Investigation of the most appropriate mother wavelet for characterizing imaginary EEG signals used in BCI systems

Authors : ÖNDER AYDEMİR, TEMEL KAYIKÇIOĞLU

Pages : 38-49

View : 8 | Download : 5

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :Feature extraction is a very challenging task, since choosing discriminative features directly affects the recognition rate of the brain--computer interface insert ignore into journalissuearticles values(BCI); system. The objective of this paper is to investigate the effect of mother wavelets insert ignore into journalissuearticles values(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.
Keywords : Continuous wavelet transform, brain computer interface, feature extraction