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|Title:||Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs|
|Author:||Carvalho, Sarah N.|
Costa, Thiago B. S.
Uribe, Luisa F. S.
Soriano, Diogo C.
Yared, Glauco F. G.
Coradine, Luis C.
|Abstract:||Brain–computer interface (BCI) systems based on electroencephalography have been increasingly used in different contexts, engendering applications from entertainment to rehabilitation in a non-invasive framework. In this study, we perform a comparative analysis of different signal processing techniques for 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 magnitude 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 addition to 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 such methodologies leads to a representative and helpful comparative overview of robustness and efficiency of classical strategies, in addition to the characterization of a relatively new classification approach (defined by ELM) applied to the BCI-SSVEP systems|
|Appears in Collections:||FEEC - Artigos e Outros Documentos|
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