Applications of SEEG brain-electrode interface modelling to electrical parameters identification and tissue classification

This paper inspects dynamical modelling for brain-electrode interface in the context of StereoElectroEncephaloGraphic (SEEG) recordings using electrodes directly implanted into brain tissue. Considering a physical-based non-integer-order transfer function modelling approach, it is first emphasized how it can be usable for tissue classification (between grey and white matters) near each SEEG contact. In addition, it is shown how the model parameters can also provide more insights on electrical properties in the areas where measurements are collected. Validating identification and classification results are finally presented for clinical data, the former providing estimates of resistivity and capacitivity-related coefficients, and the latter showing more than 70% of accuracy.

I. Introduction

StereoElectroEcephaloGraphy (SEEG) considered in this paper in short consists in recording neural activity from electrodes inserted into the brain of severe epileptic patients with refractory focal epilepsies, each electrode bearing a series of contacts (see Figure 1). Fig. 1.

SEEG principle.