EEG SUB-BAND SPECTRAL CENTROID FREQUENCY AND AMPLITUDE RATIO FEATURES: A COMPARATIVE STUDY IN LEARNING STYLE CLASSIFICATION

Authors

  • Megat Syahirul Amin Megat Ali Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Aisyah Hartini Jahidin Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Mohd Nasir Taib Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Nooritawati Md Tahir Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

DOI:

https://doi.org/10.11113/jt.v78.4100

Keywords:

EEG, learning style, spectral centroid frequency, amplitude ratio, k-nearest neighbor

Abstract

Learning styles are critical element in constructivism that facilitates the process of knowledge creation. Conventional methods to evaluate the psychological trait however are exposed to reliability issues which stem from cultural and language barriers. Hence, a new assessment approach based on the resting EEG is proposed. The paper presents a comparative study between EEG spectral centroid frequency and ratio features in learning style classification. A total of 68 university students have participated in the study. Kolb’s Learning Style Inventory has been implemented to establish the control groups. EEG is then recorded from the antero-frontal region and preprocessed for noise removal. Subsequently, the spectral centroid frequency and amplitude features are extracted. The amplitude component is further normalized via the ratio method. Synthetic EEG is implemented for dataset enhancement. In general, separate investigation via k-nearest neighbor classifier has shown that the spectral centroid frequency outperforms the amplitude ratio components. Alternatively, combination of both features concurrently can effectively improve the overall classification performance.

Author Biographies

  • Megat Syahirul Amin Megat Ali, Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
    Megat Syahirul Amin Megat Ali received the B.Eng (Biomedical) from Universiti Malaya, Malaysia, and M.Sc. (Biomedical Engineering) from University of Surrey, United Kingdom. He is currently a senior lecturer at the Faculty of Electrical Engineering, Universiti Teknologi MARA, Malaysia. His research interests include EEG and intelligent modelling of brain behavior with application to experiential learning theory.
  • Aisyah Hartini Jahidin, Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
    Aisyah Hartini Jahidin obtained the B.Eng (Telecommunication) and M.Eng.Sc (Electrical) from Universiti Malaya, Malaysia. She is currently a postgraduate researcher at the Faculty of Electrical Engineering, Universiti Teknologi MARA, Malaysia. Her main research interests include human intelligence, EEG and non-linear modelling of brain behavior via intelligent signal processing technique.
  • Mohd Nasir Taib, Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

    Mohd Nasir Taib obtained the B.Eng (Electrical) from the University of Tasmania, Australia, M.Sc. (Control Systems) from University of Sheffield, and Ph.D. (Control & Instrumentation) from University of Manchester Institute of Science and Technology, United Kingdom. He is currently a Professor and the Dean of the Faculty of Electrical Engineering, Universiti Teknologi MARA, Malaysia. He is leading an active research group and supervising a pool of researchers in advanced signal processing with applications in control systems and process, biomedical engineering, and nonlinear systems.

  • Nooritawati Md Tahir, Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
    Nooritawati Md Tahir received the B.Eng (Electronics) from the Universiti Teknologi MARA, Malaysia, M.Sc. (Microelectronics & Telecommunications) from University of Liverpool, United Kingdom, and Ph.D. in Electrical Engineering (Pattern Recognition & Artificial Intelligence) from Universiti Kebangsaan Malaysia, Malaysia. She is currently an Associate Professor at the Faculty of Electrical Engineering and the Director of Research Innovation Business Unit, Universiti Teknologi MARA, Malaysia. Her research interests include image processing, pattern recognition, computer vision and artificial intelligence.

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Published

2016-02-09

Issue

Vol. 78 No. 2: February 2016

Section

Science and Engineering

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Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.

How to Cite

EEG SUB-BAND SPECTRAL CENTROID FREQUENCY AND AMPLITUDE RATIO FEATURES: A COMPARATIVE STUDY IN LEARNING STYLE CLASSIFICATION. (2016). Jurnal Teknologi (Sciences & Engineering), 78(2). https://doi.org/10.11113/jt.v78.4100