Classification of EEG spectrogram image with ANN approach for brainwave balancing application

In this paper, an Artificial Neural Network (ANN) algorithm for classifying the EEG spectrogram images in brainwave is presented. Gray Level Co-occurrence Matrix (GLCM) texture feature from the EEG spectrogram images have been used as input to the system. The GLCM texture feature produced large dime...

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Main Authors:	Mahfuzah, Mustafa, Mohd Nasir, Taib, Zunairah, Murat, Norizam, Sulaiman, Siti Armiza, Mohd Aris
Format:	Article
Language:	English
Published:	2011
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/8402/ http://umpir.ump.edu.my/id/eprint/8402/ http://umpir.ump.edu.my/id/eprint/8402/ http://umpir.ump.edu.my/id/eprint/8402/1/Classification_of_EEG_Spectrogram_Image_with_ANN_approach_for_Brainwave_Balancing_Application.pdf

id	ump-8402
recordtype	eprints
spelling	ump-84022018-08-29T02:27:05Z http://umpir.ump.edu.my/id/eprint/8402/ Classification of EEG spectrogram image with ANN approach for brainwave balancing application Mahfuzah, Mustafa Mohd Nasir, Taib Zunairah, Murat Norizam, Sulaiman Siti Armiza, Mohd Aris TK Electrical engineering. Electronics Nuclear engineering In this paper, an Artificial Neural Network (ANN) algorithm for classifying the EEG spectrogram images in brainwave is presented. Gray Level Co-occurrence Matrix (GLCM) texture feature from the EEG spectrogram images have been used as input to the system. The GLCM texture feature produced large dimension of feature, therefore the Principal Component Analysis(PCA) is used to reduce the feature dimension. The result shows that the proposed model is able to classify EEG spectrogram images with 77% to 84% accuracy for three classes of brainwave balancing application with an optimized ANN model in training by varying the neurons in the hidden layer, epoch, momentum rate and learning rate. 2011 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/8402/1/Classification_of_EEG_Spectrogram_Image_with_ANN_approach_for_Brainwave_Balancing_Application.pdf Mahfuzah, Mustafa and Mohd Nasir, Taib and Zunairah, Murat and Norizam, Sulaiman and Siti Armiza, Mohd Aris (2011) Classification of EEG spectrogram image with ANN approach for brainwave balancing application. Classification of EEG Spectrogram Image. pp. 30-37. ISSN 1473-804x(Online); 1473-8031(print) http://dx.doi.org/10.5013/IJSSST.a.12.05.05 DOI: 10.5013/IJSSST.a.12.05.05
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Mahfuzah, Mustafa Mohd Nasir, Taib Zunairah, Murat Norizam, Sulaiman Siti Armiza, Mohd Aris Classification of EEG spectrogram image with ANN approach for brainwave balancing application
description	In this paper, an Artificial Neural Network (ANN) algorithm for classifying the EEG spectrogram images in brainwave is presented. Gray Level Co-occurrence Matrix (GLCM) texture feature from the EEG spectrogram images have been used as input to the system. The GLCM texture feature produced large dimension of feature, therefore the Principal Component Analysis(PCA) is used to reduce the feature dimension. The result shows that the proposed model is able to classify EEG spectrogram images with 77% to 84% accuracy for three classes of brainwave balancing application with an optimized ANN model in training by varying the neurons in the hidden layer, epoch, momentum rate and learning rate.
format	Article
author	Mahfuzah, Mustafa Mohd Nasir, Taib Zunairah, Murat Norizam, Sulaiman Siti Armiza, Mohd Aris
author_facet	Mahfuzah, Mustafa Mohd Nasir, Taib Zunairah, Murat Norizam, Sulaiman Siti Armiza, Mohd Aris
author_sort	Mahfuzah, Mustafa
title	Classification of EEG spectrogram image with ANN approach for brainwave balancing application
title_short	Classification of EEG spectrogram image with ANN approach for brainwave balancing application
title_full	Classification of EEG spectrogram image with ANN approach for brainwave balancing application
title_fullStr	Classification of EEG spectrogram image with ANN approach for brainwave balancing application
title_full_unstemmed	Classification of EEG spectrogram image with ANN approach for brainwave balancing application
title_sort	classification of eeg spectrogram image with ann approach for brainwave balancing application
publishDate	2011
url	http://umpir.ump.edu.my/id/eprint/8402/ http://umpir.ump.edu.my/id/eprint/8402/ http://umpir.ump.edu.my/id/eprint/8402/ http://umpir.ump.edu.my/id/eprint/8402/1/Classification_of_EEG_Spectrogram_Image_with_ANN_approach_for_Brainwave_Balancing_Application.pdf
first_indexed	2023-09-18T22:05:56Z
last_indexed	2023-09-18T22:05:56Z
_version_	1777414708030078976

Classification of EEG spectrogram image with ANN approach for brainwave balancing application

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