Eeg sub-band frequency analysis of spectrogram image for balanced brainwave and iq applications

Eeg sub-band frequency analysis of spectrogram image for balanced brainwave and iq applications

This thesis introduces new methods in analyzing 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...

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Main Author: Mahfuzah, Mustafa
Format: Thesis
Language:English
Published: 2014
Subjects:
QP Physiology
Online Access:http://umpir.ump.edu.my/id/eprint/12083/
http://umpir.ump.edu.my/id/eprint/12083/
http://umpir.ump.edu.my/id/eprint/12083/1/MAHFUZAH%20BINTI%20MUSTAFA.PDF
id ump-12083
recordtype eprints
spelling ump-120832017-10-25T02:44:12Z http://umpir.ump.edu.my/id/eprint/12083/ Eeg sub-band frequency analysis of spectrogram image for balanced brainwave and iq applications Mahfuzah, Mustafa QP Physiology This thesis introduces new methods in analyzing 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 BEG signals were recorded from 101 volunteers. BEG 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. 2014-10 Thesis NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/12083/1/MAHFUZAH%20BINTI%20MUSTAFA.PDF Mahfuzah, Mustafa (2014) Eeg sub-band frequency analysis of spectrogram image for balanced brainwave and iq applications. PhD thesis, Universiti Teknologi Mara. http://iportal.ump.edu.my/lib/item?id=chamo:87614&theme=UMP2
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QP Physiology
spellingShingle QP Physiology
Mahfuzah, Mustafa
Eeg sub-band frequency analysis of spectrogram image for balanced brainwave and iq applications
description This thesis introduces new methods in analyzing 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 BEG signals were recorded from 101 volunteers. BEG 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 Mahfuzah, Mustafa
author_facet Mahfuzah, Mustafa
author_sort Mahfuzah, Mustafa
title Eeg sub-band frequency analysis of spectrogram image for balanced brainwave and iq applications
title_short Eeg sub-band frequency analysis of spectrogram image for balanced brainwave and iq applications
title_full Eeg sub-band frequency analysis of spectrogram image for balanced brainwave and iq applications
title_fullStr Eeg sub-band frequency analysis of spectrogram image for balanced brainwave and iq applications
title_full_unstemmed Eeg sub-band frequency analysis of spectrogram image for balanced brainwave and iq applications
title_sort eeg sub-band frequency analysis of spectrogram image for balanced brainwave and iq applications
publishDate 2014
url http://umpir.ump.edu.my/id/eprint/12083/
http://umpir.ump.edu.my/id/eprint/12083/
http://umpir.ump.edu.my/id/eprint/12083/1/MAHFUZAH%20BINTI%20MUSTAFA.PDF
first_indexed 2023-09-18T22:13:20Z
last_indexed 2023-09-18T22:13:20Z
_version_ 1777415173201461248

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