Resumen

SEPULVEDA, Roberto et al. Classification of Encephalographic Signals using Artificial Neural Networks. Comp. y Sist. [online]. 2015, vol.19, n.1, pp.69-88. ISSN 2007-9737.  https://doi.org/10.13053/CyS-19-1-1570.

For the signal classification of eye blinking and muscular pain in the right arm caused by an external agent, two models of artificial neural network architectures are proposed, specifically, the perceptron multilayer and an adaptive neurofuzzy inference system. Both models use supervised learning. The ocular and electroencephalographic time-series of 15 people in the range of 23 to 25 years of age are used to generate a data base which was divided into two sets: a training set and a test set. Experimental results in the time and frequency domain of 50 tests applied to each model show that both neural network architecture proposals for classification produce successful results.

Palabras llave : EEG; BCI; brain-computer interface; blink; artificial neural network; FFT.

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