Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU124580" target="_blank" >RIV/00216305:26220/18:PU124580 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14740/18:00100718 RIV/61989592:15110/18:73590078

Výsledek na webu

<a href="https://link.springer.com/article/10.1007%2Fs10548-017-0585-8" target="_blank" >https://link.springer.com/article/10.1007%2Fs10548-017-0585-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10548-017-0585-8" target="_blank" >10.1007/s10548-017-0585-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA

Popis výsledku v původním jazyce

Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.

Název v anglickém jazyce

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA

Popis výsledku anglicky

Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

BRAIN TOPOGRAPHY

ISSN

0896-0267

e-ISSN

1573-6792

Svazek periodika

31

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

76-89

Kód UT WoS článku

000422889300007

EID výsledku v databázi Scopus

2-s2.0-85029006656