**César J. Ortiz-Echeverri 1, Sebastián Salazar-Colores 1, Juvenal Rodríguez-Reséndiz 2,\* and Roberto A. Gómez-Loenzo <sup>2</sup>**


Received: 21 September 2019; Accepted: 15 October 2019; Published: 18 October 2019

**Abstract:** Brain-Computer Interfaces (BCI) are systems that allow the interaction of people and devices on the grounds of brain activity. The noninvasive and most viable way to obtain such information is by using electroencephalography (EEG). However, these signals have a low signal-to-noise ratio, as well as a low spatial resolution. This work proposes a new method built from the combination of a Blind Source Separation (BSS) to obtain estimated independent components, a 2D representation of these component signals using the Continuous Wavelet Transform (CWT), and a classification stage using a Convolutional Neural Network (CNN) approach. A criterion based on the spectral correlation with a Movement Related Independent Component (MRIC) is used to sort the estimated sources by BSS, thus reducing the spatial variance. The experimental results of 94.66% using a k-fold cross validation are competitive with techniques recently reported in the state-of-the-art.

**Keywords:** Brain-Computer Interface; Blind Source Separation; Movement Related Independent Component; Wavelet Transform; Convolutional Neural Network
