Synthesizing epileptic seizures: Gaussian processes for EEG generation

Moutonnet, Nina; Corneck, Josh; Tobar, Felipe; Mandic, Danilo

doi:10.48550/arXiv.2601.21752

File(s)

2601.21752v1.pdf (36.53 MB)

Preprint version

Author(s)

Moutonnet, Nina

Corneck, Josh

Tobar, Felipe

Mandic, Danilo

Type

preprint

Abstract

Reliable seizure detection from electroencephalography (EEG) time series is a highpriority clinical goal, yet the acquisition cost and scarcity of labeled EEG data limit the performance of machine learning methods. This challenge is exacerbated by the long-range, highdimensional, and non-stationary nature of epileptic EEG recordings, which makes realistic data generation particularly difficult. In this work, we revisit Gaussian processes as a principled and interpretable foundation for modeling EEG dynamics, and propose a novel hierarchical framework, GP-EEG, for generating synthetic epileptic EEG recordings. At its core, our approach decomposes EEG signals into temporal segments modeled via Gaussian process regression, and integrates a domain-adaptation variational autoencoder. We validate the proposed method on two real-world, open-source epileptic EEG datasets. The synthetic EEG recordings generated by our model match real-world epileptic EEG both quantitatively and qualitatively, and can be used to augment training sets.