Klasifikasi Sinyal EEG Dengan Power Spectra Density Berbasis Metode Welch Dan MLP Backpropagation


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Title Klasifikasi Sinyal EEG Dengan Power Spectra Density Berbasis Metode Welch Dan MLP Backpropagation
Creator Husain, Nursuci Putri
Aji, Nurseno Bayu
Description Electroencephalogram (EEG) signal is a signal that could become an information for study about disorders of brain function  such as Epilepsi. EEG that detected in epileptic seizures produce patterns that allow doctors to distinguish it from normal conditions. However, a visual analysis can not be done continuously. This study proposed a new hybrid method of EEG signal classification using Power Spectral Density (PSD) based on Welch method, Principle Component Analysis (PCA), and Multi Layer Perceptron Backpropagation.There are 3 main stages in this study, firstly preprocessing the dataset of EEG signals by Power Spectral Density (PSD) based on Welch method, then Principle Component Analysis (PCA) as a method of  dimensionallity reduction of the EEG signal data and the Multi Layer Perceptron Backpropagation for classifying a signal. Based on experimental results, the proposed method is successfully obtain high accuracy for the 80-20% training-testing partition (99.68%).
Publisher P3M Politeknik Negeri Banjarmasin
Date 2019-05-10
Type info:eu-repo/semantics/article
Peer-reviewed Article
Format application/pdf
Identifier http://eltikom.poliban.ac.id/index.php/eltikom/article/view/99
Source Jurnal ELTIKOM; Vol 3 No 1 (2019); 17-25
Language eng
Relation http://eltikom.poliban.ac.id/index.php/eltikom/article/view/99/32
Rights Copyright (c) 2019 Jurnal ELTIKOM

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