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Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF)
Electroencephalogram (EEG) data are typically affected by artifacts. The detection and removal of bad channels (i.e., with poor signal-to-noise ratio) is a crucial initial step. EEG data acquired from different populations require different cleaning strategies due to the inherent differences in the...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571252/ https://www.ncbi.nlm.nih.gov/pubmed/36236413 http://dx.doi.org/10.3390/s22197314 |
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author | Kumaravel, Velu Prabhakar Buiatti, Marco Parise, Eugenio Farella, Elisabetta |
author_facet | Kumaravel, Velu Prabhakar Buiatti, Marco Parise, Eugenio Farella, Elisabetta |
author_sort | Kumaravel, Velu Prabhakar |
collection | PubMed |
description | Electroencephalogram (EEG) data are typically affected by artifacts. The detection and removal of bad channels (i.e., with poor signal-to-noise ratio) is a crucial initial step. EEG data acquired from different populations require different cleaning strategies due to the inherent differences in the data quality, the artifacts’ nature, and the employed experimental paradigm. To deal with such differences, we propose a robust EEG bad channel detection method based on the Local Outlier Factor (LOF) algorithm. Unlike most existing bad channel detection algorithms that look for the global distribution of channels, LOF identifies bad channels relative to the local cluster of channels, which makes it adaptable to any kind of EEG. To test the performance and versatility of the proposed algorithm, we validated it on EEG acquired from three populations (newborns, infants, and adults) and using two experimental paradigms (event-related and frequency-tagging). We found that LOF can be applied to all kinds of EEG data after calibrating its main hyperparameter: the LOF threshold. We benchmarked the performance of our approach with the existing state-of-the-art (SoA) bad channel detection methods. We found that LOF outperforms all of them by improving the F1 Score, our chosen performance metric, by about 40% for newborns and infants and 87.5% for adults. |
format | Online Article Text |
id | pubmed-9571252 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95712522022-10-17 Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF) Kumaravel, Velu Prabhakar Buiatti, Marco Parise, Eugenio Farella, Elisabetta Sensors (Basel) Article Electroencephalogram (EEG) data are typically affected by artifacts. The detection and removal of bad channels (i.e., with poor signal-to-noise ratio) is a crucial initial step. EEG data acquired from different populations require different cleaning strategies due to the inherent differences in the data quality, the artifacts’ nature, and the employed experimental paradigm. To deal with such differences, we propose a robust EEG bad channel detection method based on the Local Outlier Factor (LOF) algorithm. Unlike most existing bad channel detection algorithms that look for the global distribution of channels, LOF identifies bad channels relative to the local cluster of channels, which makes it adaptable to any kind of EEG. To test the performance and versatility of the proposed algorithm, we validated it on EEG acquired from three populations (newborns, infants, and adults) and using two experimental paradigms (event-related and frequency-tagging). We found that LOF can be applied to all kinds of EEG data after calibrating its main hyperparameter: the LOF threshold. We benchmarked the performance of our approach with the existing state-of-the-art (SoA) bad channel detection methods. We found that LOF outperforms all of them by improving the F1 Score, our chosen performance metric, by about 40% for newborns and infants and 87.5% for adults. MDPI 2022-09-27 /pmc/articles/PMC9571252/ /pubmed/36236413 http://dx.doi.org/10.3390/s22197314 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kumaravel, Velu Prabhakar Buiatti, Marco Parise, Eugenio Farella, Elisabetta Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF) |
title | Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF) |
title_full | Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF) |
title_fullStr | Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF) |
title_full_unstemmed | Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF) |
title_short | Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF) |
title_sort | adaptable and robust eeg bad channel detection using local outlier factor (lof) |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571252/ https://www.ncbi.nlm.nih.gov/pubmed/36236413 http://dx.doi.org/10.3390/s22197314 |
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