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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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/19/8214 |
| _version_ | 1827722176429555712 |
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| author | Ryan J. Downey Daniel P. Ferris |
| author_facet | Ryan J. Downey Daniel P. Ferris |
| author_sort | Ryan J. Downey |
| collection | DOAJ |
| 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: <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. |
| first_indexed | 2024-03-10T21:34:10Z |
| format | Article |
| id | doaj.art-12829bec60b146a6b8987d7201735b63 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T21:34:10Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-12829bec60b146a6b8987d7201735b632023-11-19T15:04:22ZengMDPI AGSensors1424-82202023-10-012319821410.3390/s23198214iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEGRyan J. Downey0Daniel P. Ferris1J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USAJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USAThe 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.https://www.mdpi.com/1424-8220/23/19/8214EEGnoise cancellationartifact removalmotion artifactsmuscle artifactsphantom head |
| spellingShingle | Ryan J. Downey Daniel P. Ferris iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG Sensors EEG noise cancellation artifact removal motion artifacts muscle artifacts phantom head |
| 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 | EEG noise cancellation artifact removal motion artifacts muscle artifacts phantom head |
| url | https://www.mdpi.com/1424-8220/23/19/8214 |
| work_keys_str_mv | AT ryanjdowney icancleanremovesmotionmuscleeyeandlinenoiseartifactsfromphantomeeg AT danielpferris icancleanremovesmotionmuscleeyeandlinenoiseartifactsfromphantomeeg |