Clustering results
We implemented the different clustering algorithms to establish homogeneous subgroups among subjects based on their resting-state EEG spectral features before intervention. The evolution of the validity measures over the different parameters does not offer a clear criterion for parameter selection. We believe this may be due to the high dimensionality of the feature space, i.e. the “curse of dimensionality”Feature space. Visualization of EEG data-points with t-SNE in 2 dimensions. Each point corresponds to a specific trial for each participant, the features of each data-point are the RBP in each channel.Concretely we can observe some singular points, i.e. “elbows”, for Clustering results for spectral clustering. The left plot shows the feature Space, the colour code represents each cluster. From all data-points, 42,7% lie in Cluster 1, 7.3% in Cluster 2, 18.4% in Cluster 3 and 31.6% in Cluster 4. The right plots show the evolution of the internal validity metrics Silhouette and Davies Bouldin when changing the number of centres. An elbow at k=4 can be seen.