Identifying microstates on the level of the individual EEG (first level clustering)

Identifying microstates on the level of the individual EEG (first level clustering)

Before you start the microstate analysis, make sure that your data satisfies the following criteria

• Average reference
• No more significant artifacts
• Suitably filtered (the low-cut is typically at 1-2Hz, the hig-cut 20-30Hz)

The identification of microstates on the level of the individual EEG is accessed thru the menu Tools->Microstates->Identify Microstates". This opens the following dialog:

Alternatively, the analysis step can also be started on the command line or using a script. In this case, the function to be called is

[EEG,com] = pop_FindMSTemplates(EEG,ClustPar,ShowMaps,ShowDyn);

typically followed by

[ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG, CURRENTSET);

where the EEG contains the structure with the data to be clustered, EEGOUT is the data structure with the obtained clusters, com is a string with the command necessary to replicate the computation, ShowMaps and ShowDyn are boolean variables to toggle on or off the display of the results, and ClustPar is a structure that corresponds to the parameters of the above dialog, containing the following:

• "Min number of classes" / ClustPar.MinClasses -> Minimal number of clusters to search for.
• "Max number of classes" / ClustPar.MaxClasses -> Maximum number of clusters to search for.
• "Number of restarts" / ClustPar.Restarts -> Number of times the k-means is restarted with a new random configuration (I use about 20 to 50).
• "Max number of maps to use" / ClustPar.MaxMaps -> Use a random subsample of the data to identify the clusters (make this inf to include all data).
• "GFP peaks only" / ClustPar.GFPPeaks -> Limit the selection of maps used for cluster to moments of GFP peaks (often the standard).
• "No polarity" / ClustPar.IgnorePolarity -> Assign maps with inverted polarity to the same class (standard for resting EEG).
• "Use AAHC Algorithm" / ClustPar.UseAAHC -> Use the AAHC algorithm instead of the k-means (I prefer the k-means).
• "Normalize EEG before clustering" / ClustPar.UseAAHC -> Make all data GFP = 1 before clustering (I prefer not to).

For those wanting access to the result in matlab directly, this creates a sub-structure in the EEG called msinfo that is pretty self-explaining.