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Review
. 2023 Feb 17:17:1082317.
doi: 10.3389/fnhum.2023.1082317. eCollection 2023.

Application of EEG in migraine

Ning Zhang  1   2   3 , Yonghui Pan  1 , Qihui Chen  1 , Qingling Zhai  1 , Ni Liu  1 , Yanan Huang  1 , Tingting Sun  1 , Yake Lin  1 , Linyuan He  1 , Yue Hou  1 , Qijun Yu  1 , Hongyan Li  1 , Shijiao Chen  1
Affiliations
Review

Application of EEG in migraine

Ning Zhang et al. Front Hum Neurosci. .

Abstract

Migraine is a common disease of the nervous system that seriously affects the quality of life of patients and constitutes a growing global health crisis. However, many limitations and challenges exist in migraine research, including the unclear etiology and the lack of specific biomarkers for diagnosis and treatment. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity. With the updating of data processing and analysis methods in recent years, EEG offers the possibility to explore altered brain functional patterns and brain network characteristics of migraines in depth. In this paper, we provide an overview of the methodology that can be applied to EEG data processing and analysis and a narrative review of EEG-based migraine-related research. To better understand the neural changes of migraine or to provide a new idea for the clinical diagnosis and treatment of migraine in the future, we discussed the study of EEG and evoked potential in migraine, compared the relevant research methods, and put forwards suggestions for future migraine EEG studies.

Keywords: EEG; brain network; functional connectivity; machine learning; microstates; migraine; spectrum power.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Feature extraction methods.
FIGURE 2
FIGURE 2
Functional connectivity metrics.
FIGURE 3
FIGURE 3
Inverse solution methods.
FIGURE 4
FIGURE 4
Methods and technologies of machine learning.
FIGURE 5
FIGURE 5
The flow chart of the EEG data analysis.
FIGURE 6
FIGURE 6
Spectral power analysis metrics.
See this image and copyright information in PMC

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