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.

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

EEG signal ، classification ، combination of multiple classifiers ، feature extraction ، majority voting ، fuzzy measure and integral