Performance Evaluation of a P300 Brain-Computer Interface Using a Kernel Extreme Learning Machine Classifier

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

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

4 Citas (Scopus)

Resumen

In this work, we present the use of Kernel Extreme Learning Machine (Kernel ELM) on electroencephalography EEG brain signals in order to classify the P300 wave during the subject development an oddball paradigm. Also, we propose a selection criteria in order to improve the classification accuracy. In this study, the brain signals of healthy and disabled subjects which suffered a stroke were recorded, analyzed and classified. The results reported that the best classification accuracy and average bitrate were 100% using target by block evaluation and 18.38 bits per minute, respectively. These results are compared to various machine learning algorithms so that our results outperformed them.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas3715-3719
Número de páginas5
ISBN (versión digital)9781538666500
DOI
EstadoPublicada - 2 jul. 2018
Evento2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japón
Duración: 7 oct. 201810 oct. 2018

Serie de la publicación

NombreProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conferencia

Conferencia2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
País/TerritorioJapón
CiudadMiyazaki
Período7/10/1810/10/18

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