A Label-Aided Filter Method for Multi-objective Feature Selection in EEG Classification for BCI

This paper proposes and evaluates a filter approach for evolutionary multi-objective feature selection in classification problems with a large number of features. Such classification problems frequently appear in many bioinformatics applications where the number of patterns is smaller than the number of features and thus the curse of dimensionality problem exists. The main contribution of this paper is proposing a set of label-aided utility functions that allows the effective search of the most adequate subset of features through an evolutionary multi-objective optimization scheme. The experimental results have been obtained in a brain-computer interface (BCI) classification task based on LDA classifiers, where the properties of multi-resolution analysis (MRA) for signal analysis in temporal and spectral domains have been used to extract the features from EEG signals. The results from the proposed filter method demonstrate some advantages such as less time consumption and better generalization capabilities with respect to some wrapper-based multi-objective feature selection alternatives.