Leite, Sarah Negreiros de CarvalhoCosta, Thiago Bulhões da SilvaSuarez Uribe, Luisa FernandaSoriano, Diogo CoutinhoYared, Glauco Ferreira GazelCoradine, Luis CláudiusAttux, Romis Ribeiro de Faissol2016-01-282016-01-282015LEITE, 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: <http://www.sciencedirect.com/science/article/pii/S1746809415000877>. Acesso em: 19 out. 2015.1746-8094http://www.repositorio.ufop.br/handle/123456789/6265Brain–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.en-USArtigo publicado em periodicoO periódico Biomedical Signal Processing and Control concede permissão para depósito deste artigo no Repositório Institucional da UFOP. Número da licença: 3736501335741.https://doi.org/10.1016/j.bspc.2015.05.008