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

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

Full description

Saved in:

Bibliographic Details
Main Author:	Mustafa, Mahfuzah
Format:	Thesis
Language:	English
Published:	2014
Subjects:	Spectrograph Spectrometer Spectroscopy
Online Access:	https://ir.uitm.edu.my/id/eprint/40047/1/40047.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.40047
record_format	uketd_dc
spelling	my-uitm-ir.400472021-08-25T08:51:33Z EEG sub-band frequency analysis of spectogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa 2014-10 Mustafa, Mahfuzah Spectrograph. Spectrometer Spectroscopy This thesis introduces new methods in analysing Electroencephalogram (EEG) signal by utilizing EEG spectrogram image and image processing texture analysis called Graylevel 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 pre-processing 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%o 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 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. 2014-10 Thesis https://ir.uitm.edu.my/id/eprint/40047/ https://ir.uitm.edu.my/id/eprint/40047/1/40047.pdf text en public phd doctoral Universiti Teknologi MARA Faculty of Electrical Engineering
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Spectrograph Spectrometer Spectroscopy
spellingShingle	Spectrograph Spectrometer Spectroscopy Mustafa, Mahfuzah EEG sub-band frequency analysis of spectogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa
description	This thesis introduces new methods in analysing Electroencephalogram (EEG) signal by utilizing EEG spectrogram image and image processing texture analysis called Graylevel 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 pre-processing 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%o 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 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
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mustafa, Mahfuzah
author_facet	Mustafa, Mahfuzah
author_sort	Mustafa, Mahfuzah
title	EEG sub-band frequency analysis of spectogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa
title_short	EEG sub-band frequency analysis of spectogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa
title_full	EEG sub-band frequency analysis of spectogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa
title_fullStr	EEG sub-band frequency analysis of spectogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa
title_full_unstemmed	EEG sub-band frequency analysis of spectogram image for balanced brainwave and IQ applications / Mahfuzah Mustafa
title_sort	eeg sub-band frequency analysis of spectogram image for balanced brainwave and iq applications / mahfuzah mustafa
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Electrical Engineering
publishDate	2014
url	https://ir.uitm.edu.my/id/eprint/40047/1/40047.pdf
_version_	1783734556948430848

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

Similar Items