Detail publikace

A neurophysiological model based on resting state EEG functional connectivity features for assessing semantic long-term memory performance

AMIN, H. AHMED, A. YUSOFF, M. MOHAMAD SAAD, M. MALIK, A.

Originální název

A neurophysiological model based on resting state EEG functional connectivity features for assessing semantic long-term memory performance

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Machine Learning Model, EEG, Electroencephalogram, Semantic, Long term memory, functional connectivity

Autoři

AMIN, H.; AHMED, A.; YUSOFF, M.; MOHAMAD SAAD, M.; MALIK, A.

Vydáno

2. 1. 2025

ISSN

1746-8108

Periodikum

Biomedical Signal Processing and Control

Ročník

99

Číslo

1

Stát

Spojené království Velké Británie a Severního Irska

Strany od

1

Strany do

11

Strany počet

9

URL

https://www.sciencedirect.com/science/article/pii/S1746809424008577?dgcid=coauthor

BibTex

@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"
}