Removal of movement-induced EEG artifacts: current state of the art and guidelines
| dc.contributor.author | Gorjan, Dasa | |
| dc.contributor.author | Gramann, Klaus | |
| dc.contributor.author | De Pauw, Kevin | |
| dc.contributor.author | Marusic, Uros | |
| dc.date.accessioned | 2022-03-30T12:18:01Z | |
| dc.date.available | 2022-03-30T12:18:01Z | |
| dc.date.issued | 2022-02-28 | |
| dc.date.updated | 2022-03-26T02:18:10Z | |
| dc.description.abstract | Objective: 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. Approach: 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. Main results: Currently, the most common methods for removing artifacts in movement studies are methods based on independent component analysis. 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. Significance: 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. | en |
| dc.description.sponsorship | EC/H2020/952401/EU/TWINning the BRAIN with machine learning for neuro-muscular efficiency/TwinBrain | en |
| dc.identifier.eissn | 1741-2552 | |
| dc.identifier.issn | 1741-2560 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/16601 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-15378 | |
| dc.language.iso | en | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject.ddc | 570 Biowissenschaften; Biologie | de |
| dc.subject.other | mobile brain/body imaging | en |
| dc.subject.other | EEG | en |
| dc.subject.other | locomotion | en |
| dc.subject.other | movement artifacts | en |
| dc.subject.other | independent component analysis | en |
| dc.title | Removal of movement-induced EEG artifacts: current state of the art and guidelines | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.articlenumber | 011004 | en |
| dcterms.bibliographicCitation.doi | 10.1088/1741-2552/ac542c | en |
| dcterms.bibliographicCitation.issue | 1 | en |
| dcterms.bibliographicCitation.journaltitle | Journal of Neural Engineering | en |
| dcterms.bibliographicCitation.originalpublishername | IOP | en |
| dcterms.bibliographicCitation.originalpublisherplace | Bristol | en |
| dcterms.bibliographicCitation.volume | 19 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 5 Verkehrs- und Maschinensysteme::Inst. Psychologie und Arbeitswissenschaft::FG Kognitionspsychologie & Kognitive Ergonomie | de |
| tub.affiliation.faculty | Fak. 5 Verkehrs- und Maschinensysteme | de |
| tub.affiliation.group | FG Kognitionspsychologie & Kognitive Ergonomie | de |
| tub.affiliation.institute | Inst. Psychologie und Arbeitswissenschaft | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |