## 4 Discussion  
This study presents a feature extraction method for epilepsy prediction using an unsupervised training model. While it reduces sensitivity by 3.19% compared to supervised learning, this unsupervised feature extraction method can help minimize the costly and time-consuming task of labeling EEG signals. The unlabeled EEG signals are used to train the WGAN-GP, and the trained discriminator is used as a feature extractor input for the Bi-LSTM classifier for epilepsy prediction. Compared to supervised learning, semi-supervised learning methods still have a performance gap in epilepsy prediction. Some studies suggest that this gap can be improved by increasing the training size [28]. Oversampling the inputs during the training of the adversarial network helps fill in the data gaps of patients and improves overall epilepsy prediction performance. Therefore, we can infer that the more EEG data available, the higher the prediction accuracy. The advantage of using unsupervised feature extraction is that data recording and feature extraction can be carried out simultaneously without requiring additional effort from clinical doctors. This research demonstrates that utilizing the Wasserstein distance and gradient penalty can further improve the stability of the unsupervised feature learning model and enhance the quality of time-frequency image generation. Figures 11 and 12 illustrate the advantages of WGAN-GP through the comparison of loss function changes and training convergence performance. As shown in the evaluation results in the third column of Tables 2 and 3, DCGAN, as an unsupervised feature learning model, often exhibits unusually low evaluation metrics, such as a sensitivity of only 21.33% for patient 3 and an AUC value of only 28.52% for patient 2. Such data indicates that DCGAN has a certain bias during the learning process, which is one of the manifestations of unstable model training and cannot be applied in practical clinical settings. The use of WGAN-GP can effectively improve the above issues while enhancing various performance indicators, resulting in more balanced results. Table 4 presents a performance comparison of the proposed method in this study with other related literature on seizure prediction. It lists the results of both fully supervised and semi-supervised seizure prediction methods using the same dataset and cases. Figure 12: Comparison of the range of the three evaluation metrics. [PERSON] et al. [26] achieved a sensitivity of 95% and an FPR below 0.06/h using an unsupervised feature extraction method based on autoencoders. They utilized stacked autoencoders for unsupervised feature extraction and incorporated prior knowledge into the design of the features. However, it is difficult to determine the contribution of this unsupervised method to predictive performance. Additionally, this method was only tested on two patients with intracranial EEG signals. [PERSON] et al. [28] used DCGAN for unsupervised training to generate high-level feature extractors, which were then connected to a two-layer, fully connected network for seizure prediction. This method employed unsupervised learning techniques for feature extraction and was the first to apply them to seizure prediction. However, there are issues with gradient vanishing and instability during the training of the DCGAN model. Furthermore, the fully connected network layer has limited learning capabilities for time-frequency features, which is why there is room for improvement in predictive performance [34]. The proposed method in this paper addresses the shortcomings of DCGAN and improves upon the existing semi-supervised prediction model in terms of AUC, sensitivity, and specificity metrics. Additionally, for each evaluation metric, a corresponding analysis of the range of poor values was conducted, as shown in Figure 12. Comparing the proposed model in this study with the fully supervised training CNN [19] and the semi-supervised training DCGAN [28], the proposed model demonstrated lower ranges of poor values in most metrics.