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A New Refinement-Free Preconditioner for the Symmetric Formulation in Electroencephalography

Authors :
Giunzioni, Viviana
G., John E. Ortiz
Merlini, Adrien
Adrian, Simon B.
Andriulli, Francesco P.
Publication Year :
2022

Abstract

Widely employed for the accurate solution of the electroencephalography forward problem, the symmetric formulation gives rise to a first kind, ill-conditioned operator ill-suited for complex modelling scenarios. This work presents a novel preconditioning strategy based on an accurate spectral analysis of the operators involved which, differently from other Calder\'on-based approaches, does not necessitate the barycentric refinement of the primal mesh (i.e., no dual matrix is required). The discretization of the new formulation gives rise to a well-conditioned, symmetric, positive-definite system matrix, which can be efficiently solved via fast iterative techniques. Numerical results for both canonical and realistic head models validate the effectiveness of the proposed formulation.

Subjects

Subjects :
Mathematics - Numerical Analysis

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2204.06857
Document Type :
Working Paper