Turkish Journal of Electrical Engineering and Computer Sciences
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.
DOI
10.3906/elk-1307-17
Keywords
Continuous wavelet transform, brain computer interface, feature extraction
First Page
38
Last Page
49
Recommended Citation
AYDEMİR, Ö, & KAYIKÇIOĞLU, T (2016). Investigation of the most appropriate mother wavelet for characterizing imaginary EEG signals used in BCI systems. Turkish Journal of Electrical Engineering and Computer Sciences 24 (1): 38-49. https://doi.org/10.3906/elk-1307-17
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