta-analysis.

Clinical biomechanics (Bristol, Avon)·2026
Same author

Lower limb biomechanics in femoroacetabular impingement syndrome, asymptomatic cam morphology, and controls during bilateral and single-leg squatting.

Gait & posture·2026
Same journal

Exploring Synergy Between Tactile Perception and Arm Usage.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same journal

Multi-Modal Muscle Activation Modeling Using Koopman Operator Linearization for an Ankle Exoskeleton.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same journal

Unsupervised Robot-Assisted Therapy at Home After Stroke: a Pilot Feasibility Study.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same journal

Optimizing Senior Living with Robots: A User Study on Social and Architectural Integration.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same journal

Effects of Exoskeletons on Error Between Marker and Markerless Motion Capture in Children With Crouch Gait: A Pilot Study.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same journal

Recovr Glove: Accessible Hand Exoskeleton for Stroke Rehabilitation and Everyday Aid.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
See all related articles

Related Experiment Video

Updated: Sep 16, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.7K

Optimising Continuous Control of Real-Time Brain-Computer Interfaces Through Trial Length and Feedback Update

Malik Muhammad Naeem Mannan, David G Lloyd, Claudio Pizzolato

    IEEE ... International Conference on Rehabilitation Robotics : [Proceedings]
    |July 11, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Optimizing brain-computer interface (BCI) systems for neurorehabilitation requires balancing accuracy and responsiveness. This study found that longer trial lengths and feedback update intervals improve classification accuracy in motor imagery (MI) BCIs.

    More Related Videos

    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

    13.8K
    A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
    07:05

    A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

    Published on: August 24, 2017

    11.1K

    Related Experiment Videos

    Last Updated: Sep 16, 2025

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
    11:54

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

    Published on: May 8, 2021

    4.7K
    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

    13.8K
    A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
    07:05

    A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

    Published on: August 24, 2017

    11.1K

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Rehabilitation Technology

    Background:

    • Brain-computer interfaces (BCIs) are crucial for neurorehabilitation, translating motor imagery (MI) signals into commands to aid motor recovery.
    • Balancing classification accuracy and real-time responsiveness is a key challenge for effective BCI control and user embodiment.

    Purpose of the Study:

    • To investigate how trial length and feedback update interval (FUI) affect classification accuracy in an MI-based BCI system.
    • To identify optimal parameter settings for enhancing BCI performance in neurorehabilitation.

    Main Methods:

    • Utilized EEG data from five subjects across 50 sessions.
    • Evaluated classification performance with varying trial lengths (1-5 seconds) and FUIs (0.2-1 second).

    Main Results:

    • Both trial length and FUI significantly impacted classification accuracy.
    • Longer trial lengths (4-5 seconds) and FUIs (0.4-1 second) generally yielded higher accuracy.
    • A saturation effect was observed, indicating no significant accuracy gains beyond certain parameter values.

    Conclusions:

    • Findings highlight the importance of balancing signal stability and responsiveness for optimal BCI performance.
    • Parameter settings identified can improve BCI usability in neurorehabilitation.
    • Future research may focus on adaptive parameter adjustment for enhanced responsiveness.