Interpretable ML for Seizure Prediction

This project focuses on interpretable machine learning for epileptic seizure prediction using biomedical time-series data. The goal is to build models that remain useful for prediction while being easier to inspect and reason about in clinical settings.

The work is aligned with my Master’s thesis at Malmö University and explores prototype-based deep learning, attention mechanisms, and clinically meaningful representation learning for seizure-prediction workflows.