## 1 Introduction  
Epilepsy is a common neurological disorder marked by frequent and unpredictable seizures that can profoundly impact a person's quality of life. Early and accurate seizure prediction is essential for improving patient outcomes by enabling timely intervention and enhancing overall safety. Electroencephalogram (EEG) signals, which capture the brain's electrical activity, have become central to the development of automated seizure detection and prediction systems. However, the highly nonstationary and complex nature of EEG data presents significant challenges in designing robust and generalizable predictive models. Recent advancements in machine learning and signal processing have substantially improved EEG-based epileptic seizure prediction models. [PERSON] et al. [1] introduced a patient-specific seizure prediction framework using a multichannel feedback capsule network (FB-CapsNet), which processes EEG signals in an end-to-end manner, eliminating the need for manual feature extraction. By leveraging feedback-enabled capsule structures, the model effectively captures complex spatial-temporal dependencies, offering high predictive accuracy and adaptability to individual EEG profiles. However, the model exhibits high computational complexity and limited generalizability, as it requires separate training for each patient. In contrast, [PERSON] et al. [2] proposed a generalized approach utilizing discrete wavelet transform (DWT) for feature extraction and support vector machines (SVM) for classification. Their method achieved an average prediction accuracy of 96.38% and anticipated seizures approximately 26.1 min before onset, without requiring patient-specific training. Nevertheless, reliance on a single dataset and sensitivity to preprocessing steps may impact its generalizability and robustness. [PERSON] et al. [3] introduced a hybrid deep learning model integrating autoencoders for unsupervised feature extraction with convolutional neural networks (CNNs) for classification. The model achieved 92.5% accuracy and demonstrated good generalization across datasets, though it remains slightly behind the best-performing models and still requires clinical validation. [PERSON] et al. [4] developed a vision transformer (ViT)-based model that processes EEG data by converting signals into image-like formats. This method benefits from self-attention mechanisms that extract patient-specific spatial features. While it offers strong predictive performance, the approach demands large volumes of patient-specific data and significant computational resources, limiting scalability. [PERSON] et al. [5] proposed a seizure prediction model combining dynamic multi-graph convolution with a channel-weighted transformer. This architecture captures intricate spatial and temporal relationships among EEG channels, resulting in enhanced prediction accuracy. However, it is computationally intensive and dependent on high-quality EEG data, which may not always be feasible in clinical settings. [PERSON] et al. [6] introduced a hybrid Cuckoo-Finch optimization algorithm for automatic EEG electrode selection and CNN hyperparameter tuning. Evaluated on the CHB-MIT and Siena datasets, the model achieved an accuracy of 97.76%. Despite its strong performance, the method's computational complexity and lack of testing in real-world IoT scenarios pose challenges for deployment. [PERSON] et al. [7] proposed a patient-specific approach combining Adder networks and supervised contrastive learning to improve energy efficiency and feature discrimination. The model demonstrated strong sensitivity and specificity while being computationally suitable for wearable devices. However, it heavily relies on patient-specific data and requires further validation in real-time, noisy environments. [PERSON] et al. [8] developed a Transformer-based model for seizure prediction that captures long-range temporal dependencies with minimal manual preprocessing.