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

• Autor(a) principal: Carvalho, Sarah Negreiros de
• Data de Publicação: 2015
• Outros Autores: Costa, Thiago Bulhões da Silva, Suarez Uribe, Luisa Fernanda, Soriano, Diogo Coutinho, Yared, Glauco Ferreira Gazel, Coradine, Luis Cláudius, Attux, Romis Ribeiro de Faissol
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFOP
• Texto Completo: http://www.repositorio.ufop.br/handle/123456789/6265; https://doi.org/10.1016/j.bspc.2015.05.008

Brain–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.