Synthesizing Epileptic Seizures: Gaussian Processes for EEG Generation

TL;DR

This paper introduces GP-EEG, a hierarchical Gaussian process-based model for generating synthetic epileptic EEG data, addressing data scarcity and improving seizure detection models.

Contribution

It presents a novel hierarchical framework combining Gaussian processes and domain-adaptation autoencoders for realistic EEG synthesis.

Findings

Synthetic EEG matches real data quantitatively and qualitatively.

Generated data effectively augment training datasets.

Model validated on two real-world datasets.

Abstract

Reliable seizure detection from electroencephalography (EEG) time series is a high-priority 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, high-dimensional, 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, \textit{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.