Working Memory Performance Classification in Children Using Electroencephalogram (EEG) and VGGNet

被引：0

作者：

Abidin, Nabila Ameera Zainal ^{[1
]}

Yassin, Ahmad Ihsan Mohd ^{[2
]}

Mansor, Wahidah ^{[1
]}

Jahidin, Aisyah Hartini ^{[3
]}

Azhan, Mirsa Nurfarhan Mohd ^{[1
]}

Ali, Megat Syahirul Amin Megat ^{[2
]}

机构：

[1] Univ Teknol MARA, Coll Engn, Sch Elect Engn, Shah Alam, Malaysia

[2] Univ Teknol MARA, Microwave Res Inst, Shah Alam, Malaysia

[3] Univ Malaya, Ctr Fdn Study Sci, Kuala Lumpur, Malaysia

来源：

TEM JOURNAL-TECHNOLOGY EDUCATION MANAGEMENT INFORMATICS | 2024年 / 13卷 / 04期

关键词：

Working memory; performance; EEG; spectrogram; NEURAL EFFICIENCY;

D O I：

10.18421/TEM134-05

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

- 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 and VGGNet model.

引用

页码：2676 / 2683

页数：8

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