Selección de Canales en Sistemas BCI basados en Potenciales P300 mediante Inteligencia de Enjambre

V. Martínez-Cagigal, R. Hornero

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

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