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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/15/5867 |
| _version_ | 1797412220825501696 |
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| author | Amanda Studnicki Ryan J. Downey Daniel P. Ferris |
| author_facet | Amanda Studnicki Ryan J. Downey Daniel P. Ferris |
| author_sort | Amanda Studnicki |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T04:59:23Z |
| format | Article |
| id | doaj.art-b57c642d508d49eab1548f89bf188c93 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:59:23Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b57c642d508d49eab1548f89bf188c932023-12-03T13:02:17ZengMDPI AGSensors1424-82202022-08-012215586710.3390/s22155867Characterizing and Removing Artifacts Using Dual-Layer EEG during Table TennisAmanda Studnicki0Ryan J. Downey1Daniel P. Ferris2J. 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, USAJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USAResearchers 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.https://www.mdpi.com/1424-8220/22/15/5867electroencephalographydual-layermotion artifacttable tennis |
| spellingShingle | Amanda Studnicki Ryan J. Downey Daniel P. Ferris Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis Sensors electroencephalography dual-layer motion artifact 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 | electroencephalography dual-layer motion artifact table tennis |
| url | https://www.mdpi.com/1424-8220/22/15/5867 |
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