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

Cerebrum: Anatomical Overview II01:11

Cerebrum: Anatomical Overview II

Each cerebral hemisphere can be divided into three main regions. The outermost region, the cerebral cortex, is a thin layer (2 to 4 millimeters thick) made up of gray matter, consisting of neuron cell bodies, dendrites, glial cells, and blood vessels. The middle region, or white matter, is primarily composed of myelinated nerve fibers organized into three types of large tracts: association fibers, commissures, and projection fibers. Association fibers connect different areas within the same...

You might also read

Related Articles

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

Sort by
Same author

EEG-based emotion recognition using phase-space reconstruction with Poincaré sections: a study on the AMIGOS dataset.

Frontiers in human neuroscience·2026
Same author

Cognitive, Neurophysiological, and Behavioral Adaptations in Golf Putting Motor Learning: A Holistic Approach.

Psychological research·2025
Same author

Enhancing Arousal Level Detection in EEG Signals through Genetic Algorithm-based Feature Selection and Fast Bit Hopping.

Journal of medical signals and sensors·2024
Same author

Directional information flow analysis in memory retrieval: a comparison between exaggerated and normal pictures.

Medical & biological engineering & computing·2024
Same author

Quantitative Comparison of Brain Waves of Dyslexic Students With Perceptual and Linguistic Types With Normal Students in Reading.

Basic and clinical neuroscience·2024
Same author

A Novel Method Based on ERP and Brain Graph for the Simultaneous Assessment of Various Types of Attention.

Computational intelligence and neuroscience·2022

Related Experiment Video

Updated: Jun 30, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.2K

Direct lingam and visibility graphs for analyzing brain connectivity in BCI.

Hoda Majdi1, Mahdi Azarnoosh2, Majid Ghoshuni1

  • 1Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.

Medical & Biological Engineering & Computing
|March 8, 2024
PubMed
Summary

This study explored brain network connectivity for brain-computer interfaces (BCIs). Limited Penetrable Horizontal Visibility Graph (LPHVG) showed higher accuracy in distinguishing motor imagery tasks compared to Direct Lingam, highlighting graph theory

Keywords:
Brain computer interfaceConvolutional neural networkDirect LiNGAMLimited penetrable horizontal visibility graphMotor imageryTransfer entropy

More Related Videos

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.1K
A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.2K

Related Experiment Videos

Last Updated: Jun 30, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.2K
Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.1K
A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.2K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Brain-computer interfaces (BCIs) enable direct communication between brain activity and external devices.
  • Understanding directed connectivity within the brain is crucial for advancing motor imagery (MI)-based BCIs.
  • Existing methods for analyzing brain networks require robust algorithms for complex signal processing.

Purpose of the Study:

  • To investigate directed brain connectivity during motor imagery (MI) using two distinct network analysis methods.
  • To compare the efficacy of Limited Penetrable Horizontal Visibility Graph (LPHVG) and Direct Lingam for MI classification.
  • To evaluate the potential of graph theory in enhancing BCI performance.

Main Methods:

  • Motor imagery electroencephalogram (MI-EEG) signals were mapped into networks using Limited Penetrable Horizontal Visibility Graph (LPHVG).
  • Directed connectivity was assessed using LPHVG combined with Transfer Entropy (TE) and the Direct Lingam (Bayesian network) model.
  • Support Vector Machine (SVM) and Convolutional Neural Network (CNN) classifiers were employed for MI classification.

Main Results:

  • The LPHVG method achieved a classification accuracy of 92.7% for distinguishing 4 classes of MI.
  • Direct Lingam achieved a classification accuracy of 90.6% for the same task.
  • Analysis revealed distinct network structures across different MI classes, with LPHVG demonstrating superior discriminative capability.

Conclusions:

  • Graph theory, particularly the LPHVG approach, shows significant potential for improving the efficiency and accuracy of motor imagery-based BCIs.
  • The study demonstrates that LPHVG is a more effective method than Direct Lingam for analyzing directed brain connectivity in MI tasks.
  • Network analysis of brain signals offers a promising avenue for developing more sophisticated and reliable brain-computer interfaces.