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Related Experiment Video

Updated: Mar 6, 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

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Consistency of EEG source localization and connectivity estimates.

Keyvan Mahjoory1, Vadim V Nikulin2, Loïc Botrel3

  • 1Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova, Genova, Italy; Machine Learning Department, Technische Universität Berlin, Berlin, Germany.

Neuroimage
|March 17, 2017
PubMed
Summary

EEG source connectivity analysis shows variable results due to modeling choices. Researchers should use multiple source imaging procedures for reliable findings in electroencephalography (EEG) studies.

Keywords:
ConsistencyElectroencephalography (EEG)Forward/inverse modelingFunctional/effective connectivityReproducibilitySource localization

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • The electroencephalography (EEG) inverse problem lacks a unique solution, making source reconstruction and connectivity analysis dependent on modeling parameters.
  • Variability in anatomical templates, electrical models, inverse methods, and software implementations can impact EEG source connectivity results.
  • Establishing stability across standard estimation routines is crucial for reliable EEG source connectivity analysis.

Purpose of the Study:

  • To comprehensively assess the consistency of EEG source localization and functional/effective connectivity metrics across various standard estimation routines.
  • To identify the impact of different anatomical templates, electrical models, inverse methods, and software packages on EEG source connectivity results.
  • To provide guidance on improving the reliability of EEG source connectivity analysis.

Main Methods:

  • Utilized resting-state EEG recordings from N=65 participants across two studies.
  • Evaluated consistency across two anatomical templates (ICBM152, Colin27).
  • Compared three electrical models (BEM, FEM, spherical harmonics expansions) and three inverse methods (WMNE, eLORETA, LCMV) using three software implementations (Brainstorm, Fieldtrip, custom toolbox).

Main Results:

  • Source localizations demonstrated greater stability across pipelines compared to functional and effective connectivity estimations.
  • Effective connectivity estimates exhibited the least consistency.
  • Choice of inverse method and source imaging package significantly influenced variability, with notable differences between LCMV and eLORETA/WMNE.
  • Consistency decreased across studies, within participants, and between participants.

Conclusions:

  • EEG source connectivity results are sensitive to the chosen inverse method and software implementation.
  • While electrical head models had minimal impact, anatomical templates showed some influence.
  • Further simulations are needed to understand variability in interacting brain sources.
  • Researchers are encouraged to validate findings using multiple source imaging procedures to ensure reliability.