Ensemble of classifiers applied to motor imagery task classification for BCI applications

Brain Computer Interfaces allow the interaction between a person and their environment using signals extracted directly from the brain. One of the most common non-invasive methods of brain signal acquisition is the electroencephalography (EEG). An EEG based BCI system generally involves four steps: preprocessing, feature extraction, feature selection, and classification. In order to design a real applicable BCI system, it is important to provide: good classification performance, adequate computational cost, robustness to variations of the signal between trials and between subjects, and a classifiers model that cope with highly dimensional data. Ensemble of classifiers is a learning model that, with a proper design, can satisfy those conditions, which make them a good match for a BCI application. In this paper, some ensemble of classifiers designs are evaluated and compared with other BCI approaches on three different subjects. In the proposed model, Genetic Algorithm is employed as feature selection method and Wavelet Packet Decomposition as preprocessing procedure. Four fusion methods were applied in the ensembles design, including: Majority Voting, Weighted Majority Voting, Genetic Algorithm for classifier selection and Genetic Algorithm to compute the weights for the Weighted Majority fusion method. The best results were obtained with the Weighted Majority Voting fusion method based on Genetic Algorithm.