1 Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada.
2 Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, USA.
3 Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, QC, Canada.
4 Neurophysiology Unit, Institute of Neurosurgery Dr. Asenjo, Santiago, Chile.
5 Department of Neurology, Duke University Medical Center, Durham, NC, USA.
6 Grenoble Institute Neurosciences, Inserm, U1216, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France.
7 Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada.
8 Multimodal Functional Imaging Lab, School of Health, Department of Physics, Concordia University, Montréal, QC, Canada.
9 Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
10 Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada. birgit.frauscher@duke.edu.
11 Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, USA. birgit.frauscher@duke.edu.
12 Department of Neurology, Duke University Medical Center, Durham, NC, USA. birgit.frauscher@duke.edu.

Stereo-electroencephalography (SEEG) is the gold standard to delineate surgical targets in focal drug-resistant epilepsy. SEEG uses electrodes placed directly into the brain to identify the seizure-onset zone (SOZ). However, its major constraint is limited brain coverage, potentially leading to misidentification of the 'true' SOZ. Here, we propose a framework to assess adequate SEEG sampling by coupling epileptic biomarkers with their spatial distribution and measuring the system's response to a perturbation of this coupling. We demonstrate that the system's response is strongest in well-sampled patients when virtually removing the measured SOZ. We then introduce the spatial perturbation map, a tool that enables qualitative assessment of the implantation coverage. Probability modelling reveals a higher likelihood of well-implanted SOZs in seizure-free patients or non-seizure free patients with incomplete SOZ resections, compared to non-seizure-free patients with complete resections. This highlights the framework's value in sparing patients from unsuccessful surgeries resulting from poor SEEG coverage.