Classification of EEG signals uses the coefficient of wavelet transform and K-nearest neighbor

ade, efiyanti Classification of EEG signals uses the coefficient of wavelet transform and K-nearest neighbor. Classification of EEG signals uses the coefficient of wavelet transform and K-nearest neighbor H.

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Abstract

The research that was built was used to explain the application of Electroencephalography (EEG) signal waves. EEG data is used to move the cursor up and the cursor down. Characterization of each EEG signal uses the Wavelet method taken at each the subband of the wavelet process. Wavelet process by taking the average value and the standard deviation value of the wavelet coefficient. The average value and the standard deviation value is used as an EEG feature. K-Nearest Neighbor is used as an identification whether the cursor will move up or vice versa. This study uses of 100 EEG signal data consisting of 50 test data and 50 testing data. The accuracy of identification uses the 80% K-

Item Type:	Article
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Engineering > School of Computer Engineering
Depositing User:	Mrs ade eviyanti
Date Deposited:	16 Apr 2021 06:31
Last Modified:	21 Apr 2021 04:23
URI:	http://eprints.umsida.ac.id/id/eprint/8397

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