## 4 Results  
### Achieved Accuracy of Implemented D\(\mathcal{\mathcal{E}}\)W CNN Classifier  
We estimate the classifier performance evaluated through the cross-validation strategy for validation purposes, reserving the 90% of points as training, while the remaining group is a hold-out data set. To this end, we employ a stratified \(K\)-Folds cross-validation, adjusting \(K\)=5. Figure 5 displays the values of accuracy obtained by the Linear Discriminant Analysis algorithm fed by the features extracted using the FBCSP method (plotted as an orange line), as described in [52]. Based on this baseline discriminating method of handcrafted features, all subjects are ranked in decreasing order of reached mean accuracy, showing that more than half of the subjects fall under the 70%-level poor MI skills (indicated by the red line); this results in a very ineffective MI training scheme. As a way of improving this analysis of the poor MI skills phenomenon, the subjects can be grouped according to their pre-training motor abilities that are critical to performing MI tasks. A common approach for clustering the individual set is to form partitions using both the features extracted from data of the state of wakefulness conscious together with the accuracy values achieved by one selected classifier under consideration [54]. Specifically, we compute the pre-training indicator that quantifies the potential for desynchronization over the sensorimotor area at the pre-cue interval evaluated in the 1-s window preceding the movement onset (\(\tau_{1}\)). Utilizing the accuracy values given by FBCSP, the Silhouette score-based cost yields three clusters to perform the \(k\)-means algorithm for estimating each subject membership of poor MI-BCI coordination skills group. Consequently, Figure 5 presents the obtained groups of motor skills painted in color bars: the best-performing subjects (Group I colored in green); the ones with intermediate MI abilities (Group II in \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Layer** & **Assignment** & **Output Dimension** & **Activation** & **Mode** \\ \hline
**IN1** & Input & \([40\times 40]\) & & \\
**CN2** & Convolution & \([40\times 40\times 2]\) & ReLu & _Padding_ = SAME \\  & & & & _Size_ = \(3\times 3\) \\  & & & & _Stride_ = \(1\times 1\) \\
**BN3** & Batch-normalization & \([40\times 40\times 2]\) & & \\
**MP4** & Max-pooling & \([20\times 20\times 2]\) & & _Size_ = \(2\times 2\) \\  & & & & _Stride_ = \(1\times 1\) \\
**CT5** & Concatenation & \([20\times 20\times O\cdot I_{L}]\) & & \\
**FL6** & Flatten & \(20\cdot 20\cdot O\cdot I_{L}\) & & \\
**BN7** & Batch-normalization & \(20\cdot 20\cdot O\cdot I_{L}\) & & \\
**FC8** & Fully-connected & \([P^{\prime}\times 1]\) & ReLu & Elastic-Net \\  & & & & _max\_norm_(1.) \\
**BN9** & Batch-normalization & \([P^{\prime}\times 1]\) & & \\
**OU10** & Output & \([C\times 1]\) & Softmax & _max\_norm_(1.) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed _Deep\(\mathcal{E}\)Wide_ architecture. Layer FC8 implements the regularization procedure using the _Elastic-Net_ configuration, whereas layers FC8 and OU10 apply a kernel constraint adjusted to _max_norm_(1.). Figure 5: Achieved MI-related classification performance. Note: the red line at 70% level shows the poor MI coordination skill threshold, under the subjects are considered as worse-performing. yellow); and Group III (red) is the set of individuals with the worst performance (half of the set, 27 individuals), as well as with an accuracy below 70%. Next, as a strategy to reduce the problem of poor MI skills [55], we improved MI's accuracy by implementing a D&W CNN classifier (blue line), which increased it from \(67.7408\pm 5.89\) (FBCSP) to \(76.218\pm 3.96\) on average over the whole subject set. Nevertheless, the CNN-based accuracy can be further enhanced by modifying the set of input topograms through the activation maps estimated after the training stage, similarly as suggested in [56].