Data Augmentation for Seizure Prediction with Generative Diffusion Model

Understand

Data augmentation (DA) can significantly strengthen the electroencephalogram (EEG)-based seizure prediction methods.

• However, existing DA approaches are just the linear transformations of original data and cannot explore the feature space to increase diversity effectively.
• Therefore, we propose a novel diffusion-based DA method called DiffEEG.
• DiffEEG can fully explore data distribution and generate samples with high diversity, offering extra information to classifiers.