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

Pérez Martínez, Jose Luis and Barrientos Cruz, Antonio (2010). BRAIN COMPUTER INTERFACE - Application of an Adaptive Bi-stage Classifier based on RBF-HMM. In: "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.

Description

Title: BRAIN COMPUTER INTERFACE - Application of an Adaptive Bi-stage Classifier based on RBF-HMM
Author/s:
  • Pérez Martínez, Jose Luis
  • Barrientos Cruz, Antonio
Item Type: Presentation at Congress or Conference (Article)
Event Title: Third International Conference on Biomedical Electronics and Devices
Event Dates: 20/01/2010 - 23/01/2010
Event Location: Valencia, España
Title of Book: Proceedings of the Third International Conference on Biomedical Electronics and Devices
Date: 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
Subjects:
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Electrónica e Informática Industrial [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (563kB) | Preview

Abstract

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.

More information

Item ID: 7968
DC Identifier: http://oa.upm.es/7968/
OAI Identifier: oai:oa.upm.es:7968
Official URL: http://www.biodevices.biostec.org/BIODEVICES2010/index.htm
Deposited by: Memoria Investigacion
Deposited on: 13 Jul 2011 07:38
Last Modified: 20 Apr 2016 16:53
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM