Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

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.

This semester have answered 10 questions in the field of brain disorder, such as how to identify an efficient pair of markers to a code of bios in today's diagnosis. The main advantage of this technique is that it could emanate as a reference of the pending problem when you're adhering to traditional EEG analysis. The applications of this technique extend toward our understanding of resting-states networks of human brain.

To begin this procedure, import the raw EEG data to the EEG lab software. Next, load the channel location file into the EEG lab software to obtain the spatial locations of these electrodes. To remove the reference electrodes, an option of Select data in channel range of the popup dialog box, select only the recording electrodes, and do not select the reference electrodes so that the reference electrodes can be removed.

To band pass filter the EEG data between 0.5 and 80 hertz, in the popup dialog box, choose 0.5 for the lower edge of the frequency pass band hertz and choose 80 for the higher edge of the frequency pass band hertz. Then click Ok.To remove the powerline noise with a notch filter between 49 and 51 hertz, in the popup dialog box, choose 49 for the lower edge of the frequency pass band hertz and choose 51 for the higher edge of the frequency pass band hertz. Then select the option of Notch filter the data instead of pass band, and click Ok.To remove eye movements, click on Tools, then click Artifact removal using AAR 1.3, and EOG removal using BSS.

To remove EMG, click Tools, then click Artifact removal using AAR 1.3 and EMG removal using BSS. Next, segment the preprocessed continuous EEG data into epochs with an epoch length of two seconds. A window will pop up that allows the saving of the segmented EEG data.

Then import the segmented EEG data to the EEG lab software and reject EEG epochs with amplitude values exceeding plus or minus 80 microvolts at any electrode. Next, save the preprocessed EEG data. In this procedure for each subject, load the preprocessed EEG data, convert reference channels to common average reference, and band pass filter the EEG data between two and 20 hertz.

Next, identify the four microstate maps in each subject. In the popup dialog box, choose three for the min number of classes, choose six for the max number of classes, choose 50 for the number of restarts, choose max number of maps to use, and select the options of GFP peak only, and No polarity. Then, click the Ok button.

Subsequently, save the EEG data of each subject after identifying its own microstate maps. Import the EEG data sets of all subjects saved in the last step at once. Then identify the group level microstate maps.

In the popup dialog box, select the data sets of all subjects in the option, Choose sets for averaging. In the option, Name of mean, give a name for the group level microstate maps, then click the button Ok.This will create a new data set named as GrandMean which stores the group level microstate maps. Manually sort the order of four group level microstate maps according to their classic order.

In the popup, select More, and then the number of maps shown becomes four. After that, select Man sort. In the popup dialog box, enter the new order of four group level microstate maps and click Close.

Following that, sort the order of the four microstate maps of each subject, save the microstate parameters for each subject which will invoke two popup dialog boxes sequentially. In the first dialog box, select the data sets of all subjects. In the second dialog box, select 4-Classes for option, Number of classes.

Select the options of Fitting only on GFP peaks and Remove potentially truncated microstates. Next, choose 30 for the label smoothing window, ms, and choose One for the the Non-Smoothness penalty, then click Ok.A CSV file which stores submicrostate parameters will be saved on the computer. These images show that microstate Class A and B have a right frontal to left occipital orientation, and a left frontal to right occipital orientation respectively.

Microstate Class C and D have symmetric topographies but prefrontal to occipital orientation, and front to central to occipital orientation were observed respectively. This table shows the mean and standard deviation of microstate parameters of the healthy subjects. Once mastered, this technique can be done in one hour if it is performed properly.

While attempting this procedure, it's important to remember that the EEG data should be preprocessed carefully. Following this procedure, other methods like source localization can be performed in order to answer additional questions such as where these microstate maps come from. This technique paved a way for researchers in the field of brain science to expose disease as in human brain.