🤖 AI Summary
This study addresses the limitations of existing head electrical models in accurately and efficiently capturing the frequency-dependent properties and capacitive effects of brain tissue. The authors propose a lumped-parameter equivalent circuit model based on a three-shell (brain, skull, scalp) geometry, which explicitly incorporates the dispersive electromagnetic characteristics of tissues through radial and tangential RC branches, thereby integrating frequency-dependent conductivity and permittivity. Formulated under the electroquasistatic approximation, the model is validated across multiple geometries and frequencies using semi-analytical solutions based on spherical harmonics. Results demonstrate that neglecting dispersion and capacitive pathways leads to significant overestimation of scalp potentials, whereas the proposed model achieves excellent agreement with reference solutions, offering both high accuracy and broad applicability.
📝 Abstract
In this work, we present a compact surrogate circuit for electro-quasi-static (EQS) head modeling. A three-shell geometry (brain, skull, scalp) is considered, and each layer is modeled through radial and tangential pathways, implemented as RC branches. Frequency-dependent tissue conductivity and permittivity are mapped into dispersive resistive and capacitive elements. The model is validated against a semi-analytical spherical-harmonics reference solution over multiple geometrical configurations and operating frequencies, demonstrating good agreement. Neglecting dispersion and capacitive pathways can lead to an overestimation of scalp potentials over the considered frequency range, highlighting the need for dispersive RC circuit modeling.