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...
Main Authors: | , |
---|---|
Format: | Article |
Language: | Indonesian |
Published: |
P3M Politeknik Negeri Banjarmasin
2019-05-01
|
Series: | Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer |
Subjects: | |
Online Access: | http://eltikom.poliban.ac.id/index.php/eltikom/article/view/99 |
id |
doaj-13ac41ae12824cbb98863b1134b668d4 |
---|---|
record_format |
Article |
spelling |
doaj-13ac41ae12824cbb98863b1134b668d42020-11-24T21:23:01ZindP3M Politeknik Negeri BanjarmasinJurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer2598-32452598-32882019-05-0131172510.31961/eltikom.v3i1.9999Klasifikasi Sinyal EEG Dengan Power Spectra Density Berbasis Metode Welch Dan MLP BackpropagationNursuci Putri Husain0Nurseno Bayu Aji1Universitas Islam MakassarUniversitas Gajayana MalangElectroencephalogram (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%).http://eltikom.poliban.ac.id/index.php/eltikom/article/view/99classificationelectroencephalogrampower spectra densityprinciple component analysismulti layer perceptron backpropagation |
collection |
DOAJ |
language |
Indonesian |
format |
Article |
sources |
DOAJ |
author |
Nursuci Putri Husain Nurseno Bayu Aji |
spellingShingle |
Nursuci Putri Husain Nurseno Bayu Aji Klasifikasi Sinyal EEG Dengan Power Spectra Density Berbasis Metode Welch Dan MLP Backpropagation Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer classification electroencephalogram power spectra density principle component analysis multi layer perceptron backpropagation |
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 |
series |
Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer |
issn |
2598-3245 2598-3288 |
publishDate |
2019-05-01 |
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%). |
topic |
classification electroencephalogram power spectra density principle component analysis multi layer perceptron backpropagation |
url |
http://eltikom.poliban.ac.id/index.php/eltikom/article/view/99 |
work_keys_str_mv |
AT nursuciputrihusain klasifikasisinyaleegdenganpowerspectradensityberbasismetodewelchdanmlpbackpropagation AT nursenobayuaji klasifikasisinyaleegdenganpowerspectradensityberbasismetodewelchdanmlpbackpropagation |
_version_ |
1716727044561174528 |