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.

翻译：从脑电图（EEG）时间序列中可靠地检测癫痫发作是一项高优先级的临床目标，然而，标记脑电图数据的获取成本和稀缺性限制了机器学习方法的性能。癫痫脑电图记录所具有的长程、高维和非平稳特性加剧了这一挑战，这使得生成逼真的数据尤为困难。在本工作中，我们重新审视 Gaussian processes，将其作为建模脑电图动态的一种原则性且可解释的基础，并提出了一种新颖的分层框架 \textit{GP-EEG}，用于生成合成的癫痫脑电图记录。我们的方法核心在于，将脑电图信号分解为通过 Gaussian process 回归建模的时间段，并集成了一个领域自适应变分自编码器。我们在两个真实世界的开源癫痫脑电图数据集上验证了所提出的方法。我们的模型生成的合成脑电图记录在定量和定性上都与真实世界的癫痫脑电图相匹配，并且可用于扩充训练集。