OBJECTIVE: The present study aimed to identify various distinguishing features for use in the accurate classification of stereoelectroencephalography (SEEG) channels based on high-frequency oscillations (HFOs) inside and outside the epileptogenic zon...
OBJECTIVE: Normal interictal [ F]FDG-PET can be predicted from the corresponding T1w MRI with Generative Adversarial Networks (GANs). A technique we call SIPCOM (Subtraction Interictal PET Co-registered to MRI) can then be used to compare epilepsy pa...
OBJECTIVE: Invasive video-electroencephalography (iVEEG) is the gold standard for evaluation of refractory temporal lobe epilepsy before second stage resective surgery (SSRS). Traditionally, the presumed seizure onset zone (SOZ) has been covered with...
OBJECTIVE: To evaluate the performance of a deep learning model for hippocampal sclerosis classification on the clinical dataset and suggest plausible visual interpretation for the model prediction.