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Investigation of the most appropriate mother wavelet for characterizing imaginary EEG signals used in BCI systems
2016
Journal:  
Turkish Journal of Electrical Engineering and Computer Science
Author:  
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.

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Turkish Journal of Electrical Engineering and Computer Science

Field :   Mühendislik

Journal Type :   Uluslararası

Metrics
Article : 2.879
Cite : 1.406
2023 Impact : 0.016
Details
Turkish Journal of Electrical Engineering and Computer Science