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Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling
Background: Electroencephalography (EEG)-derived event-related potentials (ERPs) provide information about a variety of brain functions, but often suffer from low inherent signal-to-noise ratio (SNR). To overcome the low SNR, techniques that pool data from multiple sensors have been applied. However...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588067/ https://www.ncbi.nlm.nih.gov/pubmed/34770564 http://dx.doi.org/10.3390/s21217258 |
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author | Hajra, Sujoy Ghosh Liu, Careesa C. Fickling, Shaun D. Pawlowski, Gabriela M. Song, Xiaowei D’Arcy, Ryan C. N. |
author_facet | Hajra, Sujoy Ghosh Liu, Careesa C. Fickling, Shaun D. Pawlowski, Gabriela M. Song, Xiaowei D’Arcy, Ryan C. N. |
author_sort | Hajra, Sujoy Ghosh |
collection | PubMed |
description | Background: Electroencephalography (EEG)-derived event-related potentials (ERPs) provide information about a variety of brain functions, but often suffer from low inherent signal-to-noise ratio (SNR). To overcome the low SNR, techniques that pool data from multiple sensors have been applied. However, such pooling implicitly assumes that the SNR among sensors is equal, which is not necessarily valid. This study presents a novel approach for signal pooling that accounts for differential SNR among sensors. Methods: The new technique involves pooling together signals from multiple EEG channels weighted by their respective SNRs relative to the overall SNR of all channels. We compared ERP responses derived using this new technique with those derived using both individual channels as well as traditional averaged-based channel pooling. The outcomes were evaluated in both simulated data and real data from healthy adult volunteers (n = 37). Responses corresponding to a range of ERP components indexing auditory sensation (N100), attention (P300) and language processing (N400) were evaluated. Results: Simulation results demonstrate that, compared to traditional pooling technique, the new SNR-weighted channel pooling technique improved ERP response effect size in cases of unequal noise among channels (p’s < 0.001). Similarly, results from real-world experimental data showed that the new technique resulted in significantly greater ERP effect sizes compared to either traditional pooling or individual channel approach for all three ERP components (p’s < 0.001). Furthermore, the new channel pooling approach also resulted in larger ERP signal amplitudes as well as greater differences among experimental conditions (p’s < 0.001). Conclusion: These results suggest that the new technique improves the capture of ERP responses relative to traditional techniques. As such, SNR-weighted channel pooling can further enable widespread applications of ERP techniques, especially those that require rapid assessments in noisy out-of-laboratory environments. |
format | Online Article Text |
id | pubmed-8588067 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85880672021-11-13 Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling Hajra, Sujoy Ghosh Liu, Careesa C. Fickling, Shaun D. Pawlowski, Gabriela M. Song, Xiaowei D’Arcy, Ryan C. N. Sensors (Basel) Article Background: Electroencephalography (EEG)-derived event-related potentials (ERPs) provide information about a variety of brain functions, but often suffer from low inherent signal-to-noise ratio (SNR). To overcome the low SNR, techniques that pool data from multiple sensors have been applied. However, such pooling implicitly assumes that the SNR among sensors is equal, which is not necessarily valid. This study presents a novel approach for signal pooling that accounts for differential SNR among sensors. Methods: The new technique involves pooling together signals from multiple EEG channels weighted by their respective SNRs relative to the overall SNR of all channels. We compared ERP responses derived using this new technique with those derived using both individual channels as well as traditional averaged-based channel pooling. The outcomes were evaluated in both simulated data and real data from healthy adult volunteers (n = 37). Responses corresponding to a range of ERP components indexing auditory sensation (N100), attention (P300) and language processing (N400) were evaluated. Results: Simulation results demonstrate that, compared to traditional pooling technique, the new SNR-weighted channel pooling technique improved ERP response effect size in cases of unequal noise among channels (p’s < 0.001). Similarly, results from real-world experimental data showed that the new technique resulted in significantly greater ERP effect sizes compared to either traditional pooling or individual channel approach for all three ERP components (p’s < 0.001). Furthermore, the new channel pooling approach also resulted in larger ERP signal amplitudes as well as greater differences among experimental conditions (p’s < 0.001). Conclusion: These results suggest that the new technique improves the capture of ERP responses relative to traditional techniques. As such, SNR-weighted channel pooling can further enable widespread applications of ERP techniques, especially those that require rapid assessments in noisy out-of-laboratory environments. MDPI 2021-10-31 /pmc/articles/PMC8588067/ /pubmed/34770564 http://dx.doi.org/10.3390/s21217258 Text en © 2021 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 Hajra, Sujoy Ghosh Liu, Careesa C. Fickling, Shaun D. Pawlowski, Gabriela M. Song, Xiaowei D’Arcy, Ryan C. N. Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling |
title | Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling |
title_full | Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling |
title_fullStr | Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling |
title_full_unstemmed | Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling |
title_short | Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling |
title_sort | event related potential signal capture can be enhanced through dynamic snr-weighted channel pooling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588067/ https://www.ncbi.nlm.nih.gov/pubmed/34770564 http://dx.doi.org/10.3390/s21217258 |
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