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.

This work presents a series of lectures and activities for Digital Signal Processing (DSP) teaching, based upon music and their principal elements such as melody, pitch, timbre, beat, and metric, to explain the time‐frequency analysis and its repercussions in other areas of engineering. DSP courses are difficult to the students due to a high mathematical content and high level of abstraction, for this reason, the primary objective of this work is to create a pedagogical tool that allows the improvement on the teaching process in Signal Processing courses. There are two popular approaches: Short‐Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) which have been used to find time‐frequency maps. The student roll was designed for active participation by creating melodies from real instruments as well as synthetic generators, and by implementing the codes of STFT and CWT in MATLAB. The results demonstrate that in general terms, after carrying out these laboratory activities the students are more motivated to learn Signal Processing theory, and some of them become interested in the new research line on Music Information Retrieval (MIR), which would benefit the continuity of the Accreditation Board for Engineering and Technologies ABET, obtained for the Automation Engineering program at Universidad Autónoma de Querétaro (UAQ) since 2016.

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