Comput Stat (2013) 28:5–18 DOI 10.1007/s00180-011-0292-y ORIGINAL PAPER On the discovery of events in EEG data utilizing information fusion Martin Schels · Stefan Scherer · Michael Glodek · Hans A. Kestler · Günther Palm · Friedhelm Schwenker Received: 18 October 2010 / Accepted: 2 November 2011 / Published online: 29 November 2011 © Springer-Verlag 2011 Abstract 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 bioelec- trical 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 par- titions 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 pro- posed approach utilizing information fusion shows promising results (over 0.8 area under the ROC curve). Keywords Multiple classifier systems · EEG data · P300 event M. Schels · S. Scherer · M. Glodek · H. A. Kestler · G. Palm · F. Schwenker (B ) Institute of Neural Information Processing, University of Ulm, Ulm, Germany e-mail: friedhelm.schwenker@uni-ulm.de M. Schels e-mail: martin.schels@uni-ulm.de M. Glodek e-mail: michael.glodek@uni-ulm.de G. Palm e-mail: guenther.palm@uni-ulm.de S. Scherer Speech Communication Laboratory, Trinity College Dublin, Dublin, Ireland e-mail: scherers@tcd.ie H. A. Kestler Internal Medicine I, University Hospital, Ulm, Germany e-mail: hans.kestler@uni-ulm.de 123