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Inicio Revista Iberoamericana de Automática e Informática Industrial RIAI Selección de Canales en Sistemas BCI basados en Potenciales P300 mediante Intel...
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Vol. 14. Núm. 4.
Páginas 372-383 (Octubre - Diciembre 2017)
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Vol. 14. Núm. 4.
Páginas 372-383 (Octubre - Diciembre 2017)
DOI: 10.1016/j.riai.2017.07.003
Open Access
Selección de Canales en Sistemas BCI basados en Potenciales P300 mediante Inteligencia de Enjambre
P300-Based Brain-Computer Interface Channel Selection using Swarm Intelligence
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V. Martínez-Cagigala,
Autor para correspondencia
victor.martinez@gib.tel.uva.es

Autor para correspondencia.
, R. Horneroa,b,c
a Grupo de Ingeniería Biomédica, E.T.S.I. de Telecomunicación, Universidad de Valladolid, Valladolid, España
b IMUVA, Instituto de Investigación en Matemáticas, Universidad de Valladolid, Valladolid, España
c INCYL, Instituto de Neurociencias de Castilla y León, Salamanca, España
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Resumen

Los sistemas Brain-Computer Interface (BCI) se definen como sistemas de comunicación que monitorizan la actividad cerebral y traducen determinadas características, correspondientes a las intenciones del usuario, en comandos de control de un dispositivo. La selección de canales en los sistemas BCI es fundamental para evitar el sobre-entrenamiento del clasificador, reducir la carga computacional y aumentar la comodidad del usuario. A pesar de que se han desarrollado varios algoritmos con anterioridad para tal fin, las metaheurísticas basadas en inteligencia de enjambre aún no han sido suficientemente explotadas en los sistemas BCI basados en potenciales P300. En este estudio se muestra una comparativa entre cinco métodos de enjambre, basados en el comportamiento de sistemas biológicos, aplicados con el objetivo de optimizar la selección de canales en este tipo de sistemas. Los métodos se han evaluado sobre la base de datos de la “III BCI Competition 2005”, reportando precisiones similares o, en algunos casos, incluso más altas que las obtenidas sin realizar ningún tipo de selección. Dado que los cinco métodos se han demostrado capaces de disminuir drásticamente los 64 canales originales a menos de la mitad sin comprometer el rendimiento del sistema, así como de superar el conjunto típico de 8 canales y el método backward elimination, se concluye que todos ellos son adecuados para su aplicación en la selección de canales en sistemas P300-BCI.

Palabras clave:
Interfaces
aprendizaje automático
sistemas biomédicos
optimización y métodos computacionales
electroencefalografía
sistemas de comunicación
Abstract

Brain-Computer Interfaces (BCI) are direct communication pathways between the brain and the environment that translate certain features, which correspond to users’ intentions, into device control commands. Channel selection in BCI systems is essential to avoid over-fitting, to reduce the computational cost and to increase the users’ comfort. Although several algorithms have previously developed for that purpose, metaheuristics based on swarm intelligence have not been exploited yet in P300-based BCI systems. In this study, a comparative among five different swarm methods, based on the behavior of biological systems, is shown. Those methods have been applied in order to optimize the channel selection procedure in this kind of systems, and have been tested with the ‘III BCI Competition 2005’ database II. Results show that the five methods can achieve similar or even higher accuracies than that obtained without performing any channel selection procedure. Owing to the fact that all the applied methods are able to drastically reduce the required number of channels without compromising the system performance, as well as to overcome the common 8-channel set and the backward elimination algorithm, we conclude that all of them are suitable for use in the P300-BCI systems channel selection procedure.

Keywords:
Interfaces
machine learning
biomedical systems
optimization and computational methods
electroencephalography
communication systems
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