###### Abstract  
With the increasing research of deep learning in the EEG field, it becomes more and more important to fully extract the characteristics of EEG signals. Traditional EEG signal classification prediction neither considers the topological structure between the electrodes of the signal collection device nor the data structure of the Euclidean space to accurately reflect the interaction between signals. Graph neural networks can effectively extract features of non-Euclidean spatial data. Therefore, this paper proposes a feature selection method for epilepsy EEG classification based on graph convolutional neural networks (GCNs) and long short-term memory (LSTM) cells. While enriching the input of LSTM, it also makes full use of the information hidden in the EEG signals. In the automatic detection of epileptic seizures based on neural networks, due to the strong non-stationarity and large background noise of the EEG signal, the analysis and processing of the EEG signal has always been a challenging research. Therefore, experiments were conducted using the preprocessed Boston Children's Hospital epilepsy EEG dataset, and input it into the GCN-LSTM model for deep feature extraction. The GCN network built by the graph convolution layer learns spatial features, then LSTM extracts sequence information, and the final prediction is performed by fully connected and softmax layers. The introduced method has been experimentally proven to be effective in improving the accuracy of epileptic EEG seizure detection. Experimental results show that the average accuracy of binary classification on the CHB-MIT dataset is 99.39%, and the average accuracy of ternary classification is 98.69%. EEG signal; epilepsy seizure prediction; graph convolutional neural network; long- and short-term memory cells; GCN-LSTM +
Footnote †: journal: Medical  
of the most common neurological diseases in the world. Currently, there are approximately 50 million epilepsy patients worldwide, of whom approximately 15 million are children [3]. The clinical manifestations of epilepsy are diverse and complex due to differences in the origin and conduction methods of abnormal electrical activity in the brain. Recurrent epileptic seizures may have a lasting negative impact on the patient's mental and cognitive functions and even threaten the patient's life. When employing traditional deep learning models for EEG-based seizure detection, there exist several limitations. On one hand, these models often fail to adequately extract spatial information along the channel dimension. For instance, convolutional neural networks (CNNs) lose partial spatial information during the process of transforming the collected data into a two-dimensional format. They may even completely discard spatial information of the channel dimension altogether, as exemplified by recurrent neural networks (RNNs) [4], which simply concatenate the data into one-dimensional representations. To address these issues, an increasing body of neuroscience research has underscored the importance of considering the dynamics of brain networks [5; 6]. Some studies have consequently focused on extracting graphically-related features between channels, such as adjacency matrices and distance correlations. Given that the data acquired by each electrode possess a graph-structured relationship on the scalp, the graph structure information inherent in each channel is fed into graph neural networks (GNNs) [7] as part of the feature set. This approach can be viewed as a structural characterization of brainwaves.