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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Biomedical Signal Processing and Control

ISSN

1746-8094

e-ISSN

1746-8108

Svazek periodika

99

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

9

Strana od-do

1-11

Kód UT WoS článku

001313705500001

EID výsledku v databázi Scopus

2-s2.0-85203428307