A neurophysiological model based on resting state EEG functional connectivity features for assessing semantic long-term memory performance
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F25%3APU152096" target="_blank" >RIV/00216305:26230/25:PU152096 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1746809424008577?dgcid=coauthor" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1746809424008577?dgcid=coauthor</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.bspc.2024.106799" target="_blank" >10.1016/j.bspc.2024.106799</a>
Alternative languages
Result language
angličtina
Original language name
A neurophysiological model based on resting state EEG functional connectivity features for assessing semantic long-term memory performance
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Biomedical Signal Processing and Control
ISSN
1746-8094
e-ISSN
1746-8108
Volume of the periodical
99
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
9
Pages from-to
1-11
UT code for WoS article
001313705500001
EID of the result in the Scopus database
2-s2.0-85203428307