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...

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Autores principales:	Sujoy Ghosh Hajra, Careesa C. Liu, Shaun D. Fickling, Gabriela M. Pawlowski, Xiaowei Song, Ryan C. N. D’Arcy
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	EEG ERP neural signal processing signal augmentation signal to noise ratio channel pooling Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/5c349cbf54784f4e8941fcbc8cecee4b
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spelling	oai:doaj.org-article:5c349cbf54784f4e8941fcbc8cecee4b2021-11-11T19:13:22ZEvent Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling10.3390/s212172581424-8220https://doaj.org/article/5c349cbf54784f4e8941fcbc8cecee4b2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7258https://doaj.org/toc/1424-8220Background: 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 (<i>p</i>’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 (<i>p</i>’s < 0.001). Furthermore, the new channel pooling approach also resulted in larger ERP signal amplitudes as well as greater differences among experimental conditions (<i>p</i>’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.Sujoy Ghosh HajraCareesa C. LiuShaun D. FicklingGabriela M. PawlowskiXiaowei SongRyan C. N. D’ArcyMDPI AGarticleEEGERPneural signal processingsignal augmentationsignal to noise ratiochannel poolingChemical technologyTP1-1185ENSensors, Vol 21, Iss 7258, p 7258 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	EEG ERP neural signal processing signal augmentation signal to noise ratio channel pooling Chemical technology TP1-1185
spellingShingle	EEG ERP neural signal processing signal augmentation signal to noise ratio channel pooling Chemical technology TP1-1185 Sujoy Ghosh Hajra Careesa C. Liu Shaun D. Fickling Gabriela M. Pawlowski Xiaowei Song Ryan C. N. D’Arcy Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling
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 (<i>p</i>’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 (<i>p</i>’s < 0.001). Furthermore, the new channel pooling approach also resulted in larger ERP signal amplitudes as well as greater differences among experimental conditions (<i>p</i>’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	article
author	Sujoy Ghosh Hajra Careesa C. Liu Shaun D. Fickling Gabriela M. Pawlowski Xiaowei Song Ryan C. N. D’Arcy
author_facet	Sujoy Ghosh Hajra Careesa C. Liu Shaun D. Fickling Gabriela M. Pawlowski Xiaowei Song Ryan C. N. D’Arcy
author_sort	Sujoy Ghosh Hajra
title	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_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_sort	event related potential signal capture can be enhanced through dynamic snr-weighted channel pooling
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5c349cbf54784f4e8941fcbc8cecee4b
work_keys_str_mv	AT sujoyghoshhajra eventrelatedpotentialsignalcapturecanbeenhancedthroughdynamicsnrweightedchannelpooling AT careesacliu eventrelatedpotentialsignalcapturecanbeenhancedthroughdynamicsnrweightedchannelpooling AT shaundfickling eventrelatedpotentialsignalcapturecanbeenhancedthroughdynamicsnrweightedchannelpooling AT gabrielampawlowski eventrelatedpotentialsignalcapturecanbeenhancedthroughdynamicsnrweightedchannelpooling AT xiaoweisong eventrelatedpotentialsignalcapturecanbeenhancedthroughdynamicsnrweightedchannelpooling AT ryancndarcy eventrelatedpotentialsignalcapturecanbeenhancedthroughdynamicsnrweightedchannelpooling
_version_	1718431586267955200

Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling

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