MICROSTATELAB: The EEGLAB Toolbox for Resting-State Microstate Analysis

Abstract

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.

Resting-State Electroencephalography Alpha Dynamic Connectivity: Quantifying Brain Network State Evolution in Individuals with Psychosis

“…From the resulting BNS sequence, on top of the current usual metrics derived from classical microstates 43 , new metrics can be extracted based on complexity measures 44 , which have already shown results in the observed dataset 19 . Therefore, our approach can offer extensive metrics correlating well with cognitive and clinical scales, describing the brain dynamics in resting and potentially task-related states 26,27 .…”

“…Based on the principles of EEG microstate analysis, we conducted a clustering procedure using a modified k-means algorithm 58,59 . This technique was chosen due to its ability to identify stable and recurring patterns of brain activity, as it was widely used in classical EEG microstate analyses 43,60 , which we will refer to as BNS 26,27 .…”

“…From these resulting datasets, we extracted the following BNS metrics: global connectivity power (GCP), i.e., the standard deviation of all connectivity indices, as derived from global field power (GFP) 63 , explained variance (GEV), coverage, duration, occurrence, and transition probabilities 43 .…”

Changes in EEG microstate dynamics and cognition post-chemotherapy in people with breast cancer: A pilot study

“…EEG microstate analysis was performed in MATLAB (v2023b) using the MICROSTATELAB toolbox (v1.0) 33 . Mean microstate maps were first generated for all participants at both timepoints (see Figure 2A) before being mapped onto the widely used microstate templates, and then backfitted (i.e., the raw EEG was re-expressed as a sequence of microstate classes) to each participants' individual EEG time series for feature extraction.…”

“…Mean microstate maps were first generated for all participants at both timepoints (see Figure 2A) before being mapped onto the widely used microstate templates, and then backfitted (i.e., the raw EEG was re-expressed as a sequence of microstate classes) to each participants' individual EEG time series for feature extraction. Following the advice of Nagabhushan Kalburgi et al 33 , grand mean maps were used as the template for backfitting to ensure optimal comparability across participants and the most conservative analysis of extracted microstate features. At present, there is no consensus on how to determine the optimal number of classes to use for EEG microstate analysis 16 .…”