## 1 Introduction  
Epilepsy, a severe neurological disease that leads to recurrent seizures, affects more than 65 million people worldwide, with an incidence rate of 61.44 per 100,000 person-years [1, 2]. Although antiepileptic drugs can reduce clinical complications and mortality rates, 30% of patients are refractory to such drugs [3], thus urging the development of alternative treatments. The unpredictable nature of seizures increases the risk of injury and psychosocial disability, significantly affecting the patient's quality of life [4]. However, evidence suggests that specific alterations in brain dynamics can be observed before epileptic attacks [5]. This discovery spurred the interest of academic centers and medical companies in building devices to anticipate seizures, primarily by analyzing the electroencephalogram (EEG) [6, 7]. Monitoring devices would allow patients to avoid dangerous situations and plan the administration of preventive treatments, such as electrical stimulation or targeted drug delivery, with much greater precision. Seizure _prediction_ aims to anticipate an upcoming seizure before it clinically manifests. This task differs significantly from seizure _detection_, a simpler binary classification problem that requires discriminating between normal and seizure brain activity. However, predicting seizures using EEG analysis is challenging, as EEG manifestations vary widely between patients and even within the same patient. Ten years ago, the prototype of the first implanted seizure advisory system was tested in human patients [8]. The system could identify periods of low, moderate and high probability of seizures and was developed with precise target performance criteria, such as reaching a sensitivity of high probability warnings greater than 65% and an accuracy at least higher than the probability prediction of random events. Less invasive devices that rely on scalp EEG recordings in the channel or source space have also been proposed. One hypothesis is that functional connectivity patterns can reveal information about the dynamics of the epileptic brain, which can be used to predict the onset and locations of seizures [9]. In recent years, the increasing success of artificial intelligence techniques in clinical diagnosis [10] and disease forecasting [11] has revived the interest in leveraging machine learning for the challenging task of seizure prediction (for reviews, see [12; 13; 14]). One common approach involves extracting various descriptive features from EEG recordings and using them to train machine learning algorithms to identify time blocks proximal (e.g., 2 h, 1 h, or 30 min) to an upcoming seizure. These features include time- and frequency-based indexes, information theory measures, and sophisticated metrics derived from dynamical systems theory [15; 16]. For example, some studies extracted 22 linear univariate features from 6 EEG channels and achieved an average sensitivity of approximately 73% using classifiers such as support vector machines (SVMs) and artificial neural networks [17; 18]. A similar approach trained SVMs with a reduced set of bivariate features and achieved slightly better accuracy [19]. An alternative method based on the extraction of histogram bins combined with Gaussian mixture models reported an average sensitivity of 88% [20]. The application of machine learning techniques to predict seizures has thus shown great potential. However, concerns have been raised about the possibility of robustly detecting preictal states with respect to the reproducibility and statistical validation of these techniques [13]. For example, despite achieving impressive performance in various epilepsy research applications, follow-up validation studies have often shown that the reported findings may not accurately reflect the robustness of the methods [21]. This issue is especially evident when models are trained using small data sets, which is common in epilepsy research.