MICROSTATELAB: The EEGLAB Toolbox for Resting-State Microstate Analysis

• Published in: Brain topography Vol. 37; no. 4; pp. 621 - 645
• Main Authors: Nagabhushan Kalburgi, Sahana, Kleinert, Tobias, Aryan, Delara, Nash, Kyle, Schiller, Bastian, Koenig, Thomas
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2024; Springer Nature B.V
• Subjects: Batch processing; Biomedical and Life Sciences; Biomedicine; Brain - physiology; Brain mapping; Brain Mapping - methods; Brain research; Cluster analysis; Datasets; EEG; Electroencephalography - methods; Humans; Neural networks; Neurology; Neurosciences; Original Paper; Psychiatry; Psychotherapy; Rest - physiology; Signal Processing, Computer-Assisted; Software; Statistical analysis; Topography; Tutorial; EEG microstates; EEGLAB; Resting state; Toolbox
• ISSN: 0896-0267; 1573-6792; 1573-6792
• DOI: 10.1007/s10548-023-01003-5

Microstate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and detect outlier maps, (iii) visualize, sort, and label individual, mean, and grand mean microstate maps according to published maps, (iv) compare topographical similarities of group and grand mean microstate maps and quantify shared variances, (v) obtain the temporal dynamics of the microstate classes in individual EEGs, (vi) export quantifications of these temporal dynamics of the microstates for statistical tests, and finally, (vii) test for topographical differences between groups and conditions using topographic analysis of variance (TANOVA). Here, we introduce the toolbox in a step-by-step tutorial, using a sample dataset of 34 resting-state EEG recordings that are publicly available to follow along with this tutorial. The goals of this manuscript are (a) to provide a standardized, freely available toolbox for resting-state microstate analysis to the scientific community, (b) to allow researchers to use best practices for microstate analysis by following a step-by-step tutorial, and (c) to improve the methodological standards of microstate research by providing previously unavailable functions and recommendations on critical decisions required in microstate analyses.