Identification of working memory status in children from EEG signal features using discrete wavelet transform

• Published in: Telkomnika (Telecommunication Computing Electronics and Control)
• Main Author: Azlan M.H.K.; Mansor W.; Yassin A.I.M.; Abidin N.A.Z.; Azhan M.N.M.; Jahidin A.H.; Rozlan M.F.R.M.; Mahmoodin Z.; Ali M.S.A.M.
• Format: Article
• Language: English
• Published: Universitas Ahmad Dahlan 2025
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213442882&doi=10.12928%2fTELKOMNIKA.v23i1.25551&partnerID=40&md5=77d3d7a55f7ffd0d3ae19fdc563dfd0f

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. © (2025), (Universitas Ahmad Dahlan). All rights reserved.