EEG artefacts Benchmark Dataset for the Evaluation of Artefact Detection and Correction Pipelines

EEG signals are inherently characterized by significant noise. Traditional methods of denoising, primarily involving manual correction of visual artifacts, are both time-consuming and subject to variability influenced by the expertise and educational background of the professional involved. Conseque...

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Detalles Bibliográficos
Autor: Egaña Elosua, Paula
Tipo de recurso: tesis de maestría
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/415200
Acceso en línea:https://hdl.handle.net/2117/415200
Access Level:acceso abierto
Palabra clave:Fire extinction--Equipment and supplies
Incendis--Extinció--Equip i accessoris
Àrees temàtiques de la UPC::Enginyeria química
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spelling EEG artefacts Benchmark Dataset for the Evaluation of Artefact Detection and Correction PipelinesEgaña Elosua, PaulaFire extinction--Equipment and suppliesIncendis--Extinció--Equip i accessorisÀrees temàtiques de la UPC::Enginyeria químicaEEG signals are inherently characterized by significant noise. Traditional methods of denoising, primarily involving manual correction of visual artifacts, are both time-consuming and subject to variability influenced by the expertise and educational background of the professional involved. Consequently, there has been a growing interest in automating the detection and correction of artifacts in EEG signals. Despite ongoing efforts to enhance the automation of artifact detection, two primary challenges persist. Firstly, the absence of a comprehensive database inhibits the assessment of model performance across various scenarios with diverse artifact types. Secondly, the lack of standardized metrics hampers the objective evaluation of the quality of the processed signals. This project aims to address these issues by first recording a dataset wherein participants deliberately introduce artifacts. Subsequently, five artifact correction pipelines will be analyzed and compared to assess their effectiveness in artifact correction. Additionally, the project will establish objective metrics facilitating a standardized comparison of the various correction models.Universitat Politècnica de CatalunyaBraboszcz, Claire20242024-07-1720242024-10-01master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/415200reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4152002026-05-27T15:37:01Z
dc.title.none.fl_str_mv EEG artefacts Benchmark Dataset for the Evaluation of Artefact Detection and Correction Pipelines
title EEG artefacts Benchmark Dataset for the Evaluation of Artefact Detection and Correction Pipelines
spellingShingle EEG artefacts Benchmark Dataset for the Evaluation of Artefact Detection and Correction Pipelines
Egaña Elosua, Paula
Fire extinction--Equipment and supplies
Incendis--Extinció--Equip i accessoris
Àrees temàtiques de la UPC::Enginyeria química
title_short EEG artefacts Benchmark Dataset for the Evaluation of Artefact Detection and Correction Pipelines
title_full EEG artefacts Benchmark Dataset for the Evaluation of Artefact Detection and Correction Pipelines
title_fullStr EEG artefacts Benchmark Dataset for the Evaluation of Artefact Detection and Correction Pipelines
title_full_unstemmed EEG artefacts Benchmark Dataset for the Evaluation of Artefact Detection and Correction Pipelines
title_sort EEG artefacts Benchmark Dataset for the Evaluation of Artefact Detection and Correction Pipelines
dc.creator.none.fl_str_mv Egaña Elosua, Paula
author Egaña Elosua, Paula
author_facet Egaña Elosua, Paula
author_role author
dc.contributor.none.fl_str_mv Braboszcz, Claire
dc.subject.none.fl_str_mv Fire extinction--Equipment and supplies
Incendis--Extinció--Equip i accessoris
Àrees temàtiques de la UPC::Enginyeria química
topic Fire extinction--Equipment and supplies
Incendis--Extinció--Equip i accessoris
Àrees temàtiques de la UPC::Enginyeria química
description EEG signals are inherently characterized by significant noise. Traditional methods of denoising, primarily involving manual correction of visual artifacts, are both time-consuming and subject to variability influenced by the expertise and educational background of the professional involved. Consequently, there has been a growing interest in automating the detection and correction of artifacts in EEG signals. Despite ongoing efforts to enhance the automation of artifact detection, two primary challenges persist. Firstly, the absence of a comprehensive database inhibits the assessment of model performance across various scenarios with diverse artifact types. Secondly, the lack of standardized metrics hampers the objective evaluation of the quality of the processed signals. This project aims to address these issues by first recording a dataset wherein participants deliberately introduce artifacts. Subsequently, five artifact correction pipelines will be analyzed and compared to assess their effectiveness in artifact correction. Additionally, the project will establish objective metrics facilitating a standardized comparison of the various correction models.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-07-17
2024
2024-10-01
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/415200
url https://hdl.handle.net/2117/415200
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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