“School of Cognitive%20Sciences”
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| Paper IPM / Cognitive%20Sciences / 13149 |
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| Abstract: | |||||||||||
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In this paper, we demonstrate the use of a
multiple classifier system for classification of electroencephalogram (EEG) signals. The main purpose of this
paper is to apply several approaches to classify motor
imageries originating from the brain in a more robust
manner. For this study, dataset II from BCI competition III
was used. To extract features from the brain signal, discrete wavelet transform decomposition was used. Then, several classic classifiers were implemented to be utilized in the multiple classifier system, which outperforms the reported results of other proposed methods on the dataset. Also, a variety of classifier combination methods along with
genetic algorithm feature selection were evaluated and
compared in order to diminish classification error. Our
results suggest that an ensemble system can be employed to
boost EEG classification accuracy.
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