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

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 do...

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Autores principales:	Nursuci Putri Husain, Nurseno Bayu Aji
Formato:	article
Lenguaje:	EN ID
Publicado:	P3M Politeknik Negeri Banjarmasin 2019
Materias:	classification electroencephalogram power spectra density principle component analysis multi layer perceptron backpropagation Electrical engineering. Electronics. Nuclear engineering TK1-9971 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/13ac41ae12824cbb98863b1134b668d4
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spelling	oai:doaj.org-article:13ac41ae12824cbb98863b1134b668d42021-12-02T01:08:41ZKlasifikasi Sinyal EEG Dengan Power Spectra Density Berbasis Metode Welch Dan MLP Backpropagation2598-32452598-328810.31961/eltikom.v3i1.99https://doaj.org/article/13ac41ae12824cbb98863b1134b668d42019-05-01T00:00:00Zhttp://eltikom.poliban.ac.id/index.php/eltikom/article/view/99https://doaj.org/toc/2598-3245https://doaj.org/toc/2598-3288Electroencephalogram (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%).Nursuci Putri HusainNurseno Bayu AjiP3M Politeknik Negeri Banjarmasinarticleclassificationelectroencephalogrampower spectra densityprinciple component analysismulti layer perceptron backpropagationElectrical engineering. Electronics. Nuclear engineeringTK1-9971Information technologyT58.5-58.64ENIDJurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer, Vol 3, Iss 1, Pp 17-25 (2019)
institution	DOAJ
collection	DOAJ
language	EN ID
topic	classification electroencephalogram power spectra density principle component analysis multi layer perceptron backpropagation Electrical engineering. Electronics. Nuclear engineering TK1-9971 Information technology T58.5-58.64
spellingShingle	classification electroencephalogram power spectra density principle component analysis multi layer perceptron backpropagation Electrical engineering. Electronics. Nuclear engineering TK1-9971 Information technology T58.5-58.64 Nursuci Putri Husain Nurseno Bayu Aji Klasifikasi Sinyal EEG Dengan Power Spectra Density Berbasis Metode Welch Dan MLP Backpropagation
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%).
format	article
author	Nursuci Putri Husain Nurseno Bayu Aji
author_facet	Nursuci Putri Husain Nurseno Bayu Aji
author_sort	Nursuci Putri Husain
title	Klasifikasi Sinyal EEG Dengan Power Spectra Density Berbasis Metode Welch Dan MLP Backpropagation
title_short	Klasifikasi Sinyal EEG Dengan Power Spectra Density Berbasis Metode Welch Dan MLP Backpropagation
title_full	Klasifikasi Sinyal EEG Dengan Power Spectra Density Berbasis Metode Welch Dan MLP Backpropagation
title_fullStr	Klasifikasi Sinyal EEG Dengan Power Spectra Density Berbasis Metode Welch Dan MLP Backpropagation
title_full_unstemmed	Klasifikasi Sinyal EEG Dengan Power Spectra Density Berbasis Metode Welch Dan MLP Backpropagation
title_sort	klasifikasi sinyal eeg dengan power spectra density berbasis metode welch dan mlp backpropagation
publisher	P3M Politeknik Negeri Banjarmasin
publishDate	2019
url	https://doaj.org/article/13ac41ae12824cbb98863b1134b668d4
work_keys_str_mv	AT nursuciputrihusain klasifikasisinyaleegdenganpowerspectradensityberbasismetodewelchdanmlpbackpropagation AT nursenobayuaji klasifikasisinyaleegdenganpowerspectradensityberbasismetodewelchdanmlpbackpropagation
_version_	1718403239888551936

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

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