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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Autor*in:	Ryan J. Downey [verfasserIn] Daniel P. Ferris [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	EEG noise cancellation artifact removal motion artifacts muscle artifacts phantom head

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 23(2023), 8214, p 8214
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:8214, p 8214

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s23198214

Katalog-ID:	DOAJ09320163X

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520			\|a 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: <i<Brain</i<, <i<Brain + Eyes</i<, <i<Brain + Neck Muscles</i<, <i<Brain + Facial Muscles</i<, <i<Brain + Walking Motion</i<, and <i<Brain + All Artifacts</i<. 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 <i<Brain + All Artifacts</i< 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 <i<Brain</i< 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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allfields	10.3390/s23198214 doi (DE-627)DOAJ09320163X (DE-599)DOAJ12829bec60b146a6b8987d7201735b63 DE-627 ger DE-627 rakwb eng TP1-1185 Ryan J. Downey verfasserin aut iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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: <i<Brain</i<, <i<Brain + Eyes</i<, <i<Brain + Neck Muscles</i<, <i<Brain + Facial Muscles</i<, <i<Brain + Walking Motion</i<, and <i<Brain + All Artifacts</i<. 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 <i<Brain + All Artifacts</i< 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 <i<Brain</i< 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. EEG noise cancellation artifact removal motion artifacts muscle artifacts phantom head Chemical technology Daniel P. Ferris verfasserin aut In Sensors MDPI AG, 2003 23(2023), 8214, p 8214 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:8214, p 8214 https://doi.org/10.3390/s23198214 kostenfrei https://doaj.org/article/12829bec60b146a6b8987d7201735b63 kostenfrei https://www.mdpi.com/1424-8220/23/19/8214 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 8214, p 8214
spelling	10.3390/s23198214 doi (DE-627)DOAJ09320163X (DE-599)DOAJ12829bec60b146a6b8987d7201735b63 DE-627 ger DE-627 rakwb eng TP1-1185 Ryan J. Downey verfasserin aut iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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: <i<Brain</i<, <i<Brain + Eyes</i<, <i<Brain + Neck Muscles</i<, <i<Brain + Facial Muscles</i<, <i<Brain + Walking Motion</i<, and <i<Brain + All Artifacts</i<. 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 <i<Brain + All Artifacts</i< 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 <i<Brain</i< 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. EEG noise cancellation artifact removal motion artifacts muscle artifacts phantom head Chemical technology Daniel P. Ferris verfasserin aut In Sensors MDPI AG, 2003 23(2023), 8214, p 8214 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:8214, p 8214 https://doi.org/10.3390/s23198214 kostenfrei https://doaj.org/article/12829bec60b146a6b8987d7201735b63 kostenfrei https://www.mdpi.com/1424-8220/23/19/8214 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 8214, p 8214
allfields_unstemmed	10.3390/s23198214 doi (DE-627)DOAJ09320163X (DE-599)DOAJ12829bec60b146a6b8987d7201735b63 DE-627 ger DE-627 rakwb eng TP1-1185 Ryan J. Downey verfasserin aut iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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: <i<Brain</i<, <i<Brain + Eyes</i<, <i<Brain + Neck Muscles</i<, <i<Brain + Facial Muscles</i<, <i<Brain + Walking Motion</i<, and <i<Brain + All Artifacts</i<. 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 <i<Brain + All Artifacts</i< 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 <i<Brain</i< 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. EEG noise cancellation artifact removal motion artifacts muscle artifacts phantom head Chemical technology Daniel P. Ferris verfasserin aut In Sensors MDPI AG, 2003 23(2023), 8214, p 8214 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:8214, p 8214 https://doi.org/10.3390/s23198214 kostenfrei https://doaj.org/article/12829bec60b146a6b8987d7201735b63 kostenfrei https://www.mdpi.com/1424-8220/23/19/8214 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 8214, p 8214
