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Related Experiment Video

Updated: May 29, 2025

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
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How does Independent Component Analysis Preprocessing Affect EEG Microstates?

Fiorenzo Artoni1,2, Christoph M Michel3,4

  • 1Department of Clinical Neurosciences, Faculty of Medicine, Université de Genève, Geneva, Switzerland. fiorenzo.artoni@unige.ch.

Brain Topography
|February 4, 2025
PubMed
Summary
This summary is machine-generated.

Electroencephalographic (EEG) microstate analysis is reliable for studying brain networks, but ocular artifact removal is crucial for accurate results. Proper preprocessing ensures microstate features capture brain activity robustly.

Keywords:
Artifact removalEEGIndependent component analysisMicrostatesPreprocessing

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Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Brain Network Dynamics

Background:

  • Electroencephalographic (EEG) microstates offer millisecond-scale insights into large-scale brain network dynamics.
  • Microstate research aids understanding of brain functional organization at rest and in disorders.
  • Inconsistent artifact removal strategies across studies may compromise microstate result generalizability.

Purpose of the Study:

  • To assess the reliability of EEG microstate extraction and feature stability under varying preprocessing strategies.
  • To evaluate the impact of Independent Component Analysis (ICA) based artifact removal on microstate analysis.
  • To determine the robustness of microstate features to different levels of EEG data preprocessing.

Main Methods:

  • Utilized a normative resting-state EEG dataset with alternating eyes-open (EO) and eyes-closed (EC) conditions.
  • Compared four artifact removal strategies using ICA: no ICA, ocular artifacts only, all artifacts, and brain components only.
  • Analyzed the effects of preprocessing on microstate evaluation criteria, topography, and EO/EC comparison power.

Main Results:

  • Skipping ocular artifact removal significantly impacts microstate stability and reduces statistical power for EO/EC comparisons.
  • More aggressive preprocessing (removing all artifacts or retaining only brain ICs) showed less prominent differences.
  • Microstate topographies and features are robust to artifacts when ocular artifacts are removed, even with varying preprocessing levels.

Conclusions:

  • Accurate EEG microstate analysis requires careful removal of ocular artifacts.
  • Microstate features are robust and can reliably reflect brain activity when preprocessing is adequate.
  • Standardized, automated microstate extraction pipelines are feasible with appropriate artifact handling.