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

Dynamics of cortical excitability in stimulus-response mapping for overt and covert movements is locked to visual stimulus: an LRP-TMS study.

Experimental brain research·2026
Same author

Multisession fNIRS-EEG data of Post-Stroke Motor Recovery. Recordings During Intact and Paretic Hand Movements.

Scientific data·2026
Same author

Using a TMS Navigation System for High-Precision Digitization of Sensor Locations to Improve Source Localization.

Journal of visualized experiments : JoVE·2026
Same author

Sensorimotor event-related desynchronization and hemodynamic responses during motor and tactile imagery.

Brain structure & function·2025
Same author

Editorial: Exoskeleton gait training.

Frontiers in neuroscience·2025
Same author

Remote automated delivery of mechanical stimuli coupled to brain recordings in behaving mice.

eLife·2025
Same journal

Predicting vasovagal syncope during head-up tilt test: three machine learning approaches.

Frontiers in neuroinformatics·2026
Same journal

Decoding basal ganglia motor circuit dysfunction from handwriting: a physics-informed neural signal interpretation framework for Parkinson's disease screening.

Frontiers in neuroinformatics·2026
Same journal

FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.

Frontiers in neuroinformatics·2026
Same journal

A 3D-printed phantom to validate subject orientation in 3D imaging and recordings.

Frontiers in neuroinformatics·2026
Same journal

IntegriLAB: a blockchain-enabled electronic lab notebook for reproducible neuroimaging research.

Frontiers in neuroinformatics·2026
Same journal

Long-range correlations in alpha-band of electroencephalogram: a nonlinear embedding and detrended fluctuation analysis.

Frontiers in neuroinformatics·2026
See all related articles

Related Experiment Video

Updated: Jan 1, 2026

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

26.6K

Decoding Movement From Electrocorticographic Activity: A Review.

Ksenia Volkova1, Mikhail A Lebedev1, Alexander Kaplan1,2,3

  • 1Center for Bioelectric Interfaces, Higher School of Economics, National Research University, Moscow, Russia.

Frontiers in Neuroinformatics
|December 19, 2019
PubMed
Summary
This summary is machine-generated.

Electrocorticography (ECoG) offers promising neuroprosthetic solutions for neurological disabilities. This brain-computer interface (BCI) technique decodes motor commands, advancing assistive device development.

Keywords:
BCIECoGbrain-computer interfaceelectrocorticographymovement decoding

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
Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex
09:00

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

Published on: April 15, 2015

12.7K

Related Experiment Videos

Last Updated: Jan 1, 2026

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

26.6K
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
Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex
09:00

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

Published on: April 15, 2015

12.7K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Electrocorticography (ECoG) is an invasive electrophysiological recording technique used for brain activity monitoring.
  • It offers superior spatial and temporal resolution compared to non-invasive methods with manageable risks.
  • ECoG is established in clinical practice for preoperative cortical mapping in epilepsy patients.

Purpose of the Study:

  • To review the evolution and recent advancements in Electrocorticography (ECoG) research for neuroprosthetic applications.
  • To discuss the development of brain-computer interfaces (BCIs) that utilize ECoG signals.
  • To explore future research directions and potential applications in assistive technology.

Main Methods:

  • Review of scientific literature on ECoG and brain-computer interfaces (BCIs) over the past two decades.
  • Analysis of decoding algorithms used to extract behaviorally relevant information from ECoG activity.
  • Examination of research trends in developing assistive devices driven by ECoG-based BCIs.

Main Results:

  • ECoG research has significantly grown, focusing on decoding motor commands for BCIs.
  • Assistive devices utilizing ECoG are being developed to aid individuals with neurological disabilities.
  • The field shows a trend towards more complex decoding algorithms for enhanced BCI performance.

Conclusions:

  • ECoG is a viable technique for developing efficient neuroprosthetic solutions.
  • Continued research in decoding algorithms and BCI development holds significant potential for future assistive technologies.
  • Further exploration is needed to fully realize the potential of ECoG in clinical neuroprosthetics.