allfieldsGer	10.3390/s23198214 doi (DE-627)DOAJ09320163X (DE-599)DOAJ12829bec60b146a6b8987d7201735b63 DE-627 ger DE-627 rakwb eng TP1-1185 Ryan J. Downey verfasserin aut iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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: <i<Brain</i<, <i<Brain + Eyes</i<, <i<Brain + Neck Muscles</i<, <i<Brain + Facial Muscles</i<, <i<Brain + Walking Motion</i<, and <i<Brain + All Artifacts</i<. 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 <i<Brain + All Artifacts</i< 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 <i<Brain</i< 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. EEG noise cancellation artifact removal motion artifacts muscle artifacts phantom head Chemical technology Daniel P. Ferris verfasserin aut In Sensors MDPI AG, 2003 23(2023), 8214, p 8214 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:8214, p 8214 https://doi.org/10.3390/s23198214 kostenfrei https://doaj.org/article/12829bec60b146a6b8987d7201735b63 kostenfrei https://www.mdpi.com/1424-8220/23/19/8214 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 8214, p 8214
allfieldsSound	10.3390/s23198214 doi (DE-627)DOAJ09320163X (DE-599)DOAJ12829bec60b146a6b8987d7201735b63 DE-627 ger DE-627 rakwb eng TP1-1185 Ryan J. Downey verfasserin aut iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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: <i<Brain</i<, <i<Brain + Eyes</i<, <i<Brain + Neck Muscles</i<, <i<Brain + Facial Muscles</i<, <i<Brain + Walking Motion</i<, and <i<Brain + All Artifacts</i<. 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 <i<Brain + All Artifacts</i< 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 <i<Brain</i< 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. EEG noise cancellation artifact removal motion artifacts muscle artifacts phantom head Chemical technology Daniel P. Ferris verfasserin aut In Sensors MDPI AG, 2003 23(2023), 8214, p 8214 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:8214, p 8214 https://doi.org/10.3390/s23198214 kostenfrei https://doaj.org/article/12829bec60b146a6b8987d7201735b63 kostenfrei https://www.mdpi.com/1424-8220/23/19/8214 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 8214, p 8214
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callnumber-first	T - Technology
author	Ryan J. Downey
spellingShingle	Ryan J. Downey misc TP1-1185 misc EEG misc noise cancellation misc artifact removal misc motion artifacts misc muscle artifacts misc phantom head misc Chemical technology iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG
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topic_title	TP1-1185 iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG EEG noise cancellation artifact removal motion artifacts muscle artifacts phantom head
topic	misc TP1-1185 misc EEG misc noise cancellation misc artifact removal misc motion artifacts misc muscle artifacts misc phantom head misc Chemical technology
topic_unstemmed	misc TP1-1185 misc EEG misc noise cancellation misc artifact removal misc motion artifacts misc muscle artifacts misc phantom head misc Chemical technology
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title	iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG
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title_sort	icanclean removes motion, muscle, eye, and line-noise artifacts from phantom eeg
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title_auth	iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG
abstract	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: <i<Brain</i<, <i<Brain + Eyes</i<, <i<Brain + Neck Muscles</i<, <i<Brain + Facial Muscles</i<, <i<Brain + Walking Motion</i<, and <i<Brain + All Artifacts</i<. 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 <i<Brain + All Artifacts</i< 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 <i<Brain</i< 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.
abstractGer	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: <i<Brain</i<, <i<Brain + Eyes</i<, <i<Brain + Neck Muscles</i<, <i<Brain + Facial Muscles</i<, <i<Brain + Walking Motion</i<, and <i<Brain + All Artifacts</i<. 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 <i<Brain + All Artifacts</i< 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 <i<Brain</i< 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.
abstract_unstemmed	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: <i<Brain</i<, <i<Brain + Eyes</i<, <i<Brain + Neck Muscles</i<, <i<Brain + Facial Muscles</i<, <i<Brain + Walking Motion</i<, and <i<Brain + All Artifacts</i<. 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 <i<Brain + All Artifacts</i< 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 <i<Brain</i< 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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title_short	iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG
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Meanwhile, it only improved to 27.6%, 27.2%, and 32.9% after ASR, Auto-CCA, and Adaptive Filtering. For context, the <i<Brain</i< condition scored 57.2% without cleaning (reasonable target). 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iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG

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