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http://repositorio.unicamp.br/jspui/handle/REPOSIP/349593
Type: | Artigo |
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. Attux, Romis |
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 |
Subject: | Interfaces cérebro-computador Eletroencefalografia |
Country: | Países Baixos |
Editor: | Elsevier |
Rights: | Fechado |
Identifier DOI: | 10.1016/j.bspc.2015.05.008 |
Address: | https://www.sciencedirect.com/science/article/pii/S1746809415000877 |
Date Issue: | 2015 |
Appears in Collections: | FEEC - Artigos e Outros Documentos |
Files in This Item:
File | Description | Size | Format | |
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000358627300005.pdf | 2.59 MB | Adobe PDF | View/Open |
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