Cluster membership definition and computation
For each participant we used 4 independent recordings (corresponding to each pre-stimulation EEG resting state recorded in sessions T3–T6). We thus obtained 4 different membership functions for each subject, which may present small variability due to the time between recording sessions, i.e. 1 week. The FCM algorithm directly provides cluster prototypes and a fuzzy membership function of each particular datapoint to the different clusters. In Spectral Clustering, which does not provide these as direct outputs, cluster prototypes are first calculated as the average of the points belonging to each specific cluster. The membership is then calculated as a probability function of the Euclidean distance from each datapoint to the aforementioned prototype. As we were trying to establish a participant profile, we averaged the cluster membership functions of each subject over the 4 sessions. This average membership function is expected to be more robust than the individual session membership and to get rid of the temporal variability of subject profiles.It is worth pointing out that the membership of each data sample to each of the clusters is defined in a real-valued domain in order to avoid loosing information due to the binarization of the data. By defining memberships in a real-valued domain we are able to treat differently the points that are close to the cluster centres and the points that are far from them, i.e. since they present very different membership values to the cluster under consideration. This is expected to increase the robustness of the analysis pipeline, given the fact that early binarization thresholds largely influence final results.