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Title:Removal of movement-induced EEG artifacts : current state of the art and guidelines
Authors:ID Gorjan, Daša (Author)
ID Gramann, Klaus (Author)
ID De Pauw, Kevin (Author)
ID Marušič, Uroš (Author)
Files:.pdf PDF - Presentation file, download (535,26 KB)
MD5: FB3040AE29F9146980574BFD8C3EFFA0
 
URL URL - Source URL, visit https://iopscience.iop.org/article/10.1088/1741-2552/ac52d2
 
Language:English
Typology:1.02 - Review Article
Organization:Logo ZRS Koper - Science and Research Centre Koper
Abstract: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.
Publication status:Published
Publication version:Version of Record
Article acceptance date:08.02.2022
Publication date:28.02.2022
Year of publishing:2022
Number of pages:str. 1-12
Numbering:Vol. 19, no. 1
PID:20.500.12556/DiRROS-14767 New window
UDC:612.82:616-07
ISSN on article:1741-2552
DOI:10.1088/1741-2552/ac52d2 New window
COBISS.SI-ID:97228035 New window
Copyright:© 2022 The Author(s)
Note:Nasl. z nasl. zaslona; Soavtorji: Klaus Gramann, Kevin De Pauw, Uros Marusic; Opis vira z dne 14. 2. 2022; Članek št. 011004;
Publication date in DiRROS:01.03.2022
Views:524
Downloads:531
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Record is a part of a journal

Title:Journal of neural engineering
Shortened title:J. neural eng.
Publisher:Institute of Physics Publishing
ISSN:1741-2552
COBISS.SI-ID:515074585 New window

Document is financed by a project

Funder:EC - European Commission
Project number:952401
Name:TWINning the BRAIN with machine learning for neuro-muscular efficiency
Acronym:TwinBrain

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:28.02.2020

Secondary language

Language:Undetermined
Keywords:MoBI, EEG, elektroencefalografija, lokomocija, artefakti gibanja, analiza neodvisnih komponent, Mobile brain/body imaging, locomotion, movement artifacts, independent component analysis


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