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 journal

Lower limb motion intention recognition using multi-source able-bodied gait signals.

Journal of neural engineering·2026
Same journal

Neural correlates of performance and mental workload dynamics during learning of upper-limb body-powered and myoelectric prostheses.

Journal of neural engineering·2026
Same journal

Mechanistic drivers of platinum dissolution in neural prosthetic electrode pulsing: decoupling electrochemistry, biological interfaces, and electrode degradation products.

Journal of neural engineering·2026
Same journal

Candidate EEG-derived biomarkers of epileptiform discharge occurrence in epilepsy.

Journal of neural engineering·2026
Same journal

Multimodal quantification of cognitive load using a printed wearable facial bio-potential system.

Journal of neural engineering·2026
Same journal

Deep-learning based electroencephalogram denoising: A literature review.

Journal of neural engineering·2026

Related Experiment Video

Updated: Nov 29, 2025

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

15.7K

Efficient high resolution sLORETA in brain source localization.

Younes Sadat-Nejad1, Soosan Beheshti2

  • 1Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada.

Journal of Neural Engineering
|November 19, 2020
PubMed
Summary

This study introduces efficient high-resolution standardized low-resolution brain electromagnetic tomography (EHR-sLORETA) to automatically denoise electroencephalography (EEG) data. The new method improves brain source estimation accuracy and reduces manual correction time.

Keywords:
EEG analysisEEG/MEG source imagingbrain source localizationsLORETAsource reconstruction

More Related Videos

Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain
06:52

Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain

Published on: January 26, 2024

2.6K
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

12.0K

Related Experiment Videos

Last Updated: Nov 29, 2025

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

15.7K
Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain
06:52

Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain

Published on: January 26, 2024

2.6K
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

12.0K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Brain source estimation from electroencephalography (EEG) and magnetoencephalography (MEG) is complex.
  • Standardized low-resolution brain electromagnetic tomography (sLORETA) is a popular but noisy method.
  • Current denoising methods like manual thresholding are time-consuming and suboptimal.

Purpose of the Study:

  • To develop an automated denoising technique for brain source estimation.
  • To introduce efficient high-resolution sLORETA (EHR-sLORETA) for adaptive thresholding.
  • To improve the accuracy and robustness of EEG source localization.

Main Methods:

  • Proposed an adaptive thresholding method called EHR-sLORETA.
  • Minimized the error between desired denoised sources and estimated sources.
  • Evaluated the method using synthetic and real EEG data.

Main Results:

  • EHR-sLORETA demonstrated improved accuracy and robustness compared to existing methods.
  • Quantitative metrics including spatial dispersion (SD) and mean square error (MSE) showed superior performance.
  • Qualitative analysis confirmed the effectiveness of EHR-sLORETA on real EEG data.

Conclusions:

  • EHR-sLORETA effectively automates the denoising process in brain source estimation.
  • The method enhances the accuracy of source localization from EEG.
  • EHR-sLORETA eliminates the need for manual thresholding, saving time in clinical applications.