EEG artifact removal - state-of-the-art and guidelines
This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activit...
| Autores: | , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/71401 |
| Acceso en línea: | http://hdl.handle.net/10810/71401 |
| Access Level: | acceso abierto |
| Palabra clave: | EEG artifact removal state-of-the-art |
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EEG artifact removal - state-of-the-art and guidelinesUrigüen Garaizabal, José AntonioGarcía Zapirain, María BegoñaEEGartifactremovalstate-of-the-artThis paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activity, of the artifacts and of the EEG measurement model. Then, we present algorithms commonly employed in the literature and describe their key features. Lastly, principally on the basis of the results provided by various researchers, but also supported by our own experience, we compare the state-of-the-art methods in terms of reported performance, and provide guidelines on how to choose a suitable artifact removal algorithm for a given scenario. With this review we have concluded that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis—to be precise, an algorithm based on second-order statistics such as second-order blind identification (SOBI). Other effective alternatives include extended information maximization (InfoMax) and an adaptive mixture of independent component analyzers (AMICA), based on higher order statistics. All of these algorithms have proved particularly effective with simulations and, more importantly, with data collected in controlled recording conditions. Moreover, whenever prior knowledge is available, then a constrained form of the chosen method should be used in order to incorporate such additional information. Finally, since which algorithm is the best performing is highly dependent on the type of the EEG signal, the artifacts and the signal to contaminant ratio, we believe that the optimal method for removing artifacts from the EEG consists in combining more than one algorithm to correct the signal using multiple processing stages, even though this is an option largely unexplored by researchers in the area.Jose Antonio Urigüen was in part funded by Bizkaia:Talent and the European Union’s Seventh Framework Programme. Marie Curie Actions - People. Co-funding of Regional, National and International Programmes. Grant agreement n. 267230.IOP202520252015info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/71401reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://doi.org/10.1088/1741-2560/12/3/031001info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/© 2015 IOP Publishing under a CC BY-NC-NDoai:addi.ehu.eus:10810/714012026-06-18T09:23:17Z |
| dc.title.none.fl_str_mv |
EEG artifact removal - state-of-the-art and guidelines |
| title |
EEG artifact removal - state-of-the-art and guidelines |
| spellingShingle |
EEG artifact removal - state-of-the-art and guidelines Urigüen Garaizabal, José Antonio EEG artifact removal state-of-the-art |
| title_short |
EEG artifact removal - state-of-the-art and guidelines |
| title_full |
EEG artifact removal - state-of-the-art and guidelines |
| title_fullStr |
EEG artifact removal - state-of-the-art and guidelines |
| title_full_unstemmed |
EEG artifact removal - state-of-the-art and guidelines |
| title_sort |
EEG artifact removal - state-of-the-art and guidelines |
| dc.creator.none.fl_str_mv |
Urigüen Garaizabal, José Antonio García Zapirain, María Begoña |
| author |
Urigüen Garaizabal, José Antonio |
| author_facet |
Urigüen Garaizabal, José Antonio García Zapirain, María Begoña |
| author_role |
author |
| author2 |
García Zapirain, María Begoña |
| author2_role |
author |
| dc.subject.none.fl_str_mv |
EEG artifact removal state-of-the-art |
| topic |
EEG artifact removal state-of-the-art |
| description |
This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activity, of the artifacts and of the EEG measurement model. Then, we present algorithms commonly employed in the literature and describe their key features. Lastly, principally on the basis of the results provided by various researchers, but also supported by our own experience, we compare the state-of-the-art methods in terms of reported performance, and provide guidelines on how to choose a suitable artifact removal algorithm for a given scenario. With this review we have concluded that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis—to be precise, an algorithm based on second-order statistics such as second-order blind identification (SOBI). Other effective alternatives include extended information maximization (InfoMax) and an adaptive mixture of independent component analyzers (AMICA), based on higher order statistics. All of these algorithms have proved particularly effective with simulations and, more importantly, with data collected in controlled recording conditions. Moreover, whenever prior knowledge is available, then a constrained form of the chosen method should be used in order to incorporate such additional information. Finally, since which algorithm is the best performing is highly dependent on the type of the EEG signal, the artifacts and the signal to contaminant ratio, we believe that the optimal method for removing artifacts from the EEG consists in combining more than one algorithm to correct the signal using multiple processing stages, even though this is an option largely unexplored by researchers in the area. |
| publishDate |
2015 |
| dc.date.none.fl_str_mv |
2015 2025 2025 |
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info:eu-repo/semantics/article |
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article |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/10810/71401 |
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http://hdl.handle.net/10810/71401 |
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Inglés |
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Inglés |
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https://doi.org/10.1088/1741-2560/12/3/031001 |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2015 IOP Publishing under a CC BY-NC-ND |
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openAccess |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2015 IOP Publishing under a CC BY-NC-ND |
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application/pdf |
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IOP |
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IOP |
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reponame:Addi. Archivo Digital para la Docencia y la Investigación instname:Universidad del País Vasco |
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Universidad del País Vasco |
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