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

2.1K
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...
2.1K
Indirect Motor Pathways01:22

Indirect Motor Pathways

1.5K
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...
1.5K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

117
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
117
Motor Unit Stimulation01:20

Motor Unit Stimulation

1.6K
When the neuron of a motor unit fires an action potential, it triggers a series of events, leading to a twitch contraction in the muscle fibers. The process of excitation-contraction coupling is crucial in relaying the action potential to the muscle fibers.
The latent period of contraction marks the onset of excitation-contraction coupling, when the action potential propagates across the sarcolemma, preparing the muscle fibers for contraction. As the fibers enter the contraction phase, the...
1.6K
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

2.8K
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.
2.8K
Motor and Sensory Areas of the Cortex01:14

Motor and Sensory Areas of the Cortex

4.0K
The cerebral cortex, the brain's outermost layer, is pivotal in processing complex cognitive tasks, emotions, and various sensory inputs and executing voluntary motor activities. This intricate structure is divided into three primary functional areas: the motor areas, sensory areas, and association areas.
Motor Areas
The motor areas located in the frontal lobe are central to controlling voluntary movements. This region is further subdivided into the primary motor cortex and the premotor cortex....
4.0K

You might also read

Related Articles

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

Sort by
Same author

EDSF-Net : An enhanced dynamic spatiotemporal-frequency attention network for robust EEG decoding in motor imagery.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Breaking the Depth Barrier in Motor Imagery Classification via a Residual Depthwise-Separable Network.

IEEE transactions on cybernetics·2026
Same author

Enhancing Target Recognition Performance in SSVEP-Based Brain-Computer Interfaces via Deep Neural Networks With Pyramid Squeeze Attention.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

BR-SFDA: A Source-Target Bidirectional Refined SFDA for Privacy Preserving EEG-based BCIs.

IEEE journal of biomedical and health informatics·2026
Same author

Enhancing the Capability and Accuracy of Motor Imagery Classification: A Deep Neural Network-Powered Multifaceted Strategy Model.

IEEE transactions on cybernetics·2026
Same author

TBMSCCN: Two-Branch Multi-Scale Convolutional Correlation Network for Steady-State Visual Evoked Potential Classification.

IEEE transactions on bio-medical engineering·2026

Related Experiment Video

Updated: Jul 15, 2025

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
10:14

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

1.0K

Novel channel selection model based on graph convolutional network for motor imagery.

Wei Liang1, Jing Jin1,2, Ian Daly3

  • 1Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China.

Cognitive Neurodynamics
|October 3, 2023
PubMed
Summary

This study introduces a novel graph convolutional neural network (GCN) method for selecting electroencephalography (EEG) channels in brain-computer interfaces (BCI). The GCN-based channel selection (GCN-CS) effectively identifies relevant EEG channels, improving BCI performance.

Keywords:
Brain-computer interface(BCI)Channel selectionGraph convolutional neural network (GCN)Motor imagery(MI)

More Related Videos

Non-Invasive Electrical Brain Stimulation Montages for Modulation of Human Motor Function
07:47

Non-Invasive Electrical Brain Stimulation Montages for Modulation of Human Motor Function

Published on: February 4, 2016

13.1K
Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
09:42

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

1.3K

Related Experiment Videos

Last Updated: Jul 15, 2025

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
10:14

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

1.0K
Non-Invasive Electrical Brain Stimulation Montages for Modulation of Human Motor Function
07:47

Non-Invasive Electrical Brain Stimulation Montages for Modulation of Human Motor Function

Published on: February 4, 2016

13.1K
Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
09:42

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

1.3K

Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Multi-channel electroencephalography (EEG) is crucial for motor imagery (MI) based brain-computer interfaces (BCI).
  • Redundant EEG channels can hinder BCI classification performance.
  • Effective channel selection is vital for optimizing BCI systems.

Purpose of the Study:

  • To develop a novel method for identifying and selecting relevant EEG channels for BCI applications.
  • To improve BCI classification accuracy by reducing channel redundancy.
  • To leverage graph convolutional neural networks (GCNs) for EEG channel selection.

Main Methods:

  • Proposed a graph convolutional neural network (GCN) model for channel selection, termed GCN-based channel selection (GCN-CS).
  • Treated EEG channels as nodes in a graph and channel selection as a node classification problem.
  • Evaluated the GCN-CS method on three distinct motor imagery (MI) datasets.

Main Results:

  • The GCN-CS method demonstrated significant performance improvements across all three MI datasets.
  • Classification accuracies achieved were 79.76% (Dataset 1), 89.14% (Dataset 2), and 87.96% (Dataset 3).
  • The proposed method effectively reduced the number of channels while enhancing BCI performance compared to competing methods.

Conclusions:

  • GCN-CS is an effective approach for selecting relevant EEG channels in BCI.
  • The method offers a significant improvement in classification accuracy for motor imagery tasks.
  • This GCN-based strategy provides a promising direction for optimizing brain-computer interface design.