Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Summary

This article describes the protocol underlying electroencephalography (EEG) microstate analysis and omega complexity analysis, which are two reference-free EEG measures and highly valuable to explore the neural mechanisms of brain disorders.

Abstract

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data and have been widely used to investigate the neural mechanisms in some brain disorders. The goal of this article is to describe the protocol underlying EEG microstate and omega complexity analyses step by step. The main advantage of these two measures is that they could eliminate the reference-dependent problem inherent to traditional spectrum analysis. In addition, microstate analysis makes good use of high time resolution of resting-state EEG, and the four obtained microstate classes could match the corresponding resting-state networks respectively. The omega complexity characterizes the spatial complexity of the whole brain or specific brain regions, which has obvious advantage compared with traditional complexity measures focusing on the signal complexity in a single channel. These two EEG measures could complement each other to investigate the brain complexity from the temporal and spatial domain respectively.

Introduction

Electroencephalography (EEG) has been widely used to record electrical activity of the human brain both in clinical diagnosis and scientific research, since it is noninvasive, low-costed and has very high temporal resolution¹. In order to study the EEG signals in resting state, researchers have developed many EEG techniques (e.g., power spectrum analysis, functional connectivity analysis)^2,3. Of these, microstate analysis and omega complexity analysis could make good use of the spatial and temporal information inherent in EEG signals⁴.

Protocol

This protocol was approved by the local ethical committee. All the participants and their parents signed an informed consent form for this experiment.

1. Subjects

1. Only include 15 healthy male adolescent subjects, whose age ranges from 14 to 22 years (mean ± standard deviation: 18.3 ± 2.8 years).
   NOTE: The current protocol to analyze the microstate and omega complexity has been developed for healthy subjects, but is not restricted to this group only.

2. EEG data recording

1. Ask subjects to sit on a comfortable chair in a silent, temperature-controlled room, where the E....

Results

EEG microstate

Grand mean normalized microstate maps are displayed in Figure 1. The electric potential landscapes of these four microstate classes identified here are very similar to those found in previous studies⁴.

The mean and standard deviation (SD) of microstate parameters of the healthy subjects were shown in

Discussion

In this article, two kinds of EEG analytic methods (i.e., microstate analysis and omega complexity analysis), measuring the temporal complexity and spatial complexity of human brain respectively, were described in detail. There are several critical steps within the protocol that should be mentioned. Firstly, the EEG data must be cleaned before the computation of the microstate and omega complexity. Secondly, the EEG data should be remontaged against the average reference before the computation of the microstate and omega.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
ANT 20 channels EEG/ERP system	ASA-Lab, ANT B.V., Netherlands		company web address: http://www.ant-neuro.com/
EEGLAB plugin for Microstates	Thomas Koenig		https://sccn.ucsd.edu/wiki/EEGLAB_Extensions_and_plug-ins
sLORETA	Roberto D. Pascual-Marqui		http://www.uzh.ch/keyinst/loreta.htm
MATLAB 2010a	The MathWorks Inc.		company web address: http://www.mathworks.com/
eeglab	Swartz Center for Computational Neuroscience, University of California, San Diego		https://sccn.ucsd.edu/eeglab/index.php