Feature selection using a genetic algorithm in a motor imagery-based Brain Computer Interface

This study performed an analysis of several feature extraction methods and a genetic algorithm applied to a motor imagery-based Brain Computer Interface (BCI) system. Several features can be extracted from EEG signals to be used for classification in BCIs. However, it is necessary to select a small group of relevant features because the use of irrelevant features deteriorates the performance of the classifier. This study proposes a genetic algorithm (GA) as feature selection method. It was applied to the dataset IIb of the BCI Competition IV achieving a kappa coefficient of 0.613. The use of a GA improves the classification results using extracted features separately (kappa coefficient of 0.336) and the winner competition results (kappa coefficient of 0.600). These preliminary results demonstrated that the proposed methodology could be useful to control motor imagery-based BCI applications.