**5. Conclusions**

In this work, the estimated independent components were obtained using a fastICA algorithm, separating relevant MI-related independent components from unwanted artifacts. However, the source separation by itself is not sufficient if for each trial, the order of them is not preserved. For this reason, a spectral correlation with MRIC helps to sort the sources by reducing the spatial variance, leaving in the last positions the sources with a more significant influence of artifacts and less *μ* and *β* components. These operations help to reduce the complexity in the search for relevant patterns in the posterior extraction and classification stages. The use of CWT maps in the feature extraction stage allows obtaining a 2D representation of time series. In contrast with Short-Time Fourier Transform (STFT), CWT performs a multi-resolution analysis. According to the experimentation carried out, obtained results in this work are competitive with the state-of-the-art with a 94.66% in the k-fold cross validation. Regarding the architecture of CNN, it was found that the hyper-parameters related to the size of the kernel as well as the kernel stride in each convolutional layer have a significant influence on network performance, while the number of convolutions has less impact in final accuracy. Two future works derived: first, the development of a methodology that allows to find the hyper-parameters close to the optimum and then, improve the current results. Second, to replace the BSS stage with some autoencoder architecture, as for example Variational Autoencoder (VAE), to obtain the estimated sources.

**Author Contributions:** Conceptualization, C.J.O.-E. and S.S.-C.; Methodology, C.J.O.-E. and S.S.-C.; Writing–original draft preparation, C.J.O.-E., S.S.-C., J.R.-R. and R.A.G.-L.; Writing–review and editing, C.J.O.-E., S.S.-C., J.R.-R. and R.A.G.-L.; Supervision, J.R.-R. and R.A.G.-L.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors would like to thank the Informatics Faculty and the Department of Engineering of the Universidad Autónoma de Querétaro, as well as Consejo Nacional de Ciencia y Tecnología (CONACyT) for the Doctorate Fellowship support.

**Conflicts of Interest:** The authors declare no conflict of interest.
