Publication Details
A neurophysiological model based on resting state EEG functional connectivity features for assessing semantic long-term memory performance
Machine Learning Model, EEG, Electroencephalogram, Semantic, Long term memory,
functional connectivity
Existing methods for assessing long-term memory (LTM) rely predominantly on
psychometric tests or clinical expert observations. In this study, we propose an
objective method for evaluating semantic LTM ability using resting-state
electroencephalography (EEG) functional connectivity. Data from 68 participants
were analysed, deriving functional connectivity from the phase information of EEG
theta (4-8 Hz), alpha (8-13 Hz) and gamma (30-45 Hz) frequency bands across the
entire scalp at resting state. Participants' responses were recorded during
a memory recall task over four sessions. Multiple linear regression was used to
model the LTM score. The proposed method successfully predicted LTM retention
after 30 min, with performance metrics of F(18,49) = 2.216, p = 0.014, R=0.670; 2
months retention, F(18,45) = 3.057, p < 0.001, R=0.742; 4 months retention,
F(18,42) = 2.237, p = 0.016, R=0.700; and 6 months retention, F(18,36) = 1.988,
p = 0.039, R=0.706, respectively. Additionally, this method achieved at least 27
points lower in the Bayesian Information Criterion (BIC) compared to the standard
psychometric RAPM test across all retention periods. These findings suggest that
the semantic LTM ability of healthy young individuals can be objectively
quantified using resting-state EEG functional connectivity. This approach holds
promise for future applications in understanding and addressing below standard
performance in students learning.
@article{BUT189541,
author="AMIN, H. and AHMED, A. and YUSOFF, M. and MOHAMAD SAAD, M. and MALIK, A.",
title="A neurophysiological model based on resting state EEG functional connectivity features for assessing semantic long-term memory performance",
journal="Biomedical Signal Processing and Control",
year="2025",
volume="99",
number="1",
pages="1--11",
doi="10.1016/j.bspc.2024.106799",
issn="1746-8108",
url="https://www.sciencedirect.com/science/article/pii/S1746809424008577?dgcid=coauthor"
}