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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| 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 |
| Sumario: | 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. |
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