Clasificación de señales encefalográficas mediante redes neuronales artificiales

Translated title of the contribution: Classification of encephalographic signals using artificial neural networks

Roberto Sepúlveda Cruz, Oscar Humberto Montiel Ross, Gerardo Díaz, Daniel Gutierrez, Oscar Castillo López

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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.

Translated title of the contributionClassification of encephalographic signals using artificial neural networks
Original languageSpanish
Pages (from-to)69-88
Number of pages20
JournalComputacion y Sistemas
Volume19
Issue number1
DOIs
StatePublished - 1 Jan 2015

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