Title
A unified weighted minimum norm solution for the reference inverse problem in EEG.
Abstract
A well known problem in EEG recordings deals with the unknown potential of the reference electrode. In the last years several authors presented comparisons among the most popular solutions, the global conclusion being that the traditional Average Reference (AR) and the Reference Standardization Technique (REST) are the best approximations (Nunez, 2010; Kayser and Tenke, 2010; Liu et al., 2015; Chella et al., 2016). In this work we do not aim to further compare these techniques but to support the fact that both solutions can be derived from a general inverse problem formalism for reference estimation (Hu et al., 2019; Hu et al., 2018; Salido-Ruiz et al., 2011). Using the alternative approach of least squares, our findings are consistent with the theoretical findings in Hu et al. (2019) and Hu et al. (2018) showing that the AR is the minimum norm solution, while REST is a weighted minimum norm including some approximate propagation model. AR is thus a particular case of REST, which itself uses a particular formulation of the source estimation inverse problem. With a different derivation, we provide the additional powerful evidences to reinforce the cited findings.
Year
DOI
Venue
2019
10.1016/j.compbiomed.2019.103510
Computers in Biology and Medicine
Keywords
Field
DocType
EEG,Reference potential,Inverse problems
Least squares,Applied mathematics,Pattern recognition,Computer science,Minimum norm,Artificial intelligence,Inverse problem,Formalism (philosophy),Standardization,Electroencephalography
Journal
Volume
ISSN
Citations 
115
0010-4825
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Ricardo A Salido-Ruiz100.34
Radu Ranta2379.35
Gundars Korats352.88
Steven Le Cam400.34
Laurent Koessler5233.81
Valérie Louis-Dorr6709.35