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 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.