Transfer function approaches for SEEG brain electrode interface applied to tissue classification

Mariana Mulinari Pinheiro Machado; Alina Voda; Gildas Besançon; Guillaume Becq; Philippe Kahane; Olivier David

doi:10.52846/stccj.2023.3.1.46

Authors

Mariana Mulinari Pinheiro Machado Univ. Grenoble Alpes, CNRS, Grenoble INP GIPSA-lab
Alina Voda Univ. Grenoble Alpes, CNRS, Grenoble INP GIPSA-lab
Gildas Besançon Univ. Grenoble Alpes, CNRS, Grenoble INP GIPSA-lab
Guillaume Becq Univ. Grenoble Alpes, CNRS, Grenoble INP GIPSA-lab
Philippe Kahane Univ. Grenoble Alpes, CHU Grenoble Alpes Grenoble Institut des Neurosciences, GIN
Olivier David Aix Marseille Univ., Inserm, INS Institut de Neurosciences des Syst`emes

DOI:

https://doi.org/10.52846/stccj.2023.3.1.46

Keywords:

SEEG, dynamical modelling, system identification, classification, clinical data

Abstract

This paper is about transfer function approaches for brain-electrode interface modelling in the context of StereoElectroEncephaloGraphy, and their possible use in tissue classification (between grey and white matter). Monopolar and bipolar configurations are first reviewed, giving rise to possible nonparametric and parametric identification methods, as well as related possible classification results (for identical tissues and distinct tissues at measurement points, respectively). A method combining both approaches is then proposed, so as to end up with a classification at each measurement point in any case. The proposed methodology is implemented with clinical data collected from a set of epileptic patients, confirming its interest by providing more than 70% of accuracy in the obtained results.

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