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A Study via Feature Selection on the Separability of Approximate Entropy for Brain-Computer Interfaces

Hasan, BAS and Dyson, M and Balli, T and Gan, JQ (2008) A Study via Feature Selection on the Separability of Approximate Entropy for Brain-Computer Interfaces. In: UNSPECIFIED, ? - ?.

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Abstract

The linear and nonlinear separability of approximate entropy feature for EEG-based brain-computer interfaces (BCI) are tested and compared to that of two alternative features, band power and reflection coefficients. Separability is analyzed using hybrid sequential forward floating search, in which a classifier: linear discriminate analysis (LDA) classifier or nonlinear support vector machine (SVM), and a separability index: Davies-Bouldin index (DBI) or mutual information (MI) based index, are jointly utilized to evaluate selected feature subsets. Results on BCI data demonstrate the separability of the approximate entropy feature to be comparable to that of the band power and reflection coefficients, although each feature has advantages on different situations.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Published proceedings: _not provided_
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Users 161 not found.
Date Deposited: 01 Jul 2013 13:29
Last Modified: 15 Apr 2018 09:15
URI: http://repository.essex.ac.uk/id/eprint/4181

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