Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/90641
Type: Artigo de evento
Title: Automatic Feature Selection For Bci: An Analysis Using The Davies-bouldin Index And Extreme Learning Machines
Author: Coelho G.P.
Barbante C.C.
Boccato L.
Attux R.R.F.
Oliveira J.R.
Von Zuben F.J.
Abstract: In this work, we present a novel framework for automatic feature selection in brain-computer interfaces (BCIs). The proposal, which manipulates features generated in the frequency domain by an estimate of the power spectral density of the EEG signals, is based on feature optimization (with both binary and real coding) using a state-of-the-art artificial immune network, the cob-aiNet. In order to analyze the performance of the proposed framework, two approaches are adopted: a direct use of the Davies-Bouldin index and the use of metrics associated with the operation of an extreme learning machine (ELM) in the role of a classifier. The results reveal that the proposal has the potential of improving the performance of a BCI system, and also provide elements for an analysis of the spectral content of EEG signals and of the performance of ELMs in motor imagery paradigms. © 2012 IEEE.
Editor: 
Rights: fechado
Identifier DOI: 10.1109/IJCNN.2012.6252500
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-84865079731&partnerID=40&md5=8e50e4c6faf7d8b968750881af51bb36
Date Issue: 2012
Appears in Collections:Unicamp - Artigos e Outros Documentos

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