A fuzzy genetic algorithm for optimal spatial filter selection for P300-based brain computer interfaces

David Achanccaray, Christian Flores, Christian Fonseca, Javier Andreu-Perez

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3 Citas (Scopus)

Resumen

A fuzzy genetic algorithm to optimize spatial filter selection can improve the performance of P300-based brain computer interfaces (BCI); genetic algorithm searches an optimal configuration supported by a fuzzy inference system, it would reduce the error calculated during a 4 fold crossvalidation. The performance is measured through the accuracy and the bit rate, 4 methods based on fuzzy logic and Bayesian linear discriminant analysis are considered for the performance comparison. This proposed method has obtained significant results for healthy persons and post stroke patients, accuracies above 90% and bit rates greater than 8 bits/min for the most of cases evaluated in a P300-based BCI using the Hoffman approach.

Idioma originalInglés
Título de la publicación alojada2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781509060207
DOI
EstadoPublicada - 12 oct. 2018
Evento2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Rio de Janeiro, Brasil
Duración: 8 jul. 201813 jul. 2018

Serie de la publicación

NombreIEEE International Conference on Fuzzy Systems
Volumen2018-July
ISSN (versión impresa)1098-7584

Conferencia

Conferencia2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018
País/TerritorioBrasil
CiudadRio de Janeiro
Período8/07/1813/07/18

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