Capturing EEG Spectral Microstates Through K Means Clustering
Julio Rodriguez‐Larios · 2019
In this report, I propose an analytical approach based on k means clustering to identify functionally relevant spectral microstates in EEG data. As a proof of principle, the analysis is applied to an EEG data set in which participants performed a continuous arithmetic task. In order to assess the functional relevance of the identified ‘spectral microstates’, their occurrence are compared to inter-individual differences in arithmetic task performance.