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p-Index From 2021 - 2026
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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control)
Zulkifli Mahmoodin
Universiti Kuala Lumpur British Malaysian Institute
Computer Science & IT Electrical & Electronics Engineering

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Machine learning and deep learning performance in classifying dyslexic children’s electroencephalogram during writing Ahmad Zuber Ahmad Zainuddin; Wahidah Mansor; Khuan Yoot Lee; Zulkifli Mahmoodin
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6614-6624

Abstract

Dyslexia is a form of learning disability that causes a child to have difficulties in writing alphabets, reading words, and doing mathematics. Early identification of dyslexia is important to provide early intervention to improve learning disabilities. This study was carried out to differentiate EEG signals of poor dyslexic, capable dyslexic, and normal children during writing using machine learning and deep learning. three machine learning algorithms were studied: k-nearest neighbors (KNN), support vector machine (SVM), and extreme learning machine (ELM) with input features from coefficients of beta and theta band power extracted using discrete wavelet transform (DWT). As for the deep learning (DL) algorithm, long short-term memory (LSTM) architecture was employed. The kernel parameters of the classifiers were optimized to achieve high classification accuracy. Results showed that db8 achieved the greatest classification accuracy for all classifiers. Support vector machine with radial basis function kernel yields the highest accuracy which is 88% than other classifiers. The support vector machine with radial basis function kernel with db8 could be employed in determining the dyslexic children’s levels objectively during writing.
Identification of working memory status in children from EEG signal features using discrete wavelet transform Muhammad Hilmi Khairul Azlan; Wahidah Mansor; Ahmad Ihsan Mohd Yassin; Nabila Ameera Zainal Abidin; Mirsa Nurfarhan Mohd Azhan; Aisyah Hartini Jahidin; Muhammad Fakharul Radzy Mohd Rozlan; Zulkifli Mahmoodin; Megat Syahirul Amin Megat Ali
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 1: February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i1.25551

Abstract

The conventional method for assessing the working memory performance of children is time-consuming and potentially inaccurate, especially when dealing with many samples. Therefore, an automated system that can produce swift and accurate results is required. Electroencephalograms (EEG) can be used to analyse the working memory status of children by extracting specific features from the EEG signal, which can be incorporated into an automatic system to reduce manpower and processing time for analysis. This project used EEG recording to identify children’s working memory status while they were performing working memory tasks. EEG signals were acquired from both children and adults using an automated computer-based working memory assessment tool, processed, and analyzed. The discrete wavelet transform (DWT) was then employed to identify five distinct working memory statuses: distracted, confused, daydreaming, losing focus, and active. DWT was also used to extract features that demonstrate these various statuses. The results showed that DWT could accurately identify the working memory status of both children and adults from their EEGs. This work has thus provided a more efficient method for extracting features from EEG signals to identify working memory statuses in both children and adults.
Co-Authors Ahmad Ihsan Mohd Yassin Ahmad Zuber Ahmad Zainuddin Aisyah Hartini Jahidin Khuan Yoot Lee Megat Syahirul Amin Megat Ali Mirsa Nurfarhan Mohd Azhan Muhammad Fakharul Radzy Mohd Rozlan Muhammad Hilmi Khairul Azlan Nabila Ameera Zainal Abidin Wahidah Mansor