## 4 Discussion  
In traditional methods, researchers usually use manually constructed features to extract the time domain, frequency domain, and nonlinear features of EEG signals. We proposed a new method that can better use temporal, spectral, and spatial information for extraction. First, the spectrum of 18 channel EEG segments is obtained by STFT, and then, the time, frequency and channel information from the spectrum are analyzed by the step-wise, frequency-wise, and channel-wise encoders in the transformer tower, respectively. When combined with the gated unit, the features of time, frequency, and spatial direction are fused to realize the classification of EEG signals. The analysis results show that our model has better performance and more accurate classification results. The CHB-MIT dataset used in this study has also been used for performance evaluation in other studies. [PERSON] et al. [12] combined STFT and CNN to automatically extract and classify the time-frequency features of EEG signals, and in this instance, the prediction sensitivity was 81.2% and FPR was 0.16/h. [PERSON] et al. [45] extracted eight time-based features and predicted seizures through multivariate statistical process control (MSPC), and obtained 88.89% sensitivity and 0.39/h FPR. [PERSON] et al. [46] proposed an end-to-end deep learning solution based on CNN. The overall sensitivity and FPR for scalp EEG data were 98.8% and 0.074/h, respectively. [PERSON] et al. [47] proposed a novel framework of multi-view convolutional gated recurrent network (Mv-CGRN) and embedded the attention mechanism in Mv-CGRN, and determined the best feature combination of each patient by adaptively adjusting the weight parameters, achieving an average sensitivity of 94.50% and an average FPR of 0.118/h. [PERSON] et al. [48] used a binary single-dimensional convolutional neural network (BSDCNN) to predict seizures with a sensitivity of 88.89% and an FPR of 0.39 per hour. [PERSON] et al. [49] used a simple CNN model to classify the correlation matrix obtained by calculating the Pearson correlation coefficient to distinguish the preictal states from the interictal ones and obtained a sensitivity of 92.9%. Table 5 shows a comparison of the results for our method with the classification algorithms from the above literature. From the comparative results, our method does not require the complex process of manually constructing features and it has a lower FPR and higher sensitivity. Although the CNN network proposed by [46] had higher sensitivity, this study only evaluated EEG data of seven patients without good generalization. Therefore, from the perspective of overall performance, our model is more effective at the epilepsy prediction task. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Year** & **Authors** & **Feature** & **Classifier** & **No. of Patients** & **No. of Seizures** & **Sen 1** & **FPR (h\({}^{-1}\))** & **SPH** \\ \hline
2018 & Truong & STFT & CNN & 13 & 64 & 81.2 & 0.16 & 5 min \\ \hline
2019 & Rukhsar & Eight temporal-based features & MSPC & 10 & 90 & 88.89 & 0.39 & - \\ \hline
2020 & Xu & - & CNN & 7 & 45 & 98.8 & 0.074 & 5 min \\ \hline
2020 & Tang &
\begin{tabular}{c} L-DFA, \\ relative band energy, \\ synchronization modularity \\ \end{tabular} & Mv-CGRNi & 24 & 140 & 94.5 & 0.118 & - \\ \hline
2020 & Zhao & - & BSDCNN & 6 & - & 94.69 & 0.095 & 5 min \\ \hline
2021 & Zhang & \
\begin{tabular}{c} Pearson correlation \\ coefficient matrices. \\ \end{tabular} & CNN & 19 & - & 92.9 & - & 15 min \\ \hline \multirow{3}{*}{-} & This & \multirow{3}{*}{STFT} & Three & \multirow{3}{*}{21} & \multirow{3}{*}{111} & \multirow{3}{*}{96.01} & \multirow{3}{*}{0.047} & \multirow{3}{*}{3 min} \\  & work & STFT &
\begin{tabular}{c} three \\ tower \\ transformer \\ \end{tabular} & & & & & & \\ \hline \hline \end{tabular} \({}^{1}\)SPH, seizure prediction horizon.  
\end{table} 
Table 5: The comparison of the results of this work with other reported methods. In this work, the proposed model has several limitations.