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All Journal International Journal of Electrical and Computer Engineering Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science ABDI SABHA (Jurnal Pengabdian kepada Masyarakat)
Nooritawati Md Tahir
Universiti Teknologi MARA
Religion Arts Humanities Computer Science & IT Education Electrical & Electronics Engineering Social Sciences

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Journal : International Journal of Electrical and Computer Engineering

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Learning Style Classification via EEG Sub-band Spectral Centroid Frequency Features Megat Syahirul Amin Megat Ali; Aisyah Hartini Jahidin; Nooritawati Md Tahir; Mohd Nasir Taib
International Journal of Electrical and Computer Engineering (IJECE) Vol 4, No 6: December 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (299.413 KB)

Abstract

Kolb’s Experiential Learning Theory postulates that in learning, knowledge is created by the learners’ ability to absorb and transform experience. Many studies have previously suggested that at rest, the brain emits signatures that can be associated with cognitive and behavioural patterns. Hence, the study attempts to characterise and classify learning styles from EEG using the spectral centroid frequency features. Initially, learning style of 68 university students has been assessed using Kolb’s Learning Style Inventory. Resting EEG is then recorded from the prefrontal cortex. Next, the EEG is pre-processed and filtered into alpha and theta sub-bands in which the spectral centroid frequencies are computed from the corresponding power spectral densities. The dataset is further enhanced to 160 samples via synthetic EEG. The obtained features are then used as input to the k-nearest neighbour classifier that is incorporated with k-fold cross-validation. Feature classification via k-nearest neighbour has attained five-fold mean training and testing accuracies of 100% and 97.5%, respectively. Hence, results show that the alpha and theta spectral centroid frequencies represent distinct and stable EEG signature to distinguish learning styles from the resting brain.DOI:http://dx.doi.org/10.11591/ijece.v4i6.6833
IQ Classification via Brainwave Features: Review on Artificial Intelligence Techniques Aisyah Hartini Jahidin; Mohd Nasir Taib; Nooritawati Md Tahir; Megat Syahirul Amin Megat Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 1: February 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (162.714 KB) | DOI: 10.11591/ijece.v5i1.pp84-91

Abstract

Intelligence study is one of keystone to distinguish individual differences in cognitive psychology. Conventional psychometric tests are limited in terms of assessment time, and existence of biasness issues. Apart from that, there is still lack in knowledge to classify IQ based on EEG signals and intelligent signal processing (ISP) technique. ISP purpose is to extract as much information as possible from signal and noise data using learning and/or other smart techniques. Therefore, as a first attempt in classifying IQ feature via scientific approach, it is important to identify a relevant technique with prominent paradigm that is suitable for this area of application. Thus, this article reviews several ISP approaches to provide consolidated source of information. This in particular focuses on prominent paradigm that suitable for pattern classification in biomedical area. The review leads to selection of ANN since it has been widely implemented for pattern classification in biomedical engineering.
Co-Authors Abdul Rahim Bin Ridzuan Abdullah Ahmad Ihsan Mohd Yassin Aisyah Hartini Jahidin Arridina Susan Silitonga Che Zawiyah Che Hasan Cholish Fadhlan Hafizhelmi Kamaruzaman Hana’ Abd Razak Haq, Moh Zainul M. Ahmed M. Saleh Maharani Putri Megat Syahirul Amin Megat Ali Mohammad Farid Saaid Mohd Khairi Nordin Mohd Nasir Taib Pius Fernando Hutauruk Priansus Rhein Rumahorbo Rohilah Sahak Rohilah Sahak Rozita Jailani Suprianto