Intracranial EEG (iEEG) is an indispensable method for pre-surgical planning in patients
with pharmacoresistant epilepsy. The current clinical standard for the identification of
surgical targets is grounded in the analysis of ictal data, while interictal recordings still
play a minor role in clinical diagnoses despite representing 99% of presurgical data.
This thesis introduces relative entropy as a new feature for the analysis of interictal
data to locate the epileptogenic zone, hence the surgical target. It provides analyses of
confounding variables such as antiseizure medication, states of vigilance, and proximity to
seizures and reveals that states of vigilance modulate feature values and distributions the
most. It further investigates novel features of very-fast ripples and ultra-fast ripples and
explores their behavior in different states of vigilance. Finally, a novel detection method
for ultra-fast oscillations (2-8kHz) was designed and implemented to allow quantitave
analyses of this novel phenomena.
Together, these results offer new features for the identification of the epileptogenic zone
and contribute to a deeper understanding of presurgical iEEG recordings by description
of different confounding factors and their influence on iEEG.