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iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG

The goal of this study was to test a novel approach (iCanClean) to remove non-brain sources from scalp EEG data recorded in mobile conditions. We created an electrically conductive phantom head with 10 brain sources, 10 contaminating sources, scalp, and hair. We tested the ability of iCanClean to re...

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Detalles Bibliográficos
Autores principales: Downey, Ryan J., Ferris, Daniel P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10574843/
https://www.ncbi.nlm.nih.gov/pubmed/37837044
http://dx.doi.org/10.3390/s23198214
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author Downey, Ryan J.
Ferris, Daniel P.
author_facet Downey, Ryan J.
Ferris, Daniel P.
author_sort Downey, Ryan J.
collection PubMed
description The goal of this study was to test a novel approach (iCanClean) to remove non-brain sources from scalp EEG data recorded in mobile conditions. We created an electrically conductive phantom head with 10 brain sources, 10 contaminating sources, scalp, and hair. We tested the ability of iCanClean to remove artifacts while preserving brain activity under six conditions: Brain, Brain + Eyes, Brain + Neck Muscles, Brain + Facial Muscles, Brain + Walking Motion, and Brain + All Artifacts. We compared iCanClean to three other methods: Artifact Subspace Reconstruction (ASR), Auto-CCA, and Adaptive Filtering. Before and after cleaning, we calculated a Data Quality Score (0–100%), based on the average correlation between brain sources and EEG channels. iCanClean consistently outperformed the other three methods, regardless of the type or number of artifacts present. The most striking result was for the condition with all artifacts simultaneously present. Starting from a Data Quality Score of 15.7% (before cleaning), the Brain + All Artifacts condition improved to 55.9% after iCanClean. Meanwhile, it only improved to 27.6%, 27.2%, and 32.9% after ASR, Auto-CCA, and Adaptive Filtering. For context, the Brain condition scored 57.2% without cleaning (reasonable target). We conclude that iCanClean offers the ability to clear multiple artifact sources in real time and could facilitate human mobile brain-imaging studies with EEG.
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spelling pubmed-105748432023-10-14 iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG Downey, Ryan J. Ferris, Daniel P. Sensors (Basel) Article The goal of this study was to test a novel approach (iCanClean) to remove non-brain sources from scalp EEG data recorded in mobile conditions. We created an electrically conductive phantom head with 10 brain sources, 10 contaminating sources, scalp, and hair. We tested the ability of iCanClean to remove artifacts while preserving brain activity under six conditions: Brain, Brain + Eyes, Brain + Neck Muscles, Brain + Facial Muscles, Brain + Walking Motion, and Brain + All Artifacts. We compared iCanClean to three other methods: Artifact Subspace Reconstruction (ASR), Auto-CCA, and Adaptive Filtering. Before and after cleaning, we calculated a Data Quality Score (0–100%), based on the average correlation between brain sources and EEG channels. iCanClean consistently outperformed the other three methods, regardless of the type or number of artifacts present. The most striking result was for the condition with all artifacts simultaneously present. Starting from a Data Quality Score of 15.7% (before cleaning), the Brain + All Artifacts condition improved to 55.9% after iCanClean. Meanwhile, it only improved to 27.6%, 27.2%, and 32.9% after ASR, Auto-CCA, and Adaptive Filtering. For context, the Brain condition scored 57.2% without cleaning (reasonable target). We conclude that iCanClean offers the ability to clear multiple artifact sources in real time and could facilitate human mobile brain-imaging studies with EEG. MDPI 2023-10-01 /pmc/articles/PMC10574843/ /pubmed/37837044 http://dx.doi.org/10.3390/s23198214 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
Downey, Ryan J.
Ferris, Daniel P.
iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG
title iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG
title_full iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG
title_fullStr iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG
title_full_unstemmed iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG
title_short iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG
title_sort icanclean removes motion, muscle, eye, and line-noise artifacts from phantom eeg
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10574843/
https://www.ncbi.nlm.nih.gov/pubmed/37837044
http://dx.doi.org/10.3390/s23198214
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