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

A diatrizoic acid derivative for <i>N</i>-acylation of amino acids.

Results in chemistry·2026
Same author

Human neural stem cell-derived extracellular vesicles improve cognitive function following glioma chemoradiation therapy.

Cancer letters·2026
Same author

Correction to: Early Recognition and Intervention for Poststroke Spasticity: A Scientific Statement From the American Heart Association.

Stroke·2026
Same author

Perception of brain-computer interface implantation surgery for motor, sensory, and autonomic restoration in spinal cord injury and stroke.

Frontiers in neuroscience·2026
Same author

Real-time brain-computer interface control of walking exoskeleton with bilateral sensory feedback.

Brain stimulation·2026
Same author

Early Recognition and Intervention for Poststroke Spasticity: A Scientific Statement From the American Heart Association.

Stroke·2026

Related Experiment Video

Updated: May 7, 2026

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

1.5K

Sensitivity and specificity of upper extremity movements decoded from electrocorticogram.

An H Do, Po T Wang, Christine E King

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
    PubMed
    Summary

    Brain computer interfaces (BCI) using electrocorticogram (ECoG) show promise for prosthetic arm control. However, current ECoG technology may lack the resolution needed to decode complex, multi-joint arm movements for independent function.

    More Related Videos

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
    11:25

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

    Published on: July 26, 2013

    44.0K
    Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
    13:32

    Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

    Published on: June 26, 2012

    25.8K

    Related Experiment Videos

    Last Updated: May 7, 2026

    Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
    06:37

    Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

    Published on: July 14, 2023

    1.5K
    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
    11:25

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

    Published on: July 26, 2013

    44.0K
    Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
    13:32

    Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

    Published on: June 26, 2012

    25.8K

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Rehabilitation Technology

    Background:

    • Brain computer interfaces (BCI) offer potential for controlling prosthetic limbs.
    • Electrocorticogram (ECoG) signals are a promising neural signal for BCI applications.
    • Restoring independent function in users requires control over multiple degrees-of-freedom (DOF).

    Purpose of the Study:

    • To investigate the feasibility of decoding multiple degrees-of-freedom (DOF) upper extremity movements from ECoG signals.
    • To assess the resolution of current ECoG technology for complex motor control.
    • To evaluate ECoG signal decoding models for distinguishing between different arm movements.

    Main Methods:

    • Two subjects with ECoG grids for epilepsy surgery evaluation participated.
    • Data from 6 elementary upper extremity movements were recorded.
    • Decoding models were designed to classify ECoG signals as idling or movement.

    Main Results:

    • Decoding models demonstrated high sensitivity in detecting movement.
    • Specificity was low, with difficulty distinguishing between different movement types.
    • A trade-off between sensitivity and specificity was observed.

    Conclusions:

    • Conventional ECoG grids may not offer sufficient resolution for decoding many-DOF upper extremity movements.
    • Further advancements in ECoG electrode technology are needed for sophisticated prosthetic control.
    • The study highlights limitations in current ECoG-based BCI for complex motor tasks.