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electro-encephalogram--eeg--signals
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Combination of multiple classifiers with fuzzy integral method for classifying the EEG signals in brain-computer interface
, Article ICBPE 2006 - 2006 International Conference on Biomedical and Pharmaceutical Engineering, Singapore, 11 December 2006 through 14 December 2006 ; 2006 , Pages 157-161 ; 8190426249 (ISBN); 9788190426244 (ISBN) ; Esmaeeli, M ; Shouraki, S. B ; Sharif University of Technology
2006
Abstract
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...