specificity of 98.0% and an AUC-ROC value of 0.95 for classifying interictal epileptiform discharges. This underscores the model’s efficacy in addressing the complexities of EEG signal analysis.

This project focuses on predicting epileptic seizures using EEG signals and ensemble learning techniques. It aims to provide accurate and timely predictions to help individuals with epilepsy manage ...

Objective: We aimed at studying the hemodynamic response (HR) to Interictal Epileptic Discharges (IEDs) using patient-specific and prolonged simultaneous ElectroEncephaloGraphy (EEG) and functional ...

EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD ...

Background: Ongoing or recurrent seizure activity without prominent motor features is a common burden in neurological critical care patients and people with epilepsy during ICU stays. Continuous EEG ...