## 5 Discussion and Concluding Remarks
In addition, the activation maps are developed for optimization of the generalized model architecture to increase the CNN's learner performance further (See the accuracy achieved by D&W CNN-M), and at the same time investigate how brain regions and neural activity are closely connected. However, there are some important pitfalls, such as the gradient vanishing problem inherent to deep architectures that tends to result in noisy activation maps and thus deteriorates the visualization ability of the process as a whole [76]. Another concern is that the CAMs with higher weights show a lower contribution to the network's output than the zero-baseline of RELU activation. This phenomenon may have two sources: the global pooling operation on the top of the gradients and the gradient vanishing problem [77]. To cope with this issue, we randomly select activation maps, up-sample them into the input size, and then record how much the target score will be if we only keep the region highlighted in the activation maps. Interpretability of MI Skills using GradCam++To improve visualization efficiency in interpreting the MI coordination skills, we validate some strategies to accommodate training spaces, such as piece-wise computation of GradCam++ maps over time, clustering MI skills using common GradCams, and splitting the labeled training set. In this way, the interpretation of the spatial contributions improves in discriminating between both labels of motor imagery recognition providing better physiological interpretability of the paradigm. Hence, the post hoc visualization method developed for CNN architectures can be coupled with neurophysiologically grounded models of the Motor Imagery paradigm. As future work, the authors plan to fuse several CNN architectures as an alternative to the previous approach of separating temporal and spatial (like recurrent and Long Short-Term Memory Network), explicitly accounting for sequential dependencies in time. Since GradCam++ can be estimated on a trial basis, subject-specific visualization designs will be explored to address poor MI coordination skills using CNN frameworks with transfer learning [78]. In addition, we plan to investigate the combination of the CNN-based feature extraction approach and fuzzy classifiers due to the great versatility and transversality of \begin{table}
\begin{tabular}{l c c} \hline \hline
**Approach** & **Accuracy** & **Interpretability** \\ \hline EEGnet [70] & 66.0 & – \\ LSTM + Optical [71] & 68.2 \(\pm\) 9.0 & – \\ EGGnetv4 + EA [72] & 73.4 & ✓ \\ DC1 JIN [73] & 76.50 & ✓ \\ MINE + EEGnet [74] & 76.6 \(\pm\) 12.48 & ✓ \\ MSNN [75] & 81.0 \(\pm\) 12.00 & ✓ \\ \hline D\&W & 76.2 \(\pm\) 3.96 & ✓ \\ D\&W+GC++ & **84.1 \(\pm\) 3.46** & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of bi-class accuracy achieved by state-of-the-art approaches in _GigaScience_ collection. Compared approaches that include interpretability analysis are marked with ✓, else are marked with \(-\). fuzzy techniques, which, as is well known, are data independent [79]. In particular, we will explore the novel fuzzy classification based on fuzzy similarity techniques demonstrating good performance on the assessment of the mechanical the integrity of a steel plate [80]. Conceptualization, D.F.C.-H., A.M.A.-M. and G.C.-D.; methodology, D.F.C.-H., A.M.A.-M. and G.C.-D.; validation, D.F.C.-H. and A.M.A.-M.; data curation, D.F.C.-H.; writing--original draft preparation, D.F.C.-H. and G.C.-D.; writing--review and editing, D.F.C.-H., A.M.A.-M. and G.C.-D. All authors have read and agreed to the published version of the manuscript. This research manuscript is developed supported by \"Convocatoria Doctorados Nacionales COLCIENCIAS 785 de 2017\" (Mincincias). Also, under grants provided by the Mincincias project: Herramienta de apoyo a la predicicon de los efectos de anestesicos locales via neuroaxial epidural a partir de termografia por infrarrojoc code 111984468021. Not applicable.