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 novel chimeric peptide binds MC3T3‑E1 cells to titanium and enhances their proliferation and differentiation.

Molecular medicine reports·2013
Same author

Fast trabecular bone strength predictions of HR-pQCT and individual trabeculae segmentation-based plate and rod finite element model discriminate postmenopausal vertebral fractures.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research·2013
Same author

Biological activities and corresponding SARs of andrographolide and its derivatives.

Mini reviews in medicinal chemistry·2013
Same author

The prognostic value of MGMT promoter methylation in Glioblastoma multiforme: a meta-analysis.

Familial cancer·2013
Same author

Understanding the structure and mechanism of formation of a new magnetic microbubble formulation.

Theranostics·2013
Same author

Analysis of IL-17 gene polymorphisms in Chinese patients with dilated cardiomyopathy.

Human immunology·2013

Related Experiment Video

Updated: Oct 15, 2025

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

1.6K

Motor Intention Decoding from the Upper Limb by Graph Convolutional Network Based on Functional Connectivity.

Naishi Feng1, Fo Hu1, Hong Wang1

  • 1Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, P. R. China.

International Journal of Neural Systems
|October 25, 2021
PubMed
Summary

This study introduces a novel graph convolutional network (GCN) for decoding motor intention from electroencephalography (EEG) signals. The GCN achieved 92.81% accuracy in distinguishing four fine motor movements, advancing brain-computer interface (BCI) technology.

Keywords:
EEGGCNfunctional connectivityintention decodingupper limb

More Related Videos

Functional MRI in Conjunction with a Novel MRI-compatible Hand-induced Robotic Device to Evaluate Rehabilitation of Individuals Recovering from Hand Grip Deficits
07:34

Functional MRI in Conjunction with a Novel MRI-compatible Hand-induced Robotic Device to Evaluate Rehabilitation of Individuals Recovering from Hand Grip Deficits

Published on: November 23, 2019

8.1K
Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS
05:25

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

1.4K

Related Experiment Videos

Last Updated: Oct 15, 2025

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

1.6K
Functional MRI in Conjunction with a Novel MRI-compatible Hand-induced Robotic Device to Evaluate Rehabilitation of Individuals Recovering from Hand Grip Deficits
07:34

Functional MRI in Conjunction with a Novel MRI-compatible Hand-induced Robotic Device to Evaluate Rehabilitation of Individuals Recovering from Hand Grip Deficits

Published on: November 23, 2019

8.1K
Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS
05:25

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

1.4K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Brain-computer interfaces (BCI) aim to decode neural signals for controlling external devices.
  • Decoding fine motor intentions from noninvasive neural signals is crucial for increasing BCI control capabilities.
  • Existing BCI methods often overlook the graph information inherent in electroencephalography (EEG) data.

Purpose of the Study:

  • To propose a novel graph convolutional network (GCN) approach for decoding motor intention using EEG.
  • To incorporate functional connectivity into the decoding process, addressing limitations of previous methods.
  • To decode motor intentions for four distinct fine movements: shoulder, elbow, wrist, and hand.

Main Methods:

  • Analysis of event-related desynchronization (ERD) to identify differences between motor tasks.
  • Construction of functional connectivity networks using metrics like Synchronization Likelihood (SL), Phase-Locking Value (PLV), H index (H), Mutual Information (MI), and Weighted Phase-Lag Index (WPLI).
  • Application of a GCN model leveraging functional topological structures and a Convolutional Neural Network (CNN) model based on time points for decoding.

Main Results:

  • The proposed GCN method achieved a high decoding accuracy of up to 92.81% for the four-class motor intention task.
  • Functional connectivity analysis revealed distinct electrode pair differences crucial for decoding.
  • The study demonstrated the effectiveness of integrating GCN with functional connectivity for enhanced BCI performance.

Conclusions:

  • The GCN approach based on functional connectivity significantly improves motor intention decoding accuracy in BCI.
  • This method effectively utilizes the graph structure of EEG data, overcoming previous limitations.
  • The findings suggest that combining GCN and functional connectivity is a promising direction for advancing BCI technology.