A Lumped RC Equivalent Circuit Model of Head Tissues in sub-MHz Frequency Regimes

2026-05-28

AI Summary

This study addresses the high computational cost of conventional electromagnetic simulations, which hinders rapid prototyping and circuit integration for neural sensing and stimulation systems. The authors propose a minimal lumped RC equivalent circuit model that accurately captures the electrical characteristics of a three-layer spherical head model below 50 kHz. Built upon the electroquasistatic approximation, the model incorporates complex conductivity to simultaneously account for tissue dispersion and displacement current effects—a first in low-frequency neurophysiological modeling. Validation against semi-analytical solutions and scalp potential simulations driven by dipole sources demonstrates high fidelity across varying skull thicknesses and dipole eccentricities. The resulting model achieves an optimal balance of accuracy, simplicity, and compatibility with integrated circuit implementation.

Abstract

Accurate modeling of electric potential and current distribution in head tissues is crucial for the design and evaluation of neuro-sensing and neuro-stimulation systems operating in the sub megahertz frequency range. Numerical methods are widely employed in electromagnetic simulations, however their computational cost can limit their applicability to rapid prototyping, real-time simulations, and circuit-level integration. In this work, we introduce a lumped RC equivalent circuit model that reproduces the electrical behavior of a canonical three-layer spherical head geometry over a frequency range up to 50 kHz. The model accounts for frequency-dependent tissue conductivity and permittivity to capture dispersive effects, employing complex conductivity in the electro-quasi-static (EQS) regime. The circuit topology uses a minimal set of impedance elements in order to represent the essential mechanisms of electric signal propagation. Validation was performed using a dipolar brain source configuration for scalp voltage peak estimation, showing close agreement with semi-analytical solutions across different skull thicknesses and dipole eccentricities. In addition, the impact of tissue dispersion and displacement current inclusion on the model accuracy was quantitatively assessed, highlighting their contribution to the overall fidelity of the proposed approach.

Problem

• head tissues
• sub-MHz frequency
• electric potential modeling
• neuro-sensing
• computational cost

Innovation

• lumped RC model
• head tissue modeling
• frequency dispersion
• electro-quasi-static
• neuro-sensing