EEG sub-band frequency analysis of spectrogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa

This thesis introduces new methods in analyzing Electroencephalogram (EEG) signal by utilizing EEG spectrogram image and image processing texture analysis called Gray level Co-occurrence Matrices (GLCM). The methods attempt to apply in balanced brain and Intelligence Quotient (IQ) applications. The...

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Main Author:	Mustafa, Mahfuzah
Format:	Thesis
Language:	English
Published:	2014
Subjects:	Electronics Pattern recognition systems
Online Access:	http://ir.uitm.edu.my/id/eprint/27796/ http://ir.uitm.edu.my/id/eprint/27796/1/TP_MAHFUZAH%20MUSTAFA%20EE%2014_5.pdf

id	uitm-27796
recordtype	eprints
spelling	uitm-277962020-01-30T08:37:09Z http://ir.uitm.edu.my/id/eprint/27796/ EEG sub-band frequency analysis of spectrogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa Mustafa, Mahfuzah Electronics Pattern recognition systems This thesis introduces new methods in analyzing Electroencephalogram (EEG) signal by utilizing EEG spectrogram image and image processing texture analysis called Gray level Co-occurrence Matrices (GLCM). The methods attempt to apply in balanced brain and Intelligence Quotient (IQ) applications. The relationship between balanced brain and IQ application also proposed in this thesis. Collection of EEG signals were recorded from 101 volunteers. EEG signals recorded for the balanced brain application contain closed eyes state meanwhile for the IQ application contains closed eyes and opened eyes state. Before processing the information from the EEG signals, signal preprocessing is done to remove artefacts and unwanted signal frequencies. A time frequency based technique called EEG spectrogram image was used to generate an image from EEG signal. The spectrogram image was produced for each EEG signals sub-band frequency Delta, Theta, Alpha and Beta. The GLCM texture analysis derives features from EEG spectrogram image. Then, Principal Component Analysis (PCA) was applied to reduce the results and selected principal components features were used as inputs to the classifier. Two classifiers involved in this experiment are K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The number of training and testing ratio is assessed at 70 to 30 and 80 to 20 to find the best model based on percentage of accuracy, sensitivity, specificity as well as Mean Squared Error (MSE). The relationship pattern of balanced brain and IQ application were observed via histogram and then Scatterplot. The strength and significant of the relationship was evaluated by using Pearson correlation test. The percentage of correctness classification for balanced brain application is 90% and MSE 0.1. The sensitivity and specificity of this application is ranging from 66.67% to 100%. The accuracy for IQ application is 94.44% and MSE 0.0752. Meanwhile, the sensitivity and specificity of this application is ranging from 0% to 100%. The relationship between balanced brain and IQ achieved with positive and strong correlation with r ranging between 0.860 to 1.000 and p < 0.05 for some cases. The experiments reported in this thesis showed that the proposed technique were highly successful in indexing the balanced brain level and IQ. 2014 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/27796/1/TP_MAHFUZAH%20MUSTAFA%20EE%2014_5.pdf Mustafa, Mahfuzah (2014) EEG sub-band frequency analysis of spectrogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa. PhD thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Electronics Pattern recognition systems
spellingShingle	Electronics Pattern recognition systems Mustafa, Mahfuzah EEG sub-band frequency analysis of spectrogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa
description	This thesis introduces new methods in analyzing Electroencephalogram (EEG) signal by utilizing EEG spectrogram image and image processing texture analysis called Gray level Co-occurrence Matrices (GLCM). The methods attempt to apply in balanced brain and Intelligence Quotient (IQ) applications. The relationship between balanced brain and IQ application also proposed in this thesis. Collection of EEG signals were recorded from 101 volunteers. EEG signals recorded for the balanced brain application contain closed eyes state meanwhile for the IQ application contains closed eyes and opened eyes state. Before processing the information from the EEG signals, signal preprocessing is done to remove artefacts and unwanted signal frequencies. A time frequency based technique called EEG spectrogram image was used to generate an image from EEG signal. The spectrogram image was produced for each EEG signals sub-band frequency Delta, Theta, Alpha and Beta. The GLCM texture analysis derives features from EEG spectrogram image. Then, Principal Component Analysis (PCA) was applied to reduce the results and selected principal components features were used as inputs to the classifier. Two classifiers involved in this experiment are K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The number of training and testing ratio is assessed at 70 to 30 and 80 to 20 to find the best model based on percentage of accuracy, sensitivity, specificity as well as Mean Squared Error (MSE). The relationship pattern of balanced brain and IQ application were observed via histogram and then Scatterplot. The strength and significant of the relationship was evaluated by using Pearson correlation test. The percentage of correctness classification for balanced brain application is 90% and MSE 0.1. The sensitivity and specificity of this application is ranging from 66.67% to 100%. The accuracy for IQ application is 94.44% and MSE 0.0752. Meanwhile, the sensitivity and specificity of this application is ranging from 0% to 100%. The relationship between balanced brain and IQ achieved with positive and strong correlation with r ranging between 0.860 to 1.000 and p < 0.05 for some cases. The experiments reported in this thesis showed that the proposed technique were highly successful in indexing the balanced brain level and IQ.
format	Thesis
author	Mustafa, Mahfuzah
author_facet	Mustafa, Mahfuzah
author_sort	Mustafa, Mahfuzah
title	EEG sub-band frequency analysis of spectrogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa
title_short	EEG sub-band frequency analysis of spectrogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa
title_full	EEG sub-band frequency analysis of spectrogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa
title_fullStr	EEG sub-band frequency analysis of spectrogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa
title_full_unstemmed	EEG sub-band frequency analysis of spectrogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa
title_sort	eeg sub-band frequency analysis of spectrogram image for balanced brainwave and iq applications / mahfuzah mustafa
publishDate	2014
url	http://ir.uitm.edu.my/id/eprint/27796/ http://ir.uitm.edu.my/id/eprint/27796/1/TP_MAHFUZAH%20MUSTAFA%20EE%2014_5.pdf
first_indexed	2023-09-18T23:19:00Z
last_indexed	2023-09-18T23:19:00Z
_version_	1777419305075343360

EEG sub-band frequency analysis of spectrogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa

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