Objective . Brain-computer interface (BCI) has demonstrated its effectiveness in epilepsy treatment and control. In a BCI-aided epilepsy treatment system, therapic electrical stimulus is delivered in response to the prediction of upcoming seizure onsets, therefore timely and accurate seizure prediction algorithm plays an important role. However, unlike typical signatures such as slow or sharp waves in ictal periods, the signal patterns in preictal periods are usually subtle, and highly individual-dependent. How to extract effective and robust preictal features is still a challenging problem. Approach . Most recently, graph convolutional neural network (GCNN) has demonstrated the strength in the electroencephalogram (EEG) and intracranial electroencephalogram (iEEG) signal modeling, due to its advantages in describing complex relationships among different EEG/iEEG regions. However, current GCNN models are not suitable for seizure prediction. The effectiveness of GCNN...

中文翻译：

---

在图卷积神经网络中学习图以进行稳健的癫痫发作预测。

目标。脑机接口（BCI）已证明其在癫痫治疗和控制中的有效性。在BCI辅助的癫痫治疗系统中，响应于即将发作的癫痫发作的预测而提供治疗性电刺激，因此及时准确的癫痫发作预测算法起着重要的作用。但是，不同于典型的信号，如发作期的慢波或尖波，发作期的信号模式通常是微妙的，并且高度依赖个体。如何提取有效和健壮的前期特征仍然是一个具有挑战性的问题。方法。最近，图卷积神经网络（GCNN）证明了脑电图（EEG）和颅内脑电图（iEEG）信号建模的优势，由于其在描述不同EEG / iEEG地区之间的复杂关系方面的优势。但是，当前的GCNN模型不适合癫痫发作预测。GCNN的功效...