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Electroencephalography (EEG) microstates are brief brain patterns offering insights into neural organization. This review highlights challenges and future directions for robust microstate analysis in understanding brain function and disorders.

Keywords:
Alzheimer’s diseasecognitionconsciousnessfunctional brain networksresting-state EEGschizophrenia

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Area of Science:

  • Neuroscience
  • Brain Dynamics
  • Cognitive Science

Background:

  • Electroencephalography (EEG) microstates are transient, discrete topographic configurations of brain activity.
  • These states, lasting 50-120 ms, reflect large-scale neural network organization.
  • Microstates offer reference-independent insights into synchronized brain activity relevant to cognition and disease.

Purpose of the Study:

  • To review critical methodological challenges in EEG microstate analysis.
  • To propose optimization-based approaches for improved rigor and interpretability.
  • To outline future directions for advancing microstate research.

Main Methods:

  • Discussion of data-driven cluster optimization techniques for microstate identification.
  • Examination of template derivation strategies for robust group comparisons.
  • Emphasis on interpretive frameworks grounded in convergent evidence.

Main Results:

  • Identification of key methodological challenges including cluster optimization and template derivation.
  • Proposal for transitioning from arbitrary conventions to optimization-based methods.
  • Highlighting the need for characterizing higher-order temporal dynamics and multimodal integration.

Conclusions:

  • Methodological rigor is essential for advancing EEG microstate analysis.
  • Optimization-based approaches and validation standards are crucial for reliable findings.
  • Microstate analysis holds significant potential for understanding brain dynamics in health and disease.