A supervised filter method for multi-objective feature selection in EEG classification based on multi-resolution analysis for BCI

This paper proposes a supervised filter method for evolutionary multi-objective feature selection for classification problems in high-dimensional feature space, which is evaluated by comparison with wrapper approaches for the same application. The filter method based on a set of label-aided utility...

Full description

Saved in:
Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 250; pp. 45 - 56
Main Authors: Martín-Smith, Pedro, Ortega, Julio, Asensio-Cubero, Javier, Gan, John Q., Ortiz, Andrés
Format: Journal Article
Language:English
Published: Elsevier B.V 09.08.2017
Subjects:
Brain–computer interfaces (BCI)
Feature selection
Filter methods
Multi-objective optimization
Multi-resolution analysis (MRA)
Wrapper-based feature selection
Filter methods
Brain–computer interfaces (BCI)
Feature selection
Multi-objective optimization
Multi-resolution analysis (MRA)
Wrapper-based feature selection
ISSN:0925-2312, 1872-8286
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper proposes a supervised filter method for evolutionary multi-objective feature selection for classification problems in high-dimensional feature space, which is evaluated by comparison with wrapper approaches for the same application. The filter method based on a set of label-aided utility functions is compared with wrapper approaches using the accuracy and generalization properties in the effective searching of the most adequate subset of features through an evolutionary multi-objective optimization scheme. The target application corresponds to a brain–computer interface (BCI) classification task based on linear discriminant analysis (LDA) classifiers, where the properties of multi-resolution analysis (MRA) for signal analysis in temporal and spectral domains have been used to extract features from electroencephalogram (EEG) signals. The results, corresponding to a dataset obtained from the databases of the BCI Laboratory of the University of Essex, UK, including ten subjects with three different imagery movements, have allowed us to evaluate the advantages and drawbacks of the different approaches with respect to time consumption, accuracy and generalization capabilities.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2016.09.123