An EEG-Based Visual Working Memory Assessment Protocol for Children

• Published in: IEEE Symposium on Wireless Technology and Applications, ISWTA
• Main Author: Azhan M.N.M.; Megat Ali M.S.A.; Mansor W.; Zainal Abidin N.A.; Jahidin A.H.; Mohd Yassin A.I.; Mohd Rozlan M.F.R.; Mahmoodin Z.
• Format: Conference paper
• Language: English
• Published: IEEE Computer Society 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174261727&doi=10.1109%2fISWTA58588.2023.10249299&partnerID=40&md5=2e5f9ee1fcf8542a06e539842962b78a

Working memory is a critical cognitive skill essential for supporting children's learning and academic success. However, existing assessment methods are time-consuming and require human intervention to interpret the result. To address this challenge, we propose an innovative EEG-based visual working memory assessment protocol specifically designed for children. The primary objective is to offer a more comprehensive and reliable evaluation of children's working memory abilities. Electroencephalography (EEG) is utilized to record brain electrical activity, enabling analysis and interpretation of working memory performance. The assessment protocol follows the guidelines set by the Automated Working Memory Assessment (AWMA) and encompasses four tasks, including recalling the sequence of dots appearance, recalling the correct shapes and their order of appearance, along with pre- and post-task relaxation periods. The assessment focuses on the Visuospatial Sketchpad aspect of working memory and measures brain activity through 32 scalp electrodes, with a particular emphasis on the prefrontal cortex. Our findings demonstrate distinct categorizations of top scorers, average scorers, and under scorers based on their working memory performance, providing further validation of the protocol's efficacy in capturing individual differences in working memory abilities. This innovative protocol holds the potential to support continuous working memory assessment and foster the development of essential learning skills in children. © 2023 IEEE.