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Brain Waves

Brain Waves

Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:

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Related Experiment Video

Updated: Jul 23, 2025

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

Normative Intercorrelations Between EEG Microstate Characteristics.

Tobias Kleinert^1,2, Kyle Nash³, Thomas Koenig⁴

¹Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors, Ardeystr. 67, 44139, Dortmund, Germany. kleinert.science@gmail.com.

Brain Topography

|July 14, 2023

Summary

This summary is machine-generated.

Electroencephalography (EEG) microstate temporal characteristics reveal network interactions. Microstate A and B show mutual reinforcement, while Microstate C influences others, potentially linking to the default mode network.

Keywords:

EEG microstates Global field power (GFP)Microstate C Neural networks Normative correlations Temporal dynamics

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Last Updated: Jul 23, 2025

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

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Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

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Published on: August 20, 2019

Area of Science:

Neuroscience
Brain Activity Analysis
Electroencephalography (EEG)

Background:

EEG microstates are brief, stable brain activity periods reflecting large-scale neural networks.
Temporal microstate characteristics (duration, occurrence, contribution) are potential biomarkers for neurological disorders.
Understanding intercorrelations between microstate parameters is crucial for network function insights.

Purpose of the Study:

To systematically analyze intercorrelations between EEG microstate temporal characteristics.
To establish normative intercorrelations in a large, representative population sample.
To explore relationships between microstate parameters and underlying neural networks.

Main Methods:

Analysis of intercorrelations between EEG microstate temporal characteristics (duration, occurrence, contribution).
Utilized a large sample (n=583) representative of western working populations.
Findings validated using independent EEG recordings from a retest session (n=542).

Main Results:

Microstate duration is a general characteristic varying across different microstate types.
Microstates A and B exhibit mutual reinforcement, suggesting linked auditory and visual processing at rest.
Microstate C is associated with longer durations of other microstates and increased global field power, potentially linking to the anterior default mode network.

Conclusions:

Established normative intercorrelations between EEG microstate temporal characteristics.
Identified specific relationships between microstates A, B, and C, offering insights into resting-state network interactions.
Findings support the role of microstate dynamics in reflecting neural network function and provide a basis for future research.