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Naslov:Removal of movement-induced EEG artifacts : current state of the art and guidelines
Avtorji:ID Gorjan, Daša (Avtor)
ID Gramann, Klaus (Avtor)
ID De Pauw, Kevin (Avtor)
ID Marušič, Uroš (Avtor)
Datoteke:.pdf PDF - Predstavitvena datoteka, prenos (535,26 KB)
MD5: FB3040AE29F9146980574BFD8C3EFFA0
 
URL URL - Izvorni URL, za dostop obiščite https://iopscience.iop.org/article/10.1088/1741-2552/ac52d2
 
Jezik:Angleški jezik
Tipologija:1.02 - Pregledni znanstveni članek
Organizacija:Logo ZRS Koper - Znanstveno-raziskovalno središče Koper / Centro di Ricerche Scientifiche Capodistria
Povzetek:Electroencephalography (EEG) is a non-invasive technique used to record cortical neurons' electrical activity using electrodes placed on the scalp. It has become a promising avenue for research beyond state-of-the-art EEG research that is conducted under static conditions. EEG signals are always contaminated by artifacts and other physiological signals. Artifact contamination increases with the intensity of movement. In the last decade (since 2010), researchers have started to implement EEG measurements in dynamic setups to increase the overall ecological validity of the studies. Many different methods are used to remove non-brain activity from the EEG signal, and there are no clear guidelines on which method should be used in dynamic setups and for specific movement intensities. Currently, the most common methods for removing artifacts in movement studies are methods based on independent component analysis (ICA). However, the choice of method for artifact removal depends on the type and intensity of movement, which affects the characteristics of the artifacts and the EEG parameters of interest. When dealing with EEG under non-static conditions, special care must be taken already in the designing period of an experiment. Software and hardware solutions must be combined to achieve sufficient removal of unwanted signals from EEG measurements. We have provided recommendations for the use of each method depending on the intensity of the movement and highlighted the advantages and disadvantages of the methods. However, due to the current gap in the literature, further development and evaluation of methods for artifact removal in EEG data during locomotion is needed.
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Datum sprejetja članka:08.02.2022
Datum objave:28.02.2022
Leto izida:2022
Št. strani:str. 1-12
Številčenje:Vol. 19, no. 1
PID:20.500.12556/DiRROS-14767 Novo okno
UDK:612.82:616-07
ISSN pri članku:1741-2552
DOI:10.1088/1741-2552/ac52d2 Novo okno
COBISS.SI-ID:97228035 Novo okno
Avtorske pravice:© 2022 The Author(s)
Opomba:Nasl. z nasl. zaslona; Soavtorji: Klaus Gramann, Kevin De Pauw, Uros Marusic; Opis vira z dne 14. 2. 2022; Članek št. 011004;
Datum objave v DiRROS:01.03.2022
Število ogledov:552
Število prenosov:553
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Gradivo je del revije

Naslov:Journal of neural engineering
Skrajšan naslov:J. neural eng.
Založnik:Institute of Physics Publishing
ISSN:1741-2552
COBISS.SI-ID:515074585 Novo okno

Gradivo je financirano iz projekta

Financer:EC - European Commission
Številka projekta:952401
Naslov:TWINning the BRAIN with machine learning for neuro-muscular efficiency
Akronim:TwinBrain

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Začetek licenciranja:28.02.2020

Sekundarni jezik

Jezik:Ni določen
Ključne besede:MoBI, EEG, elektroencefalografija, lokomocija, artefakti gibanja, analiza neodvisnih komponent, Mobile brain/body imaging, locomotion, movement artifacts, independent component analysis


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