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Vol.13, No.4, November 2024. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Working Memory Performance Classification in Children Using Electroencephalogram (EEG) and VGGNet
Nabila Ameera Zainal Abidin, Ahmad Ihsan Mohd Yassin, Wahidah Mansor, Aisyah Hartini Jahidin, Mirsa Nurfarhan Mohd Azhan, Megat Syahirul Amin Megat Ali
© 2024 Megat Syahirul Amin Megat Ali, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 13, Issue 4, Pages 2676-2683, ISSN 2217-8309, DOI: 10.18421/TEM134-05, November 2024.
Received: 30 March 2024. Revised: 21 July 2024.
Abstract:
This study investigates the relationship between EEG and different levels of working memory performance in children. A total of two hundred thirty subjects have volunteered for the study. Initially, the students are required to answer psychometric tests to gauge their working memory performance. Based on the scores obtained, the students are then segregated in high, medium, and low working memory performance groups. Resting EEG is recorded from prefrontal cortex and pre-processed for noise removal. Synthetic EEG is then generated to balance out and enhance the number of samples to two hundred for every control group. Next, short-time Fourier transform is applied to convert the signal to spectrogram. The feature image is used to train the VGGNet model. The deep learning model has been successfully developed with 100% accuracy for training, and 85.8% accuracy for validation. These indicate the potential of assessing working memory performance alternatively using EEG and VGGNet model.
Keywords –Working memory, performance, EEG, spectrogram, VGG16. |
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