Novel signal processing approaches for cortico-cortical evoked potentials from stereo-electroencephalography to improve seizure onset zone localization in drug-resistant epilepsy

Drug-resistant epilepsy (DRE) remains a major clinical challenge, with many patients continuing to experience seizures despite advances in surgical interventions. Accurate delineation of the seizure onset zone (SOZ) is essential for optimizing surgical outcomes, yet current methods relying on electroencephalography, imaging, and invasive recordings are often limited by network-level complexity and inter-patient variability. Cortico-cortical evoked potentials (CCEPs) derived from stereo-electroencephalography (SEEG) provide valuable insights into brain connectivity and have been investigated as potential markers of epileptogenic tissue. Traditional analyses have focused on early and late negative deflections (N1/N2), but these components suffer from limited sensitivity, specificity, and generalizability across patients and cortical regions. In this study, we propose an advanced signal processing framework that moves beyond fixed windows and simple peak amplitudes. By extracting a broader set of features—including energy, line length, entropy, and Root Mean Square metrics—we aim to more reliably distinguish epileptogenic from non-epileptogenic regions. This approach emphasizes individualized, data-driven characterization of evoked responses to refine surgical planning. Ultimately, our framework seeks to reduce unnecessary resections, improve precision in SOZ localization, and enhance outcomes for patients with DRE.