COMBINATION OF MULTIPLE CLASSIFIERS WITH FUZZY INTEGRAL METHOD FOR CLASSIFING THE EEG SIGNALS IN BRAIN-COMPUTER INTERFACE

In this paper we study the effectiveness of using multiple classifier combination for EEG signal classification aiming to obtain more accurate results  than it possible from each of the constituent classifiers. The developed system employs two linear classifiers (SVM,LDA) fused at the abstract and   measurement levels for integrating information to reach a collective decision. For making decision, the majority voting scheme has been used. While  at the measurement level, two types of combination methods have been investigated: one used fixed combination rules that don’t require prior  training and a trainable combination method. For the second type, the fuzzy integral method was used. The ensemble classification task is completed by feeding the classifiers with five different features extracted from the EEG signal for imagination of right and left hands movements (i.e., at EEG  channels C3 and C4). The results show that using classifier fusion methods improved the overall classification performance.

Maryam Esmailee,Department of computer engineering University of Amirkabir

Zahra Shoaie, Department of Computer engineering, University of Sharif, Tehran

Dr. Mohammad Rahmati, Department of Computer engineering, University of Amirkabir

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