Research Repository

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection

Ortega, Julio and Asensio-Cubero, Javier and Gan, John Q and Ortiz, Andrés (2016) 'Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection.' BioMedical Engineering OnLine, 15 (S1). ISSN 1475-925X

art%3A10.1186%2Fs12938-016-0178-x.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview


Background: Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. Methods: This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. Results and conclusion: The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed.

Item Type: Article
Uncontrolled Keywords: Brain-computer interfaces (BCI), Feature selection, EEG classification, Imagery tasks classification, Multiobjective optimization, Multiresolution analysis (MRA)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 13 Dec 2016 09:45
Last Modified: 06 Jul 2018 13:15

Actions (login required)

View Item View Item