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A Comparative Study of Different EEG Reference Choices for Event-Related Potentials Extracted by Independent Component Analysis

In the event-related potential (ERP) of scalp electroencephalography (EEG) studies, the vertex reference (Cz), linked mastoids or ears (LM), and average reference (AVG) are popular reference methods, and the reference electrode standardization technique (REST) is increasingly applied. Because scalp...

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Autores principales: Dong, Li, Liu, Xiaobo, Zhao, Lingling, Lai, Yongxiu, Gong, Diankun, Liu, Tiejun, Yao, Dezhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798171/
https://www.ncbi.nlm.nih.gov/pubmed/31680810
http://dx.doi.org/10.3389/fnins.2019.01068
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author Dong, Li
Liu, Xiaobo
Zhao, Lingling
Lai, Yongxiu
Gong, Diankun
Liu, Tiejun
Yao, Dezhong
author_facet Dong, Li
Liu, Xiaobo
Zhao, Lingling
Lai, Yongxiu
Gong, Diankun
Liu, Tiejun
Yao, Dezhong
author_sort Dong, Li
collection PubMed
description In the event-related potential (ERP) of scalp electroencephalography (EEG) studies, the vertex reference (Cz), linked mastoids or ears (LM), and average reference (AVG) are popular reference methods, and the reference electrode standardization technique (REST) is increasingly applied. Because scalp EEG recordings are considered as spatially degraded signals, independent component analysis (ICA) is a widely used data-driven method for obtaining ERPs by decomposing EEG data. However, the accurate estimation of the differences in ERP components extracted by ICA with different references remains unclear. In this study, we first provided formal descriptions of the above reference methods (Cz, LM, AVG, and REST) and ICA decomposition in ERP and then investigated the influences of different reference techniques on simulation and real EEG datasets. The results revealed that (1) the reference method did not change the peak amplitudes and latencies of relative ERPs corresponding to some IC time courses; (2) there were non-negligible effects of different reference methods on both temporal ERPs and spatial topographies of some ICs; and (3) compared to Cz, LM, and AR, considering both the performances of temporal ERPs and spatial topographies, the REST reference had overall superiority. These findings provide a recommended choice of REST for ICA analysis at the trial level and contribute to empirical investigations regarding the use of reference methods in ERP domains with ICA analysis.
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spelling pubmed-67981712019-11-01 A Comparative Study of Different EEG Reference Choices for Event-Related Potentials Extracted by Independent Component Analysis Dong, Li Liu, Xiaobo Zhao, Lingling Lai, Yongxiu Gong, Diankun Liu, Tiejun Yao, Dezhong Front Neurosci Neuroscience In the event-related potential (ERP) of scalp electroencephalography (EEG) studies, the vertex reference (Cz), linked mastoids or ears (LM), and average reference (AVG) are popular reference methods, and the reference electrode standardization technique (REST) is increasingly applied. Because scalp EEG recordings are considered as spatially degraded signals, independent component analysis (ICA) is a widely used data-driven method for obtaining ERPs by decomposing EEG data. However, the accurate estimation of the differences in ERP components extracted by ICA with different references remains unclear. In this study, we first provided formal descriptions of the above reference methods (Cz, LM, AVG, and REST) and ICA decomposition in ERP and then investigated the influences of different reference techniques on simulation and real EEG datasets. The results revealed that (1) the reference method did not change the peak amplitudes and latencies of relative ERPs corresponding to some IC time courses; (2) there were non-negligible effects of different reference methods on both temporal ERPs and spatial topographies of some ICs; and (3) compared to Cz, LM, and AR, considering both the performances of temporal ERPs and spatial topographies, the REST reference had overall superiority. These findings provide a recommended choice of REST for ICA analysis at the trial level and contribute to empirical investigations regarding the use of reference methods in ERP domains with ICA analysis. Frontiers Media S.A. 2019-10-11 /pmc/articles/PMC6798171/ /pubmed/31680810 http://dx.doi.org/10.3389/fnins.2019.01068 Text en Copyright © 2019 Dong, Liu, Zhao, Lai, Gong, Liu and Yao. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Dong, Li
Liu, Xiaobo
Zhao, Lingling
Lai, Yongxiu
Gong, Diankun
Liu, Tiejun
Yao, Dezhong
A Comparative Study of Different EEG Reference Choices for Event-Related Potentials Extracted by Independent Component Analysis
title A Comparative Study of Different EEG Reference Choices for Event-Related Potentials Extracted by Independent Component Analysis
title_full A Comparative Study of Different EEG Reference Choices for Event-Related Potentials Extracted by Independent Component Analysis
title_fullStr A Comparative Study of Different EEG Reference Choices for Event-Related Potentials Extracted by Independent Component Analysis
title_full_unstemmed A Comparative Study of Different EEG Reference Choices for Event-Related Potentials Extracted by Independent Component Analysis
title_short A Comparative Study of Different EEG Reference Choices for Event-Related Potentials Extracted by Independent Component Analysis
title_sort comparative study of different eeg reference choices for event-related potentials extracted by independent component analysis
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798171/
https://www.ncbi.nlm.nih.gov/pubmed/31680810
http://dx.doi.org/10.3389/fnins.2019.01068
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