## 4 Results
### Grouping of Subjects with MI Skills Using Common GradCam++  
Another strategy to deal with the poor MI coordination skills issue is to improve the clustering of individuals to explore common dynamic representations across the subject set. Applying the Mahalanobis distance, we achieve CKA matching that is thus fed by the activation maps and the corresponding D&W CNN accuracy vector. However, the alignment is followed by PCA dimensional reduction to deal with the enlarged dimensionality of obtained dynamic representation; each GradCam-based map is computed on the trial basis from both brain rhythms (\(\mu\), \(\beta\)), at each time window \(\tau\), using two feature extraction methods (CWT and FBCSP), and for either MI label \(\mathbf{\Lambda}\). The result is a reduced feature space corresponding to the discriminating time-frequency representations (encoded in the attention maps) that contribute most to the classification performance. In analogy to the partition above presented, we present the results of clustering the reduced feature space, for which the Silhouette score-based cost yields three clusters to feed the \(k\)-means algorithm. The scatter plot in Figure 5(a) depicts the obtained subject's membership of motor skills (painted in the same color bars as above), showing a good within-group separation. However, some outlier subjects affect the between-group compactness (namely, #11 and #12). According to the newly assessed partitions, Figure 5(b) displays the subjects ranked in decreasing order of the accuracy obtained by the D&W CNN classifier (blue line), showing that both outlier points scramble the corresponding groups of membership. It is worth noting that the newly estimated poor-performing subjects (G-III) are the only ones achieving accuracy scores under the 70%-threshold (red line), except for the outlier point #11. For comparison, the arrangement in Figure 6c depicts the cells colored according to the individual clusters and shows the difference in membership assigned by the common GradCam representations (bottom row) and the above-explained pre-training motor abilities (top row). As seen, the former clustering is less scrambled and yields a more reduced number of subjects with the poor MI skills issue, resulting in the set of poor-performing individuals in Group III of the last partition. Notably, both outliers (#11 and #12) are apart from their designated group, regardless of the evaluated clustering approach. Additionally, the feature maps computed are based on motor skills to determine the importance of each electrode location in terms of the CNN decision-making process. Thus, the interpretation of the spatial contributions improves in discriminating between both labels of intention recognition. Figure 7 displays the GradCam maps of tree subjects (each one belonging to a different group of MI skills) computed within the considered intervals of the MI paradigm timeline. As seen in the top row, the activation weights produced by subject # 41 (Group I) are low at the two initial windows (pre-cue and cue) because of a lack of stimulation. Instead, the maps reveal boosted neural activity during the following two intervals of MI responses (after cue). Moreover, we can observe a few clusters with powerful contributions that are rightly focalized over the sensorimotor area. Further, the contribution of neural brain activity decreases at the last window because of desynchronization before the break. Generally, the whole timeline of activations generated by this well-performing subject fulfills the MI paradigm. Figure 6: Resulting clustering performed by the CKA-based data projection from GradCam++ activation maps. (**a**) Obtained subject’s membership, (**b**) Ranking of the accuracy obtained by the D&W CNN classifier, and (**c**) Membership difference between the common GradCam representations and pre-training motor abilities. Figure 7: Max-pooling representation of GradCam++ maps achieved by the representative subjects: Top raw # 41 (G I), Middle: # 31 (G II), Bottom: # 38(G III).