Combination Of Multiple Classifiers With Fuzzy Integral Method for Classifying 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 me...

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Bibliographic Details
Published in2006 International Conference on Biomedical and Pharmaceutical Engineering pp. 157 - 161
Main Authors Shouraki, S.B., Esmaeeli, M., Shoaie, Z.
Format Conference Proceeding
LanguageEnglish
Published 2006
Subjects
Brain computer interfaces
Computer interfaces
Data mining
Electroencephalography
Feature extraction
Linear discriminant analysis
Machine learning
Switches
Training data
Voting
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Summary: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.
ISBN:9789810579
ISSN:1947-1386
1947-1394