Artículos

 

Clasificación de señales encefalográficas mediante redes neuronales artificiales

 

Classification of Encephalographic Signals using Artificial Neural Networks

 

Roberto Sepúlveda¹, Oscar Montiel¹, Gerardo Díaz¹, Daniel Gutierrez¹ y Oscar Castillo²

 

¹ Instituto Politécnico Nacional-CITEDI, Tijuana, B.C., México. rsepulvedac@ipn.mx, oross@ipn.mx, gdiaz@citedi.mx, dgutierrez@citedi.mx

² Instituto Tecnológico de Tijuana, Tijuana, B.C., México. ocastillo@tectijuana.mx

Autor de correspondencia es Roberto Sepúlveda.

 

Article received on 04/10/2013.
Accepted on 26/11/2014.

 

Resumen

Para la clasificación de las señales del parpadeo y dolor muscular en el brazo derecho ocasionado por un agente externo, se proponen dos modelos de arquitecturas de redes neuronales artificiales, específicamente del tipo perceptron multicapa y sistema de inferencia neurodifuso adaptativo, ambos modelos utilizan aprendizaje supervisado. Se utilizan series de tiempo obtenidas del parpadeo y electroencefalografías de 15 personas en el rango de 23 a 25 años de edad, para generar una base de datos que se divide en dos conjuntos de datos: entrenamiento y prueba. Los resultados experimentales en el dominio del tiempo y de la frecuencia, de 50 pruebas aplicadas a cada modelo de red, muestran que ambas propuestas de arquitecturas de redes neuronales producen resultados exitosos.

Palabras clave: EEG, BCI, interface cerebro-computadora, parpadeo, red neuronal artificial, FFT.

 

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.

Keywords. EEG, BCI, brain-computer interface, blink, artificial neural network, FFT.

 

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