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

Direct Motor Pathways01:11

Direct Motor Pathways

The direct motor pathways, also known as the pyramidal tracts, are a group of neural pathways that originate in the brain and descend through the spinal cord. They control the voluntary movement of the body. There are two major direct motor pathways: the corticospinal and the corticobulbar tracts.
The corticospinal tract is responsible for the voluntary movement of the limbs and trunk. It originates in the cerebral cortex of the brain and descends through the cerebrum's internal capsule and the...
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
Indirect Motor Pathways01:22

Indirect Motor Pathways

The indirect motor or extrapyramidal pathways originate in the brainstem, the lower portion of the brain that connects it to the spinal cord. They consist of several distinct tracts, each with specialized functions. The four main tracts of the indirect motor pathways are the vestibulospinal tract, the reticulospinal tract, the tectospinal tract, and the rubrospinal tract.
The vestibulospinal tract originates in the vestibular nuclei of the brainstem. The vestibular system detects changes in...

You might also read

Related Articles

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

Sort by
Same author

Association between motor cortex grey matter loss and inability to control an ECoG-based implanted Brain-Computer Interface in ALS.

medRxiv : the preprint server for health sciences·2026
Same author

Incremental prognostic value of the fibrinogen-to-albumin ratio for adverse perinatal outcomes in preeclampsia: a dual-center retrospective cohort study.

Frontiers in endocrinology·2026
Same author

Pretreatment with different doses of oliceridine attenuates etomidate-induced myoclonus during painless gastroscopy: a randomized controlled trial.

Frontiers in pharmacology·2026
Same author

Stable speech BCI performance during slow progression of ALS: A longitudinal ECoG study.

Research square·2026
Same author

Implanted brain-computer interface functionality during nighttime in late-stage amyotrophic lateral sclerosis.

Scientific reports·2026
Same author

Erratum: Early postoperative acute kidney injury prediction in patients with acute type A aortic dissection.

iScience·2026

Related Experiment Video

Updated: May 27, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

Connectivity analysis as a novel approach to motor decoding for prosthesis control.

Heather L Benz1, Huaijian Zhang, Anastasios Bezerianos

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA. benz@jhu.edu

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
|November 16, 2011
PubMed
Summary

Researchers explored using electrocorticography (ECoG) network connectivity for brain-machine interfaces (BMI). This novel approach significantly improved prosthesis control accuracy compared to traditional methods.

More Related Videos

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
05:21

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

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

Related Experiment Videos

Last Updated: May 27, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
05:21

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

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

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Brain-machine interfaces (BMIs) aim to restore function for amputees and paralyzed individuals.
  • Electrocorticography (ECoG) offers an alternative to other BMI approaches like EEG.
  • Current ECoG BMIs primarily use spectral analysis for signal extraction.

Purpose of the Study:

  • To compare traditional spectral features with network connectivity features for ECoG BMI.
  • To introduce and evaluate time-varying dynamic Bayesian networks (TV-DBN) for ECoG channel connectivity.
  • To assess the potential of connectivity features to enhance ECoG BMI accuracy.

Main Methods:

  • Utilized time-varying dynamic Bayesian networks (TV-DBN) to analyze ECoG channel connectivity in human subjects during motor tasks.
  • Extracted connectivity features from ECoG signals.
  • Implemented a hand kinematic decoder using both traditional spectral features and the novel TV-DBN connectivity features.

Main Results:

  • TV-DBN connectivity showed a decrease from baseline preceding movement, becoming negative and indicating altered phase relationships.
  • These connectivity changes were observed before spectral power changes in some subjects.
  • The TV-DBN connectivity decoder achieved an average correlation coefficient (r^2) of 0.40 (up to 0.66), significantly outperforming spectral feature decoders (average r^2 of 0.13).

Conclusions:

  • ECoG network connectivity, analyzed via TV-DBN, provides a valuable new feature set for BMIs.
  • Connectivity-based features demonstrate superior performance in improving ECoG BMI accuracy for prosthesis control.
  • This approach holds significant promise for advancing neuroprosthetic technology.