Combination of classifiers using the fuzzy integral for uncertainty identification and subject specific optimization - application to brain-computer interface

In this paper we propose a framework for combination of classifiers using fuzzy measures and integrals that aims at providing researchers and practitioners with a simple and structured approach to deal with two issues that often arise in many pattern recognition applications: (i) the need for an automatic and user-specific selection of the best performing classifier or, better, ensemble of classifiers, out of the available ones; (ii) the need for uncertainty identification which should result in an abstention rather than an unreliable decision. We evaluate the framework within the context of Brain-Computer Interface, a field in which abstention and intersubject variability have a remarkable impact. Analysis of experimental data relative to five subjects shows that the proposed system is able to answer such needs