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

Effects of probiotics on psychological issues in people with fibromyalgia: a systematic review.

Annals of medicine and surgery (2012)·2026
Same author

Single-Substance SSRI Intoxication: A Clinical and Outcome Profile Presentation in a Poisoning Referal Center.

Emergency medicine international·2025
Same author

Burden attributable to Iodine deficiency in Iran from 1990 to 2019: findings from Global Burden of Disease study.

Journal of preventive medicine and hygiene·2025
Same author

Enhancing clinical risk assessment in pediatric blunt abdominal trauma: A novel scoring system using ultrasound and laboratory data.

BMC emergency medicine·2025
Same author

Detection of epileptic seizures through EEG signals using entropy features and ensemble learning.

Frontiers in human neuroscience·2023
Same author

Effects of <i>Satureja Bachtiarica</i> Essential Oil in Preventing Seizure in Pentylenetetrazol-Kindled Mice.

Basic and clinical neuroscience·2022

Related Experiment Video

Updated: May 29, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

11.6K

Generalized Coupled Matrix Tensor Factorization Method Based on Normalized Mutual Information for Simultaneous

Zahra Rabiei1, Hussain Montazery Kordy2

  • 1Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.

Neuroinformatics
|February 6, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a generalized coupled matrix tensor factorization (GCMTF) method for fusing electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. GCMTF effectively identifies shared brain activity components, outperforming existing methods in accuracy and identifying more brain regions.

Keywords:
Auditory oddballCoupled matrix tensor factorizationData fusionEEG-fMRINormalized mutual information

More Related Videos

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.2K
Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

4.1K

Related Experiment Videos

Last Updated: May 29, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

11.6K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.2K
Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

4.1K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Data Science

Background:

  • Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) offer complementary insights into brain activity.
  • Joint analysis of EEG and fMRI data is crucial for a comprehensive understanding of neural processes.
  • Existing methods often assume restrictive equality for shared components, limiting their applicability.

Purpose of the Study:

  • To develop a novel method for joint EEG-fMRI analysis that overcomes limitations of existing techniques.
  • To introduce a generalized coupled matrix tensor factorization (GCMTF) approach utilizing normalized mutual information (NMI).
  • To enhance the identification of shared and unshared brain activity components between EEG and fMRI.

Main Methods:

  • Implementation of the generalized coupled matrix tensor factorization (GCMTF) method.
  • Utilizing normalized mutual information (NMI) to define component similarity, accommodating nonlinear relationships.
  • Application to simulated data with nonlinear component relationships and real EEG-fMRI data from an auditory oddball paradigm.

Main Results:

  • The GCMTF method demonstrated a 23.46% increase in average match score compared to the advanced coupled matrix tensor factorization (ACMTF) model on simulated data.
  • GCMTF effectively identified shared components with both linear and nonlinear relationships, unlike ACMTF.
  • Analysis of real data revealed three shared components in alpha and theta bands during an auditory oddball task, identifying more active brain areas than ACMTF.

Conclusions:

  • The proposed GCMTF method offers a robust and generalized approach for joint EEG-fMRI data analysis.
  • GCMTF accurately identifies shared neural components, even with nonlinear relationships and varying noise levels.
  • This method enhances the discovery of brain activity patterns associated with cognitive tasks, improving upon existing fusion techniques.