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

Deep learning for freezing of gait assessment using inertial measurement units: a multicentre validation study.

NPJ Parkinson's disease·2026
Same author

AI and Internet of Things for Chronic Obstructive Pulmonary Disease Remote Monitoring: Systematic Review of Exacerbation Prediction and Key Physiological Variables.

JMIR medical informatics·2026
Same author

Learning Where to Look: Differentiable Slice Selection and Efficient Channel Attention for FCD-II MRI Classification.

IEEE journal of biomedical and health informatics·2026
Same author

Prevalence of Early Rheumatic Heart Disease Among Asymptomatic Students in Underserved Communities in Ethiopia: Cross-Sectional Observational Study.

JMIR public health and surveillance·2026
Same author

Respiration Rate Estimation Using ECG and PPG Data: Open Source Implementations Focused on Wearables.

IEEE journal of biomedical and health informatics·2026
Same author

Robust Multimodal Learning Framework for Intake Gesture Detection Using Contactless Radar and Wearable IMU Sensors.

IEEE journal of biomedical and health informatics·2026

Related Experiment Video

Updated: Jun 28, 2026

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Review on solving the inverse problem in EEG source analysis.

Roberta Grech1, Tracey Cassar, Joseph Muscat

  • 1iBERG, University of Malta, Malta. roberta.grech@um.edu.mt

Journal of Neuroengineering and Rehabilitation
|November 8, 2008
PubMed
Summary
This summary is machine-generated.

This review explores electroencephalography (EEG) source localization inverse problem techniques. Regularized standardized low-resolution brain electromagnetic tomography (sLORETA) excels in single-source localization, outperforming other methods in accuracy and minimizing ghost sources.

More Related Videos

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

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Related Experiment Videos

Last Updated: Jun 28, 2026

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

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

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • The inverse problem in electroencephalography (EEG) aims to identify neural sources from scalp potentials.
  • Researchers need a comprehensive overview of current state-of-the-art techniques for EEG source localization.

Purpose of the Study:

  • To provide a review of inverse problem techniques for EEG source localization.
  • To offer insights into methods for approximating brain sources from scalp recordings.
  • To compare the performance of various inverse solutions and contribute to existing literature.

Main Methods:

  • Mathematical description of the EEG inverse problem.
  • Categorization into non-parametric (e.g., LORETA, sLORETA, VARETA, LAURA) and parametric (e.g., beamforming, BESA, MUSIC) methods.
  • Monte Carlo analysis comparing Weighted Minimum Norm (WMN), LORETA, sLORETA, and Shrinking LORETA FOCUSS (SLF) algorithms.

Main Results:

  • LORETA solutions generally provide satisfactory results for EEG source localization.
  • Higher resolution algorithms like MUSIC or FINES are preferred for localizing clusters of dipoles.
  • Regularized sLORETA demonstrated superior performance in single-source localization, minimizing localization error and ghost sources across various noise levels and source depths.

Conclusions:

  • Non-parametric methods like LORETA and sLORETA are effective for EEG source localization.
  • Parametric methods offer higher resolution for complex source configurations.
  • Regularized sLORETA is recommended for single-source localization tasks due to its accuracy and ghost source suppression.