Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs.

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorLeite, Sarah Negreiros de Carvalho-
Autor(es): dc.creatorCosta, Thiago Bulhões da Silva-
Autor(es): dc.creatorSuarez Uribe, Luisa Fernanda-
Autor(es): dc.creatorSoriano, Diogo Coutinho-
Autor(es): dc.creatorYared, Glauco Ferreira Gazel-
Autor(es): dc.creatorCoradine, Luis Cláudius-
Autor(es): dc.creatorAttux, Romis Ribeiro de Faissol-
Data de aceite: dc.date.accessioned2025-08-21T15:37:33Z-
Data de disponibilização: dc.date.available2025-08-21T15:37:33Z-
Data de envio: dc.date.issued2016-01-28-
Data de envio: dc.date.issued2016-01-28-
Data de envio: dc.date.issued2015-
Fonte completa do material: dc.identifierhttp://www.repositorio.ufop.br/handle/123456789/6265-
Fonte completa do material: dc.identifierhttps://doi.org/10.1016/j.bspc.2015.05.008-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1020055-
Descrição: dc.descriptionBrain–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.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsO periódico Biomedical Signal Processing and Control concede permissão para depósito deste artigo no Repositório Institucional da UFOP. Número da licença: 3736501335741.-
Título: dc.titleComparative analysis of strategies for feature extraction and classification in SSVEP BCIs.-
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