Unified Bayesian Estimator of EEG Reference at Infinity: rREST (Regularized Reference Electrode Standardization Technique)

Frontiers in Neuroscience
Shiang HuPedro Valdés-Sosa

Abstract

The choice of reference for the electroencephalogram (EEG) is a long-lasting unsolved issue resulting in inconsistent usages and endless debates. Currently, both the average reference (AR) and the reference electrode standardization technique (REST) are two primary, apparently irreconcilable contenders. We propose a theoretical framework to resolve this reference issue by formulating both (a) estimation of potentials at infinity, and (b) determination of the reference, as a unified Bayesian linear inverse problem, which can be solved by maximum a posterior estimation. We find that AR and REST are very particular cases of this unified framework: AR results from biophysically non-informative prior; while REST utilizes the prior based on the EEG generative model. To allow for simultaneous denoising and reference estimation, we develop the regularized versions of AR and REST, named rAR and rREST, respectively. Both depend on a regularization parameter that is the noise to signal variance ratio. Traditional and new estimators are evaluated with this framework, by both simulations and analysis of real resting EEGs. Toward this end, we leverage the MRI and EEG data from 89 subjects which participated in the Cuban Human Brain Mapping P...Continue Reading

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Citations

May 1, 2019·Brain Topography·Dezhong YaoPedro A Valdés Sosa
Apr 12, 2019·Brain Topography·Shiang HuPedro A Valdes-Sosa
Dec 19, 2020·Frontiers in Neuroscience·Jorge Bosch-BayardPedro A Valdes-Sosa
Sep 7, 2019·NeuroImage·Nima Bigdely-ShamloKay Robbins
Dec 31, 2021·International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology·Pavel PradoAgustín Ibáñez

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Methods Mentioned

BETA
imaging techniques

Software Mentioned

rAR
CIVET
REST
rREST
AR

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