## 5 Discussion and Concluding Remarks  
This study explores the ability to distinguish between motor imagery tasks and the interpretability of the brain's ability to produce elicited mental responses with improved accuracy. To this end, we develop a Deep and Wide convolutional neuronal network fed by a set of topoplots derived from the multi-channel EEG data for two brain rhythms (\(\mu\), \(\beta\)) through the feature extraction methods (WT and FBCSP). Then, a visualization approach based on gradient-based activation maps is employed to interpret the learned neural network weights discriminating between MI responses. In order to take into account the intra-subject variability of neural responses over time, each visualization map is extracted at different intervals along the MI paradigm timeline. Additionally, we cluster the common dynamic spatial representations of the extracted GradCam maps across the subject set, together with the accuracy values of the D&W NN classification, to account for the poor MI-BCI skills phenomenon. The results obtained from the evaluated GigaScience Database of motor-evoked potentials show the potential of the developed approach to improve the meaningful physiological explanation of motor imagery skills in aspects like neural synchronization between rhythms, brain lateralization, and the prediction ability of extracted GradCam maps. After the evaluation stage, however, the following points are worth mentioning:  
_Preprocessing and Extraction Topoplots_: Preprocessing of EEG signals is commonly implemented by three steps: artifact removal, channel selection, and frequency filtering. Based on the fact that DL can extract useful features from raw and unfiltered data, as a rule, the first two steps are not performed [65,66]. As channel selection may enable lower generalization errors in DL [67], we compute the topoplot representation from the whole set of EEG channels to visualize the spectral power variations on the scalp averaged over each MI interval. The topoplots extracted by FBCSP and CWT algorithms are inputs to a Convolutional Neural Network with a Deep and Wide architecture that is more generic for decoding EEG signals, delivering a competitive classification accuracy[68]. The feature extraction procedures require several parameters to fix, affecting the CNN learning properties like discriminability and interpretability. However, the short-time window selected to encode the latency of brain responses must be adjusted to extract temporal EEG dynamics accurately [69]. For example, as [61] suggests, using shorter window values may increase the performance of poor-performing subjects. _Achieved accuracy by CNN Neural-Network learner_: We take advantage of the D&W architecture for classification problems with multiple inputs so that a multi-view set of  
Figure 10: Prediction ability of extracted GradCam++. (**a**) Inertia estimation over training runs. (**b**) Correlation between the features extracted from the MI onset responses. extracted time-frequency features supports the CNN learner. The evaluated CNN classifier uses a multi-layer perceptron that includes batch normalization and fully connected layers designed to exploit the close spatial relationship present in the topoplot data set, improving performance on unseen examples. As a result, the implemented D&W CNN learner fed by the extracted feature sets significantly improves the tested GigaScience data set' accuracy, requiring a few trainable parameters. Table 2 compares the classifier performance of some recently reported works using CNN-based learners, underperforming both proposed scenarios of D&W CNN training. Computation of GradCam++ Visualization mapsIn MI EEG, the use of GradCam sets is growing, providing insight into the electrodes' ability to discriminate the elicited tasks. Specifically, we introduce the recently proposed GradCam++ method, which enables the identification of multiple instances of the same class and multidimensional representations of inputs.