## 1 Introduction
More and more people are using unsupervised feature learning with unlabeled data, such as clustering, Gaussian mixture models, hidden Markov models, and autoencoders [22; 23]. Unsupervised learning does not rely on any labels and instead exploits the inherent structural properties of the data to perform relevant tasks. It can be applied during the recording of EEG signals, eliminating the need for data annotation and individual feature extraction methods for each patient. This technique has mainly been used in seizure detection and has achieved high sensitivity and specificity [22; 24; 25]. Currently, there are two main techniques utilized: autoencoders (AE) and deep convolutional generative adversarial networks (DCGAN) [26; 27; 28; 29; 30]. However, there is relatively limited research on successfully applying unsupervised learning to seizure prediction. In a study [26], the authors used unsupervised stacked autoencoders (SAE) combined with prior knowledge to extract features and train a SVM classifier, achieving a sensitivity of 95% and a false positive rate (FPR) of 0.06/h. Unfortunately, this result was only tested on intracranial EEG signals from two patients, and the performance impact of the features extracted from SAE could not be determined due to the use of prior knowledge in feature design. In another study [31], researchers tested their method on the CHB-MIT dataset, using a deep convolutional autoencoder to extract features and input them into a bidirectional long-short-term memory model, ultimately achieving a sensitivity of 94.6% and an FPR of 0.04/h. However, this method had a seizure prediction horizon (SPH) of 0, which means there was no reserved time for clinical intervention, resulting in a lack of practical utility. In response to the given questions, this paper proposes a predictive model called WGAN-GP-Bi-LSTM. The model combines the Short-Time Fourier Transform (STFT), a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), and a semi-supervised epileptic seizure prediction algorithm based on the Bi-LSTM classification network model. Firstly, the time series data of the EEG signals is transformed into a two-dimensional matrix with time and frequency axes using the STFT. Secondly, the WGAN-GP is used as an unsupervised feature learning model. This model uses Earth-Mover (EM) distance as a measure instead of Jensen-Shannon (JS) divergence to overcome training instability and model collapse issues, ensuring the richness of generated samples [32; 33]. Lastly, a Bi-LSTM classification model is employed as the backend classifier, using a small amount of labeled STFT spectrograms to guide the prediction task. This classification model efficiently captures temporal information in EEG signals, thereby improving prediction performance [34]. The proposed methodology is validated on the publicly available CHB-MIT dataset of pediatric epilepsy data. The performance of the proposed method is evaluated using metrics such as AUC, sensitivity, and specificity. The contributions of this paper are as follows:  
1. Introducing the WGAN-GP model for unsupervised feature learning of epileptic EEG signals. Although WGAN-GP is commonly used in machine learning models, its application in EEG signal feature extraction, especially for epileptic seizure prediction, is limited. 2. Using semi-supervised learning methods to compensate for the deficiencies of fully supervised and unsupervised learning methods. 3. Validation of the proposed method using the CHB-MIT dataset, demonstrating its effectiveness through measurements of AUC, sensitivity, and specificity.