title: Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Computer Architecture and Technology, CITIC, University of Granada, Granada, Spain. jortega@ugr.es.
authors.forename: Julio
authors.initials: J
authors.lastname: Ortega
authors.affiliation: Neuralcubes Ltd, London, UK.
authors.forename: Javier
authors.initials: J
authors.lastname: Asensio-Cubero
authors.affiliation: School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK.
authors.forename: John Q
authors.initials: JQ
authors.lastname: Gan
authors.affiliation: Department of Communications Engineering, University of Malaga, Málaga, Spain.
authors.forename: Andrés
authors.initials: A
authors.lastname: Ortiz
mesh_terms: D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D006801:Humans; D019018:Imagery, Psychotherapy / Q000379:methods*; D009043:Motor Activity; D012815:Signal Processing, Computer-Assisted; D000069553:Supervised Machine Learning
keywords: Brain-computer interfaces (BCI); EEG classification; Feature selection; Imagery tasks classification; Multiobjective optimization; Multiresolution analysis (MRA)