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Turkish Journal of Electrical Engineering and Computer Sciences

DOI

10.3906/elk-1307-17

Abstract

Feature extraction is a very challenging task, since choosing discriminative features directly affects the recognition rate of the brain--computer interface (BCI) system. The objective of this paper is to investigate the effect of mother wavelets (MWs) on classification results. To this end, features were extracted from 3 different datasets using 12 MWs, and then the signals were classified using 3 classification algorithms, including k-nearest neighbor, support vector machine, and linear discriminant analysis. The experiments proved that Daubechies and Shannon were the most suitable wavelet families for extracting more discriminative features from imaginary EEG/ECoG signals.

First Page

38

Last Page

49

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