Artificial neural network modelling for IQ classification based on EEG signals / Aisyah Hartini Jahidin

Electroencephalogram (EEG) is a non-invasive approach for measuring brainwaves applied extensively in cognitive studies. Intelligence, which is commonly gauged as intelligence quotient (IQ) is one of the human potential ability that originates from cognitive functioning of the brain. Recent researc...

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Main Author:	Jahidin, Aisyah Hartini
Format:	Thesis
Language:	English
Published:	2015
Subjects:	Neural networks (Computer science)
Online Access:	http://ir.uitm.edu.my/id/eprint/15601/ http://ir.uitm.edu.my/id/eprint/15601/1/TP_AISYAH%20HARTINI%20JAHIDIN%20EE%2015_5.pdf

id	uitm-15601
recordtype	eprints
spelling	uitm-156012016-12-18T03:48:41Z http://ir.uitm.edu.my/id/eprint/15601/ Artificial neural network modelling for IQ classification based on EEG signals / Aisyah Hartini Jahidin Jahidin, Aisyah Hartini Neural networks (Computer science) Electroencephalogram (EEG) is a non-invasive approach for measuring brainwaves applied extensively in cognitive studies. Intelligence, which is commonly gauged as intelligence quotient (IQ) is one of the human potential ability that originates from cognitive functioning of the brain. Recent researches have shown that correlation exists between EEG and IQ. Furthermore, various advanced studies on the EEG signal are conducted using advanced computation methods. However, a systematic approach for IQ classification based on brainwaves and intelligent modelling technique has yet to be studied. Hence, this thesis proposed a practical and systematic approach to develop IQ classification model via artificial neural network (ANN) based on EEG sub-band features which then, can be related with brain asymmetry (BA) and learning style (LS). The protocols involved EEG recording during resting with eyes closed and answering the conventional psychometric test. Fifty subjects of UiTM students are divided into three IQ levels based on the IQ scores from Raven’s Progressive Matrices as the control group. Power ratio (PR) and spectral centroid (SC) features of Theta, Alpha and Beta are extracted from left prefrontal cortex EEG signals. Then, the distributions of sub-band features are examined for each IQ level. Cross-relational studies are also done between IQ and other cognitive abilities, which are brain asymmetry and learning style based on EEG features. Further, IQ classification models comprising of inputs based on PR and SC features (Model A and Model B) are developed using multilayer feedforward network. Findings from this research showed that sub-band PR and SC features are indeed correlated with IQ. Consequently, the network models yielded low mean squared error (MSE) and fulfilled the correlation requirements in classifying IQ levels. In cross-relational studies, findings have also revealed that PR and SC in relaxed closed-eyes state reflect the relationship between intelligence and other cognitive abilities. The results showed that different balanced states of the brain and learning styles can be mapped to distinct IQ levels using the developed models. Results also demonstrated that high IQ is obtained when subjects maintain relatively balanced hemispheric control. Additionally, results also revealed that medium and high IQ levels are capable of utilising four learning styles compared to low IQ level where only three learning styles were utilised. In conclusion, this research has proven that IQ level classification via EEG and ANN modelling is successful specifically the PR and SC features at resting EEG that can be considered as a stable biological marker in relation to cognitive performance. In addition, the study also confirmed that left hemisphere of the frontal region is adequate for IQ recognition. 2015 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/15601/1/TP_AISYAH%20HARTINI%20JAHIDIN%20EE%2015_5.pdf Jahidin, Aisyah Hartini (2015) Artificial neural network modelling for IQ classification based on EEG signals / Aisyah Hartini Jahidin. 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	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Jahidin, Aisyah Hartini Artificial neural network modelling for IQ classification based on EEG signals / Aisyah Hartini Jahidin
description	Electroencephalogram (EEG) is a non-invasive approach for measuring brainwaves applied extensively in cognitive studies. Intelligence, which is commonly gauged as intelligence quotient (IQ) is one of the human potential ability that originates from cognitive functioning of the brain. Recent researches have shown that correlation exists between EEG and IQ. Furthermore, various advanced studies on the EEG signal are conducted using advanced computation methods. However, a systematic approach for IQ classification based on brainwaves and intelligent modelling technique has yet to be studied. Hence, this thesis proposed a practical and systematic approach to develop IQ classification model via artificial neural network (ANN) based on EEG sub-band features which then, can be related with brain asymmetry (BA) and learning style (LS). The protocols involved EEG recording during resting with eyes closed and answering the conventional psychometric test. Fifty subjects of UiTM students are divided into three IQ levels based on the IQ scores from Raven’s Progressive Matrices as the control group. Power ratio (PR) and spectral centroid (SC) features of Theta, Alpha and Beta are extracted from left prefrontal cortex EEG signals. Then, the distributions of sub-band features are examined for each IQ level. Cross-relational studies are also done between IQ and other cognitive abilities, which are brain asymmetry and learning style based on EEG features. Further, IQ classification models comprising of inputs based on PR and SC features (Model A and Model B) are developed using multilayer feedforward network. Findings from this research showed that sub-band PR and SC features are indeed correlated with IQ. Consequently, the network models yielded low mean squared error (MSE) and fulfilled the correlation requirements in classifying IQ levels. In cross-relational studies, findings have also revealed that PR and SC in relaxed closed-eyes state reflect the relationship between intelligence and other cognitive abilities. The results showed that different balanced states of the brain and learning styles can be mapped to distinct IQ levels using the developed models. Results also demonstrated that high IQ is obtained when subjects maintain relatively balanced hemispheric control. Additionally, results also revealed that medium and high IQ levels are capable of utilising four learning styles compared to low IQ level where only three learning styles were utilised. In conclusion, this research has proven that IQ level classification via EEG and ANN modelling is successful specifically the PR and SC features at resting EEG that can be considered as a stable biological marker in relation to cognitive performance. In addition, the study also confirmed that left hemisphere of the frontal region is adequate for IQ recognition.
format	Thesis
author	Jahidin, Aisyah Hartini
author_facet	Jahidin, Aisyah Hartini
author_sort	Jahidin, Aisyah Hartini
title	Artificial neural network modelling for IQ classification based on EEG signals / Aisyah Hartini Jahidin
title_short	Artificial neural network modelling for IQ classification based on EEG signals / Aisyah Hartini Jahidin
title_full	Artificial neural network modelling for IQ classification based on EEG signals / Aisyah Hartini Jahidin
title_fullStr	Artificial neural network modelling for IQ classification based on EEG signals / Aisyah Hartini Jahidin
title_full_unstemmed	Artificial neural network modelling for IQ classification based on EEG signals / Aisyah Hartini Jahidin
title_sort	artificial neural network modelling for iq classification based on eeg signals / aisyah hartini jahidin
publishDate	2015
url	http://ir.uitm.edu.my/id/eprint/15601/ http://ir.uitm.edu.my/id/eprint/15601/1/TP_AISYAH%20HARTINI%20JAHIDIN%20EE%2015_5.pdf
first_indexed	2023-09-18T22:54:13Z
last_indexed	2023-09-18T22:54:13Z
_version_	1777417745686593536

Artificial neural network modelling for IQ classification based on EEG signals / Aisyah Hartini Jahidin

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