## 2 Related Work  
With the continuous development of AI technology and research exploring AI applications [10,11], deep learning has achieved great success in areas such as imaging, videos, speech, and text. Deep learning combines the two steps of feature extraction and classification in traditional machine learning. Usually, the more training data, the better learning performance can be obtained, and it is also gradually applied to EEG spike detection. As the first deep learning model applied to EEG epileptic seizure prediction, the convolutional neural network (CNN) [12,13] is groundbreaking for subsequent research work. In the study of EEG signals, the main work can be divided into three parts: data preprocessing, feature extraction, and feature classification [14]. In the data preprocessing part, the main work focuses on data filtering and normalization. The main function of filtering is to eliminate noise mainly coming from power line signals and the surrounding environment. In addition, the most critical technology is feature extraction and feature classification. Currently, there have been some studies on EEG feature extraction and classification. After the EEG signal is normalized, feature extraction can be performed from multiple angles of the EEG signal, including the time domain, frequency domain, and graph. After feature extraction, the feature data can be input into the classification model. Different models have different performance in processing features. Among them, recurrent neural networks have higher processing capabilities for time series information, and convolutional neural networks have higher processing capabilities for Euclidean space data. Graph neural networks are good at processing non-Euclidean spatial data structures. [PERSON] et al. [15,16] employed Linear Frequency Cepstral Coefficients (LFCCs) in various experiments, utilizing a linear frequency scale that represents an improvement over the classical Mel Frequency Cepstral Coefficients (MFCCs). MFCCs, commonly used for extracting frequency content of audio signals, have undergone adjustments in some studies and have been applied in the context of EEG seizure detection. Researchers [17,18] employed EEG signals to compute spectrograms via short-time Fourier transform (STFT) or power spectral density (PSD) for seizure identification tasks. [PERSON] [19] and [PERSON] [20], among others, utilized Continuous Wavelet Transform (CWT) or Discrete Wavelet Transform (DWT) to extract frequency-domain features of EEG signals, characterizing them in terms of power, energy, etc. With respect to individual and age-related variations in EEG data, certain studies [21] normalized the raw EEG signals to the interval [0, 1], whereas others [22] adopted Z-score normalization to set the mean of the data to zero and the variance to one, enhancing the robustness of models employed in subsequent seizure detection tasks. In the research work of [PERSON] et al. [23], Hurst exponential and Autoregressive Moving Average (ARMA) features were extracted from EEG signals and configured for the LSTM architecture with a single-layer memory cell and a double-layer memory cell. After normalizing the features, the dual-layer LSTM approach provided the highest accuracy and proved to be computationally efficient on GPUs. In Experiment [24], an effective framework based on deep spatiotemporal neural networks, known as ConvLSTM, was developed for detecting seizures from EEG signals. The proposed model commenced by selecting standard 19-channel EEG data, resampling them at 256 Hz, and subsequently dividing these signals into three time frames. Subsequently, the segmented data were fed as input to the ConvLSTM model to distinguish epileptic patients from normal subjects. [PERSON] et al. [25] employed DWT analysis with a DB4 mother wavelet to decompose raw EEG signal data. Following feature extraction and selection, 12 eigenvalues were chosen as inputs to an SVM model for classifying healthy controls, seizure-free, and seizure-active subjects. [PERSON] et al.