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

Anterior Cingulate Cortex Mediates State-Dependent Prioritization of Distressed Conspecifics.

Brain sciences·2026
Same journal

Hemispherotomy for Pediatric Post-Traumatic Epilepsy.

Brain sciences·2026
Same journal

When Robots Learn: Artificial Intelligence and the Next Human-Centered Era of Neurorehabilitation.

Brain sciences·2026
Same journal

The Association Between Changes in White Matter Microstructure and Cognitive Function in Older Adults with Mild Cognitive Impairment.

Brain sciences·2026
Same journal

Beyond Ventricular Enlargement: Multimodal MRI Assessment Improves Surgical Decision-Making in Normal Pressure Hydrocephalus.

Brain sciences·2026
Same journal

The Effects of Personalized Observation, Execution, and Mental Imagery (POEM) Therapy in Logopenic Primary Progressive Aphasia: A Telepractice-Based Single-Case Study.

Brain sciences·2026

Related Experiment Video

Updated: Feb 22, 2026

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

9.6K

Feasibility of Equivalent Dipole Models for Electroencephalogram-Based Brain Computer Interfaces.

Paul H Schimpf1

  • 1Department of Computer Science, Eastern Washington University, Cheney, WA 99004, USA. pschimpf@ewu.edu.

Brain Sciences
|September 16, 2017
PubMed
Summary

This study reveals that realistic head models offer reliable source localization for brain-computer interfaces, unlike ambiguous spherical models. Realistic models enable robust classification parameters for electroencephalogram-based brain-computer interfaces.

Keywords:
brain–computer interfaceelectroencephalogramequivalent source model

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K
High-density Electroencephalographic Acquisition in a Rodent Model Using Low-cost and Open-source Resources
12:39

High-density Electroencephalographic Acquisition in a Rodent Model Using Low-cost and Open-source Resources

Published on: November 26, 2016

16.7K

Related Experiment Videos

Last Updated: Feb 22, 2026

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

9.6K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K
High-density Electroencephalographic Acquisition in a Rodent Model Using Low-cost and Open-source Resources
12:39

High-density Electroencephalographic Acquisition in a Rodent Model Using Low-cost and Open-source Resources

Published on: November 26, 2016

16.7K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Surface electroencephalogram (EEG) is crucial for brain-computer interfaces (BCIs).
  • Accurate source localization is vital for interpreting EEG signals and developing effective BCIs.
  • Previous methods often relied on simplified head models, potentially limiting accuracy.

Purpose of the Study:

  • To evaluate localization errors of equivalent dipolar sources from EEG.
  • To assess the feasibility of using source location as classification parameters for non-invasive BCIs.
  • To compare localization accuracy between spherical and realistic head models.

Main Methods:

  • Inversion of equivalent dipolar sources from surface EEG data.
  • Analysis of inverse localization errors using a four-concentric-sphere head model.
  • Examination of inverse localization errors using a realistic head model derived from medical imagery.
  • Investigation of source localization error versus signal-to-noise ratio.

Main Results:

  • Spherical head models exhibit significant localization ambiguity in azimuth and orientation, limiting their utility for BCI parameters.
  • Elevation of inverted sources in spherical models remains unambiguous and potentially useful for BCIs.
  • Realistic head models provide reliable localization for all three source parameters (azimuth, elevation, orientation).
  • Both models show local minima in residual error hypersurfaces, necessitating global search strategies.

Conclusions:

  • Realistic head models are superior to spherical models for accurate EEG source localization in BCIs.
  • Source location parameters derived from realistic models offer a more robust foundation for BCI development.
  • Global search algorithms are recommended for accurate dipole source localization to overcome local minima.