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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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Updated: Feb 22, 2026

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

Exploring resting-state EEG complexity before migraine attacks.

Zehong Cao^1,2, Kuan-Lin Lai^3,4,5, Chin-Teng Lin^1,2

¹1 Center for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.

Cephalalgia : an International Journal of Headache

|September 30, 2017

Summary

This summary is machine-generated.

Electroencephalogram complexity increases in the preictal phase of migraine without aura, resembling normal brain activity. This finding supports a new classification model for potential preictal alerts.

Keywords:

EEG Migraine classification complexity resting-state

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Last Updated: Feb 22, 2026

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

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Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

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Area of Science:

Neuroscience
Complexity Science

Background:

Entropy-based methods offer insights into brain activity dynamics.
Understanding temporal complexity in electroencephalogram (EEG) is crucial for neurological disorder research.

Purpose of the Study:

To examine EEG complexity in the preictal phase of migraine without aura using fuzzy entropy.
To develop an EEG-based classification model distinguishing preictal and interictal phases.

Main Methods:

Recruited 40 migraine patients and 40 controls, collecting resting-state EEG from prefrontal and occipital areas.
Defined preictal phase as within 72 hours before a migraine attack.
Utilized an inherent fuzzy entropy approach and support vector machine for classification.

Main Results:

EEG complexity was significantly higher in the preictal phase compared to the interictal phase in the prefrontal area (p < 0.05).
Preictal EEG complexity in migraine patients resembled that of normal control subjects.
The classification model achieved 76% accuracy in distinguishing preictal and interictal phases using prefrontal EEG complexity.

Conclusions:

Frontal EEG complexity normalizes during the preictal phase of migraine without aura.
The developed classification model shows potential for providing preictal alerts to migraine patients.