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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 5th Brazilian Technology Symposium - Emerging Trends, Issues, and Challenges in the Brazilian Technology
EditorsYuzo Iano, Rangel Arthur, Osamu Saotome, Guillermo Kemper, Ana Carolina Borges Monteiro
PublisherSpringer Science and Business Media Deutschland GmbH
Pages131-139
Number of pages9
ISBN (Print)9783030575656
DOIs
StatePublished - 2021
Event5th Brazilian Technology Symposium, BTSym 2019 - Campinas, Brazil
Duration: 22 Oct 201924 Oct 2019

Publication series

NameSmart Innovation, Systems and Technologies
Volume202
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference5th Brazilian Technology Symposium, BTSym 2019
Country/TerritoryBrazil
CityCampinas
Period22/10/1924/10/19

Keywords

  • Multilayered perceptron
  • P300
  • Stroke patients
  • Support vector machines

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