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

Jahidin, Aishah Hartini (2016) Artificial neural network modelling for IQ classification based on EEG signals / Aishah Hartini Jahidin. In: The Doctoral Research Abstracts. IGS Biannual Publication, 9 (9). Institute of Graduate Studies, UiTM, Shah Alam.

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Abstract

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…

Item Type: Book Section
Creators:
CreatorsEmail
Jahidin, Aishah HartiniUNSPECIFIED
Subjects: L Education > LB Theory and practice of education > Higher Education > Dissertations, Academic. Preparation of theses > Malaysia
Divisions: Institut Pengajian Siswazah (IPSis) : Institute of Graduate Studies (IGS)
Series Name: IGS Biannual Publication
Volume: 9
Number: 9
Item ID: 19605
Uncontrolled Keywords: Abstract; Abstract of thesis; Newsletter; Research information; Doctoral graduates; IPSis; IGS; UiTM; EEG signals
Last Modified: 07 Jun 2018 02:24
Depositing User: Staf Pendigitalan 7
URI: http://ir.uitm.edu.my/id/eprint/19605

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