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Article

Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis

J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
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Author to whom correspondence should be addressed.
Academic Editors: Winko W. An, Laurel Gabard-Durnam and Wanze Xie
Sensors 2022, 22(15), 5867; https://doi.org/10.3390/s22155867 (registering DOI)
Received: 1 July 2022 / Revised: 28 July 2022 / Accepted: 2 August 2022 / Published: 5 August 2022
(This article belongs to the Special Issue Advances on EEG-Based Sensing and Imaging)
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. View Full-Text
Keywords: electroencephalography; dual-layer; motion artifact; table tennis electroencephalography; dual-layer; motion artifact; table tennis
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MDPI and ACS Style

Studnicki, A.; Downey, R.J.; Ferris, D.P. Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis. Sensors 2022, 22, 5867. https://doi.org/10.3390/s22155867

AMA Style

Studnicki A, Downey RJ, Ferris DP. Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis. Sensors. 2022; 22(15):5867. https://doi.org/10.3390/s22155867

Chicago/Turabian Style

Studnicki, Amanda, Ryan J. Downey, and Daniel P. Ferris. 2022. "Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis" Sensors 22, no. 15: 5867. https://doi.org/10.3390/s22155867

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