Artifacts in simultaneous hdEEG/fMRI imaging: a nonlinear dimensionality reduction approach

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F19%3A43920026" target="_blank" >RIV/00023752:_____/19:43920026 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21460/19:00334368 RIV/00216208:11120/19:43918908

Result on the web

<a href="https://www.mdpi.com/1424-8220/19/20/4454" target="_blank" >https://www.mdpi.com/1424-8220/19/20/4454</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s19204454" target="_blank" >10.3390/s19204454</a>

Result language

angličtina

Original language name

Artifacts in simultaneous hdEEG/fMRI imaging: a nonlinear dimensionality reduction approach

Original language description

Simultaneous recordings of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) are at the forefront of technologies of interest to physicians and scientists because they combine the benefits of both modalities—better time resolution (hdEEG) and space resolution (fMRI). However, EEG measurements in the scanner contain an electromagnetic field that is induced in leads as a result of gradient switching slight head movements and vibrations, and it is corrupted by changes in the measured potential because of the Hall phenomenon. The aim of this study is to design and test a methodology for inspecting hidden EEG structures with respect to artifacts. We propose a top-down strategy to obtain additional information that is not visible in a single recording. The time-domain independent component analysis algorithm was employed to obtain independent components and spatial weights. A nonlinear dimension reduction technique t-distributed stochastic neighbor embedding was used to create low-dimensional space, which was then partitioned using the density-based spatial clustering of applications with noise (DBSCAN). The relationships between the found data structure and the used criteria were investigated. As a result, we were able to extract information from the data structure regarding electrooculographic, electrocardiographic, electromyographic and gradient artifacts. This new methodology could facilitate the identification of artifacts and their residues from simultaneous EEG in fMRI.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2019

Confidentiality

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

Name of the periodical

Sensors

ISSN

1424-8220

e-ISSN

—

Volume of the periodical

19

Issue of the periodical within the volume

20

Country of publishing house

CH - SWITZERLAND

Number of pages

21

Pages from-to

"Article Number: 4454"

UT code for WoS article

000497864700102

EID of the result in the Scopus database

2-s2.0-85073436369