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iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG

Motion artifacts hinder source-level analysis of mobile electroencephalography (EEG) data using independent component analysis (ICA). iCanClean is a novel cleaning algorithm that uses reference noise recordings to remove noisy EEG subspaces, but it has not been formally tested in a parameter sweep....

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Detalles Bibliográficos
Autores principales: Gonsisko, Colton B., Ferris, Daniel P., Downey, Ryan J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863946/
https://www.ncbi.nlm.nih.gov/pubmed/36679726
http://dx.doi.org/10.3390/s23020928
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author Gonsisko, Colton B.
Ferris, Daniel P.
Downey, Ryan J.
author_facet Gonsisko, Colton B.
Ferris, Daniel P.
Downey, Ryan J.
author_sort Gonsisko, Colton B.
collection PubMed
description Motion artifacts hinder source-level analysis of mobile electroencephalography (EEG) data using independent component analysis (ICA). iCanClean is a novel cleaning algorithm that uses reference noise recordings to remove noisy EEG subspaces, but it has not been formally tested in a parameter sweep. The goal of this study was to test iCanClean’s ability to improve the ICA decomposition of EEG data corrupted by walking motion artifacts. Our primary objective was to determine optimal settings and performance in a parameter sweep (varying the window length and r(2) cleaning aggressiveness). High-density EEG was recorded with 120 + 120 (dual-layer) EEG electrodes in young adults, high-functioning older adults, and low-functioning older adults. EEG data were decomposed by ICA after basic preprocessing and iCanClean. Components well-localized as dipoles (residual variance < 15%) and with high brain probability (ICLabel > 50%) were marked as ‘good’. We determined iCanClean’s optimal window length and cleaning aggressiveness to be 4-s and r(2) = 0.65 for our data. At these settings, iCanClean improved the average number of good components from 8.4 to 13.2 (+57%). Good performance could be maintained with reduced sets of noise channels (12.7, 12.2, and 12.0 good components for 64, 32, and 16 noise channels, respectively). Overall, iCanClean shows promise as an effective method to clean mobile EEG data.
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spelling pubmed-98639462023-01-22 iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG Gonsisko, Colton B. Ferris, Daniel P. Downey, Ryan J. Sensors (Basel) Article Motion artifacts hinder source-level analysis of mobile electroencephalography (EEG) data using independent component analysis (ICA). iCanClean is a novel cleaning algorithm that uses reference noise recordings to remove noisy EEG subspaces, but it has not been formally tested in a parameter sweep. The goal of this study was to test iCanClean’s ability to improve the ICA decomposition of EEG data corrupted by walking motion artifacts. Our primary objective was to determine optimal settings and performance in a parameter sweep (varying the window length and r(2) cleaning aggressiveness). High-density EEG was recorded with 120 + 120 (dual-layer) EEG electrodes in young adults, high-functioning older adults, and low-functioning older adults. EEG data were decomposed by ICA after basic preprocessing and iCanClean. Components well-localized as dipoles (residual variance < 15%) and with high brain probability (ICLabel > 50%) were marked as ‘good’. We determined iCanClean’s optimal window length and cleaning aggressiveness to be 4-s and r(2) = 0.65 for our data. At these settings, iCanClean improved the average number of good components from 8.4 to 13.2 (+57%). Good performance could be maintained with reduced sets of noise channels (12.7, 12.2, and 12.0 good components for 64, 32, and 16 noise channels, respectively). Overall, iCanClean shows promise as an effective method to clean mobile EEG data. MDPI 2023-01-13 /pmc/articles/PMC9863946/ /pubmed/36679726 http://dx.doi.org/10.3390/s23020928 Text en © 2023 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
Gonsisko, Colton B.
Ferris, Daniel P.
Downey, Ryan J.
iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG
title iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG
title_full iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG
title_fullStr iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG
title_full_unstemmed iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG
title_short iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG
title_sort icanclean improves independent component analysis of mobile brain imaging with eeg
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863946/
https://www.ncbi.nlm.nih.gov/pubmed/36679726
http://dx.doi.org/10.3390/s23020928
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