Artificial neural network modelling for IQ classification based on EEG signals / Aishah 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 research...
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Institute of Graduate Studies, UiTM
2016
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| Online Access: | http://ir.uitm.edu.my/id/eprint/19605/ http://ir.uitm.edu.my/id/eprint/19605/1/ABS_AISHAH%20HARTINI%20JAHIDIN%20TDRA%20VOL%209%20IGS%2016.pdf |
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uitm-196052018-06-07T02:24:44Z http://ir.uitm.edu.my/id/eprint/19605/ Artificial neural network modelling for IQ classification based on EEG signals / Aishah Hartini Jahidin Jahidin, Aishah Hartini Malaysia 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… Institute of Graduate Studies, UiTM 2016 Book Section PeerReviewed text en http://ir.uitm.edu.my/id/eprint/19605/1/ABS_AISHAH%20HARTINI%20JAHIDIN%20TDRA%20VOL%209%20IGS%2016.pdf 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. |
| repository_type |
Digital Repository |
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Local University |
| institution |
Universiti Teknologi MARA |
| building |
UiTM Institutional Repository |
| collection |
Online Access |
| language |
English |
| topic |
Malaysia |
| spellingShingle |
Malaysia Jahidin, Aishah Hartini Artificial neural network modelling for IQ classification based on EEG signals / Aishah 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… |
| format |
Book Section |
| author |
Jahidin, Aishah Hartini |
| author_facet |
Jahidin, Aishah Hartini |
| author_sort |
Jahidin, Aishah Hartini |
| title |
Artificial neural network modelling for IQ classification based on EEG signals / Aishah Hartini Jahidin |
| title_short |
Artificial neural network modelling for IQ classification based on EEG signals / Aishah Hartini Jahidin |
| title_full |
Artificial neural network modelling for IQ classification based on EEG signals / Aishah Hartini Jahidin |
| title_fullStr |
Artificial neural network modelling for IQ classification based on EEG signals / Aishah Hartini Jahidin |
| title_full_unstemmed |
Artificial neural network modelling for IQ classification based on EEG signals / Aishah Hartini Jahidin |
| title_sort |
artificial neural network modelling for iq classification based on eeg signals / aishah hartini jahidin |
| publisher |
Institute of Graduate Studies, UiTM |
| publishDate |
2016 |
| url |
http://ir.uitm.edu.my/id/eprint/19605/ http://ir.uitm.edu.my/id/eprint/19605/1/ABS_AISHAH%20HARTINI%20JAHIDIN%20TDRA%20VOL%209%20IGS%2016.pdf |
| first_indexed |
2023-09-18T23:02:55Z |
| last_indexed |
2023-09-18T23:02:55Z |
| _version_ |
1777418293010759680 |