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

H Hindarto | Zendy; Arif Muntasa | Zendy; Ade Efiyanti | Zendy

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

Author(s) -

H Hindarto,

Arif Muntasa,

Ade Efiyanti

Publication year - 2020

Publication title -

iop conference series materials science and engineering

Language(s) - English

Resource type - Journals

eISSN - 1757-899X

pISSN - 1757-8981

DOI - 10.1088/1757-899x/821/1/012036

Subject(s) - wavelet , pattern recognition (psychology) , standard deviation , cursor (databases) , artificial intelligence , electroencephalography , wavelet transform , k nearest neighbors algorithm , computer science , signal (programming language) , speech recognition , mathematics , statistics , psychology , psychiatry , programming language

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-NN method.

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