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Open AccessArticle

A New Approach for Motor Imagery Classification Based on Sorted Blind Source Separation, Continuous Wavelet Transform, and Convolutional Neural Network

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Facultad de Informática, Universidad Autónoma de Querétaro, C.P. 76230 Querétaro, Mexico
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Facultad de Ingniería, Universidad Autónoma de Querétaro, C.P. 76010 Querétaro, Mexico
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Author to whom correspondence should be addressed.
Sensors 2019, 19(20), 4541; https://doi.org/10.3390/s19204541
Received: 21 September 2019 / Revised: 14 October 2019 / Accepted: 15 October 2019 / Published: 18 October 2019
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. View Full-Text
Keywords: Brain-Computer Interface; Blind Source Separation; Movement Related Independent Component; Wavelet Transform; Convolutional Neural Network Brain-Computer Interface; Blind Source Separation; Movement Related Independent Component; Wavelet Transform; Convolutional Neural Network
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Ortiz-Echeverri, C.J.; Salazar-Colores, S.; Rodríguez-Reséndiz, J.; Gómez-Loenzo, R.A. A New Approach for Motor Imagery Classification Based on Sorted Blind Source Separation, Continuous Wavelet Transform, and Convolutional Neural Network. Sensors 2019, 19, 4541.

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