A Smart Home Control Prototype Using a P300-Based Brain–Computer Interface for Post-stroke Patients

Sergio A. Cortez, Christian Flores, Javier Andreu-Perez

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

10 Citas (Scopus)

Resumen

In this paper, we present and compare the accuracy of two types of classifiers to be used in a Brain–Computer Interface (BCI) based on the P300 waveforms of three post-stroke patients and six healthy subjects. Multilayer Perceptrons (MLPs) and Support Vector Machines (SVMs) were used for single-trial P300 discrimination in EEG signals recorded from 16 electrodes. The performance of each classifier was obtained using a five-fold cross-validation technique. The classification results reported a maximum accuracy of 91.79% and 89.68% for healthy and disabled subjects, respectively. This approach was compared with our previous work also focused on the P300 waveform classification.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 5th Brazilian Technology Symposium - Emerging Trends, Issues, and Challenges in the Brazilian Technology
EditoresYuzo Iano, Rangel Arthur, Osamu Saotome, Guillermo Kemper, Ana Carolina Borges Monteiro
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas131-139
Número de páginas9
ISBN (versión impresa)9783030575656
DOI
EstadoPublicada - 2021
Evento5th Brazilian Technology Symposium, BTSym 2019 - Campinas, Brasil
Duración: 22 oct. 201924 oct. 2019

Serie de la publicación

NombreSmart Innovation, Systems and Technologies
Volumen202
ISSN (versión impresa)2190-3018
ISSN (versión digital)2190-3026

Conferencia

Conferencia5th Brazilian Technology Symposium, BTSym 2019
País/TerritorioBrasil
CiudadCampinas
Período22/10/1924/10/19

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