Single-trial ERP component analysis using a spatiotemporal LCMV beamformer

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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Single-trial ERP component analysis using a spatiotemporal LCMV beamformer. / Van Vliet, Marijn; Chumerin, Nikolay; De Deyne, Simon; Wiersema, Jan Roelf; Fias, Wim; Storms, Gerrit; Van Hulle, Marc M.

julkaisussa: IEEE Transactions on Biomedical Engineering, Vuosikerta 63, Nro 1, 2468588, 01.01.2016, s. 55-66.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Harvard

Van Vliet, M, Chumerin, N, De Deyne, S, Wiersema, JR, Fias, W, Storms, G & Van Hulle, MM 2016, 'Single-trial ERP component analysis using a spatiotemporal LCMV beamformer', IEEE Transactions on Biomedical Engineering, Vuosikerta. 63, Nro 1, 2468588, Sivut 55-66. https://doi.org/10.1109/TBME.2015.2468588

APA

Van Vliet, M., Chumerin, N., De Deyne, S., Wiersema, J. R., Fias, W., Storms, G., & Van Hulle, M. M. (2016). Single-trial ERP component analysis using a spatiotemporal LCMV beamformer. IEEE Transactions on Biomedical Engineering, 63(1), 55-66. [2468588]. https://doi.org/10.1109/TBME.2015.2468588

Vancouver

Author

Van Vliet, Marijn ; Chumerin, Nikolay ; De Deyne, Simon ; Wiersema, Jan Roelf ; Fias, Wim ; Storms, Gerrit ; Van Hulle, Marc M. / Single-trial ERP component analysis using a spatiotemporal LCMV beamformer. Julkaisussa: IEEE Transactions on Biomedical Engineering. 2016 ; Vuosikerta 63, Nro 1. Sivut 55-66.

Bibtex - Lataa

@article{76ba339add924feda8cf877a7535bd4f,
title = "Single-trial ERP component analysis using a spatiotemporal LCMV beamformer",
abstract = "Goal: For statistical analysis of event-related potentials (ERPs), there are convincing arguments against averaging across stimuli or subjects. Multivariate filters can be used to isolate an ERP component of interest without the averaging procedure. However, we would like to have certainty that the output of the filter accurately represents the component. Methods: We extended the linearly constrained minimum variance (LCMV) beamformer, which is traditionally used as a spatial filter for source localization, to be a flexible spatiotemporal filter for estimating the amplitude of ERP components in sensor space. In a comparison study on both simulated and real data, we demonstrated the strengths and weaknesses of the beamformer as well as a range of supervised learning approaches. Results: In the context of measuring the amplitude of a specific ERP component on a single-trial basis, we found that the spatiotemporal LCMV beamformer is a filter that accurately captures the component of interest, even in the presence of both structured noise (e.g., other overlapping ERP components) and unstructured noise (e.g., ongoing brain activity and sensor noise). Conclusion: The spatiotemporal LCMV beamformer method provides an accurate and intuitive way to conduct analysis of a known ERP component, without averaging across trials or subjects. Significance: Eliminating averaging allows us to test more detailed hypotheses and apply more powerful statistical models. For example, it allows the usage of multilevel regression models that can incorporate between subject/stimulus variation as random effects, test multiple effects simultaneously, and control confounding effects by partial regression.",
keywords = "Beamformer, Brain models, Electroencephalography, Event-related potentials, Multivariate analysis",
author = "{Van Vliet}, Marijn and Nikolay Chumerin and {De Deyne}, Simon and Wiersema, {Jan Roelf} and Wim Fias and Gerrit Storms and {Van Hulle}, {Marc M.}",
year = "2016",
month = "1",
day = "1",
doi = "10.1109/TBME.2015.2468588",
language = "English",
volume = "63",
pages = "55--66",
journal = "IEEE Transactions on Biomedical Engineering",
issn = "0018-9294",
number = "1",

}

RIS - Lataa

TY - JOUR

T1 - Single-trial ERP component analysis using a spatiotemporal LCMV beamformer

AU - Van Vliet, Marijn

AU - Chumerin, Nikolay

AU - De Deyne, Simon

AU - Wiersema, Jan Roelf

AU - Fias, Wim

AU - Storms, Gerrit

AU - Van Hulle, Marc M.

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Goal: For statistical analysis of event-related potentials (ERPs), there are convincing arguments against averaging across stimuli or subjects. Multivariate filters can be used to isolate an ERP component of interest without the averaging procedure. However, we would like to have certainty that the output of the filter accurately represents the component. Methods: We extended the linearly constrained minimum variance (LCMV) beamformer, which is traditionally used as a spatial filter for source localization, to be a flexible spatiotemporal filter for estimating the amplitude of ERP components in sensor space. In a comparison study on both simulated and real data, we demonstrated the strengths and weaknesses of the beamformer as well as a range of supervised learning approaches. Results: In the context of measuring the amplitude of a specific ERP component on a single-trial basis, we found that the spatiotemporal LCMV beamformer is a filter that accurately captures the component of interest, even in the presence of both structured noise (e.g., other overlapping ERP components) and unstructured noise (e.g., ongoing brain activity and sensor noise). Conclusion: The spatiotemporal LCMV beamformer method provides an accurate and intuitive way to conduct analysis of a known ERP component, without averaging across trials or subjects. Significance: Eliminating averaging allows us to test more detailed hypotheses and apply more powerful statistical models. For example, it allows the usage of multilevel regression models that can incorporate between subject/stimulus variation as random effects, test multiple effects simultaneously, and control confounding effects by partial regression.

AB - Goal: For statistical analysis of event-related potentials (ERPs), there are convincing arguments against averaging across stimuli or subjects. Multivariate filters can be used to isolate an ERP component of interest without the averaging procedure. However, we would like to have certainty that the output of the filter accurately represents the component. Methods: We extended the linearly constrained minimum variance (LCMV) beamformer, which is traditionally used as a spatial filter for source localization, to be a flexible spatiotemporal filter for estimating the amplitude of ERP components in sensor space. In a comparison study on both simulated and real data, we demonstrated the strengths and weaknesses of the beamformer as well as a range of supervised learning approaches. Results: In the context of measuring the amplitude of a specific ERP component on a single-trial basis, we found that the spatiotemporal LCMV beamformer is a filter that accurately captures the component of interest, even in the presence of both structured noise (e.g., other overlapping ERP components) and unstructured noise (e.g., ongoing brain activity and sensor noise). Conclusion: The spatiotemporal LCMV beamformer method provides an accurate and intuitive way to conduct analysis of a known ERP component, without averaging across trials or subjects. Significance: Eliminating averaging allows us to test more detailed hypotheses and apply more powerful statistical models. For example, it allows the usage of multilevel regression models that can incorporate between subject/stimulus variation as random effects, test multiple effects simultaneously, and control confounding effects by partial regression.

KW - Beamformer

KW - Brain models

KW - Electroencephalography

KW - Event-related potentials

KW - Multivariate analysis

UR - http://www.scopus.com/inward/record.url?scp=84956885637&partnerID=8YFLogxK

U2 - 10.1109/TBME.2015.2468588

DO - 10.1109/TBME.2015.2468588

M3 - Article

C2 - 26285053

AN - SCOPUS:84956885637

VL - 63

SP - 55

EP - 66

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

IS - 1

M1 - 2468588

ER -

ID: 30057566