Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/30849
Title: EEG Microstate Syntax Analysis: A Review of Methodological Challenges and Advances
Authors: Haydock, D
Kadir, S
Leech, R
Nehaniv, CL
Antonova, E
Keywords: neuroimaging;electroencephalography;biomarkers;resting-state EEG;cognition
Issue Date: 16-Feb-2025
Publisher: Elsevier
Citation: Haydock, D. et al. (2025) ‘EEG microstate syntax analysis: A review of methodological challenges and advances’, NeuroImage, 309, 121090, pp. 1 - 13. doi: 10.1016/j.neuroimage.2025.121090.
Abstract: Electroencephalography (EEG) microstates are “quasi-stable” periods of electrical potential distribution in multichannel EEG derived from peaks in Global Field Power. Transitions between microstates form a temporal sequence that may reflect underlying neural dynamics. Mounting evidence indicates that EEG microstate sequences have long-range, non-Markovian dependencies, suggesting a complex underlying process that drives EEG microstate syntax (i.e., the transitional dynamics between microstates). Despite growing interest in EEG microstate syntax, the field remains fragmented, with inconsistent terminologies used between studies and a lack of defined methodological categories. To advance the understanding of functional significance of microstates and to facilitate methodological comparability and finding replicability across studies, we: i) derive categories of syntax analysis methods, reviewing how each may be utilised most readily; ii) define three “time-modes” for EEG microstate sequence construction; and iii) outline general issues concerning current microstate syntax analysis methods, suggesting that the microstate models derived using these methods are cross-referenced against models of continuous EEG. We advocate for these continuous approaches as they do not assume a winner-takes-all model inherent in the microstate derivation methods and contextualise the relationship between microstate models and EEG data. They may also allow for the development of more robust associative models between microstates and functional Magnetic Resonance Imaging data.
Description: Data availability: No data was used for the research described in the article.
URI: https://bura.brunel.ac.uk/handle/2438/30849
DOI: https://doi.org/10.1016/j.neuroimage.2025.121090
ISSN: 1053-8119
Other Identifiers: ORCiD: David Haydock https://orcid.org/0000-0003-1247-0328
ORCiD: Shabnam Kadir https://orcid.org/0000-0002-0103-9156
ORCiD: Robert Leech https://orcid.org/0000-0002-5801-6318
ORCiD: Chrystopher L. Nehaniv https://orcid.org/0000-0002-7807-1875
ORCiD: Elena Antonova https://orcid.org/0000-0003-1624-3202
121090
Appears in Collections:Dept of Life Sciences Research Papers

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