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dc.contributor.authorLeite, Sarah Negreiros de Carvalho-
dc.contributor.authorCosta, Thiago Bulhões da Silva-
dc.contributor.authorSuarez Uribe, Luisa Fernanda-
dc.contributor.authorSoriano, Diogo Coutinho-
dc.contributor.authorYared, Glauco Ferreira Gazel-
dc.contributor.authorCoradine, Luis Cláudius-
dc.contributor.authorAttux, Romis Ribeiro de Faissol-
dc.identifier.citationLEITE, S. N. de C. et al. Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs. Biomedical Signal Processing and Control, v. 21, p. 34-42, 2015. Disponível em: <>. Acesso em: 19 out. 2015.pt_BR
dc.description.abstractBrain–computer interface (BCI) systems based on electroencephalography have been increasingly usedin different contexts, engendering applications from entertainment to rehabilitation in a non-invasiveframework. In this study, we perform a comparative analysis of different signal processing techniquesfor each BCI system stage concerning steady state visually evoked potentials (SSVEP), which includes: (1)feature extraction performed by different spectral methods (bank of filters, Welch’s method and the mag-nitude of the short-time Fourier transform); (2) feature selection by means of an incremental wrapper,a filter using Pearson’s method and a cluster measure based on the Davies–Bouldin index, in additionto a scenario with no selection strategy; (3) classification schemes using linear discriminant analysis(LDA), support vector machines (SVM) and extreme learning machines (ELM). The combination of suchmethodologies leads to a representative and helpful comparative overview of robustness and efficiency ofclassical strategies, in addition to the characterization of a relatively new classification approach (definedby ELM) applied to the BCI-SSVEP systems.pt_BR
dc.titleComparative analysis of strategies for feature extraction and classification in SSVEP BCIs.pt_BR
dc.typeArtigo publicado em periodicopt_BR
dc.rights.licenseO periódico Biomedical Signal Processing and Control concede permissão para depósito deste artigo no Repositório Institucional da UFOP. Número da licença: 3736501335741.pt_BR
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