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Communication Dans Un Congrès Année : 2022

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

Résumé

Widely employed for the accurate solution of the electroencephalography forward problem, the symmetric formu- lation 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 Calderón-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.

Dates et versions

hal-03942661 , version 1 (17-01-2023)

Identifiants

Citer

Viviana Giunzioni, John Ortiz G., Adrien Merlini, Simon Adrian, Francesco Andriulli. A New Refinement-Free Preconditioner for the Symmetric Formulation in Electroencephalography. AP-S/USNC-URSI 2022: IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, Jul 2022, Denver, United States. pp.513-514, ⟨10.1109/AP-S/USNC-URSI47032.2022.9887114⟩. ⟨hal-03942661⟩
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