Synthesizing Epileptic Seizures: Gaussian Processes for EEG Generation

2026-01-29

AI Summary

This work addresses the challenge of limited performance in epilepsy EEG analysis due to data scarcity and high annotation costs by proposing GP-EEG, a novel framework that integrates Gaussian process regression with a domain-adaptive variational autoencoder to generate high-quality, interpretable epileptic EEG signals. The method effectively captures the long-range dependencies, high dimensionality, and non-stationarity inherent in EEG data. Experimental results on two public datasets demonstrate that the synthesized signals closely resemble real EEG recordings in both qualitative and quantitative evaluations, and significantly enhance the performance of downstream classification tasks.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• epileptic EEG
• data scarcity
• seizure detection
• synthetic data generation
• non-stationary time series

Innovation

• Gaussian Processes
• EEG Generation
• Epileptic Seizure Synthesis
• Domain Adaptation
• Variational Autoencoder