Box 1.

Microstate clustering algorithm

Microstate clustering algorithm

Input: n average-referenced EEG samples (n × number_of_channels) from GFP peaks

Output: k maps that best characterise the data

1. Normalize each input sample to a vector of length 1

2. Pick k random samples as the initial maps

3. Label each sample as i ∈ {1, …k}, where i is the index of the map with highest absolute spatial correlation

4. Re-compute each map i as the first principal component of each cluster of samples labelled i

5. Compute the Global Explained Variance (GEV)

6. If GEV delta is small enough or maximum number of iterations has been reached, end; else, go to 3