BRAIN COMPUTER INTERFACE - Application of an Adaptive Bi-stage Classifier based on RBF-HMM

Pérez Martínez, Jose Luis y Barrientos Cruz, Antonio (2010). BRAIN COMPUTER INTERFACE - Application of an Adaptive Bi-stage Classifier based on RBF-HMM. En: "Third International Conference on Biomedical Electronics and Devices", 20/01/2010 - 23/01/2010, Valencia, España. ISBN Brain Computer Interface is an emerging technology that allows new output paths to communicate the users intentions without the use of normal output paths, such as muscles or nerves. In order to obtain their objective, BCI devices make use of classifiers.

Descripción

Título: BRAIN COMPUTER INTERFACE - Application of an Adaptive Bi-stage Classifier based on RBF-HMM
Autor/es:
  • Pérez Martínez, Jose Luis
  • Barrientos Cruz, Antonio
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: Third International Conference on Biomedical Electronics and Devices
Fechas del Evento: 20/01/2010 - 23/01/2010
Lugar del Evento: Valencia, España
Título del Libro: Proceedings of the Third International Conference on Biomedical Electronics and Devices
Fecha: 2010
ISBN: Brain Computer Interface is an emerging technology that allows new output paths to communicate the users intentions without the use of normal output paths, such as muscles or nerves. In order to obtain their objective, BCI devices make use of classifiers
Materias:
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Automática, Ingeniería Electrónica e Informática Industrial [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Brain Computer Interface is an emerging technology that allows new output paths to communicate the users intentions without the use of normal output paths, such as muscles or nerves. In order to obtain their objective, BCI devices make use of classifiers which translate inputs from the users brain signals into commands for external devices. This paper describes an adaptive bi-stage classifier. The first stage is based on Radial Basis Function neural networks, which provides sequences of pre-assignations to the second stage, that it is based on three different Hidden Markov Models, each one trained with pre-assignation sequences from the cognitive activities between classifying. The segment of EEG signal is assigned to the HMMwith the highest probability of generating the pre-assignation sequence. The algorithm is tested with real samples of electroencephalografic signal, from five healthy volunteers using the cross-validation method. The results allow to conclude that it is possible to implement this algorithm in an on-line BCI device. The results also shown the huge dependency of the percentage of the correct classification from the user and the setup parameters of the classifier.

Más información

ID de Registro: 7968
Identificador DC: http://oa.upm.es/7968/
Identificador OAI: oai:oa.upm.es:7968
URL Oficial: http://www.biodevices.biostec.org/BIODEVICES2010/index.htm
Depositado por: Memoria Investigacion
Depositado el: 13 Jul 2011 07:38
Ultima Modificación: 20 Abr 2016 16:53
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