On the discovery of events in EEG data utilizing information fusion

One way to tackle brain computer interfaces is to consider event related potentials in electroencephalography, like the well established P300 phenomenon. In this paper a multiple classifier approach to discover these events in the bioelectrical signal and with them whether or not a subject has recognized a particular pattern, is employed. Dealing with noisy data as well as heavily imbalanced target class distributions are among the difficulties encountered. Our approach utilizes partitions of electrodes to create robust and meaningful individual classifiers, which are then subsequently combined using decision fusion. Furthermore, a classifier selection approach using genetic algorithms is evaluated and used for optimization. The proposed approach utilizing information fusion shows promising results (over 0.8 area under the ROC curve).