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Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis
Researchers can improve the ecological validity of brain research by studying humans moving in real-world settings. Recent work shows that dual-layer EEG can improve the fidelity of electrocortical recordings during gait, but it is unclear whether these positive results extrapolate to non-locomotor...
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/PMC9371038/ https://www.ncbi.nlm.nih.gov/pubmed/35957423 http://dx.doi.org/10.3390/s22155867 |
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author | Studnicki, Amanda Downey, Ryan J. Ferris, Daniel P. |
author_facet | Studnicki, Amanda Downey, Ryan J. Ferris, Daniel P. |
author_sort | Studnicki, Amanda |
collection | PubMed |
description | Researchers can improve the ecological validity of brain research by studying humans moving in real-world settings. Recent work shows that dual-layer EEG can improve the fidelity of electrocortical recordings during gait, but it is unclear whether these positive results extrapolate to non-locomotor paradigms. For our study, we recorded brain activity with dual-layer EEG while participants played table tennis, a whole-body, responsive sport that could help investigate visuomotor feedback, object interception, and performance monitoring. We characterized artifacts with time-frequency analyses and correlated scalp and reference noise data to determine how well different sensors captured artifacts. As expected, individual scalp channels correlated more with noise-matched channel time series than with head and body acceleration. We then compared artifact removal methods with and without the use of the dual-layer noise electrodes. Independent Component Analysis separated channels into components, and we counted the number of high-quality brain components based on the fit of a dipole model and using an automated labeling algorithm. We found that using noise electrodes for data processing provided cleaner brain components. These results advance technological approaches for recording high fidelity brain dynamics in human behaviors requiring whole body movement, which will be useful for brain science research. |
format | Online Article Text |
id | pubmed-9371038 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93710382022-08-12 Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis Studnicki, Amanda Downey, Ryan J. Ferris, Daniel P. Sensors (Basel) Article Researchers can improve the ecological validity of brain research by studying humans moving in real-world settings. Recent work shows that dual-layer EEG can improve the fidelity of electrocortical recordings during gait, but it is unclear whether these positive results extrapolate to non-locomotor paradigms. For our study, we recorded brain activity with dual-layer EEG while participants played table tennis, a whole-body, responsive sport that could help investigate visuomotor feedback, object interception, and performance monitoring. We characterized artifacts with time-frequency analyses and correlated scalp and reference noise data to determine how well different sensors captured artifacts. As expected, individual scalp channels correlated more with noise-matched channel time series than with head and body acceleration. We then compared artifact removal methods with and without the use of the dual-layer noise electrodes. Independent Component Analysis separated channels into components, and we counted the number of high-quality brain components based on the fit of a dipole model and using an automated labeling algorithm. We found that using noise electrodes for data processing provided cleaner brain components. These results advance technological approaches for recording high fidelity brain dynamics in human behaviors requiring whole body movement, which will be useful for brain science research. MDPI 2022-08-05 /pmc/articles/PMC9371038/ /pubmed/35957423 http://dx.doi.org/10.3390/s22155867 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 Studnicki, Amanda Downey, Ryan J. Ferris, Daniel P. Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis |
title | Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis |
title_full | Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis |
title_fullStr | Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis |
title_full_unstemmed | Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis |
title_short | Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis |
title_sort | characterizing and removing artifacts using dual-layer eeg during table tennis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371038/ https://www.ncbi.nlm.nih.gov/pubmed/35957423 http://dx.doi.org/10.3390/s22155867 |
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