title: Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network.
abstract: This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies.
authors.affiliation: Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia. Electronic address: aisyah23@gmail.com.
authors.forename: A H
authors.initials: AH
authors.lastname: Jahidin
authors.affiliation: Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia.
authors.forename: M S A
authors.initials: MS
authors.lastname: Megat Ali
authors.affiliation: Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia.
authors.forename: M N
authors.initials: MN
authors.lastname: Taib
authors.affiliation: Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia.
authors.forename: N Md
authors.initials: NM
authors.lastname: Tahir
authors.affiliation: Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia.
authors.forename: I M
authors.initials: IM
authors.lastname: Yassin
authors.affiliation: Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia.
authors.forename: S
authors.initials: S
authors.lastname: Lias
mesh_terms: D001921:Brain / Q000502:physiology*; D006801:Humans; D007360:Intelligence; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted
keywords: Artificial neural network (ANN); EEG; Intelligence quotient (IQ); Sub-band power ratio; White Gaussian noise