Jove
Visualize
Contact Us

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

Characterizing superficial cerebral cortical venous anatomy for endovascular device implantation: a cross-sectional imaging study.

Journal of neurointerventional surgery·2025
Same author

Selective recording of physiologically evoked neural activity in a mixed autonomic nerve using a minimally invasive array.

APL bioengineering·2023
Same author

Fractal Microelectrodes for More Energy-Efficient Cervical Vagus Nerve Stimulation.

Advanced healthcare materials·2023
Same author

Cortical Auditory Evoked Potentials Recorded Directly Through the Cochlear Implant in Cochlear Implant Recipients: a Feasibility Study.

Ear and hearing·2022
Same author

Rapid Analysis of Visual Receptive Fields by Iterative Tomography.

eNeuro·2021
Same author

Computational modelling of nerve stimulation and recording with peripheral visceral neural interfaces.

Journal of neural engineering·2021
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
See all related articles
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: Jul 8, 2025

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 'Total Unique Variation Analysis' for Brain-Machine Interfaces.

Calvin D Eiber, Venkata S Aditya Tarigoppula, Gil S Rind

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary
    This summary is machine-generated.

    A new method called Total Unique Variance Analysis (TUVA) helps design better brain-machine interfaces (BMIs) by identifying the most informative neural recording channels and reducing redundancy.

    More Related Videos

    A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
    06:34

    A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

    Published on: July 7, 2023

    2.4K
    Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
    06:57

    Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

    Published on: August 9, 2016

    11.4K

    Related Experiment Videos

    Last Updated: Jul 8, 2025

    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 Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
    06:34

    A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

    Published on: July 7, 2023

    2.4K
    Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
    06:57

    Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

    Published on: August 9, 2016

    11.4K

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Statistics

    Background:

    • Designing brain-machine interfaces (BMIs) requires maximizing neural information capture with minimal channels.
    • Minimizing channel redundancy is crucial for efficient BMI design.

    Purpose of the Study:

    • Introduce Total Unique Variance Analysis (TUVA) to quantify unique signal variance per channel.
    • Provide a method for ordering channels by informativeness to optimize BMI design.
    • Aid in the development of maximally efficacious BMIs.

    Main Methods:

    • Developed TUVA, a statistical method for analyzing multidimensional data.
    • Applied TUVA to simulated electrocorticography (ECoG) lead-field maps.
    • Compared TUVA values from simulations with real ECoG recordings during epilepsy surgery planning.

    Main Results:

    • TUVA effectively quantifies the unique signal contribution of each channel.
    • The method ranks channels by their information content, guiding efficient electrode placement.
    • Demonstrated TUVA's applicability in both simulated and real-world neural recording scenarios.

    Conclusions:

    • TUVA offers a novel approach for comparing neural interface designs.
    • This method quantifies recording efficiency by minimizing channel crosstalk.
    • TUVA can improve the risk-benefit profile of invasive neural recording technologies.