Dynamic Functional Connectivity Neural Network for Epileptic Seizure Prediction Using Multi-Channel EEG Signal

Epilepsy, one of the world's most common neurological diseases, impacts over 1% of the global population. Accurate early prediction of epileptic seizure has a great influence on epileptic patients' lives and attracted extensive attention. However, existing methods do not fully consider the complexity of multi-channel electroencephalogram (EEG) signal, which is the most common measurement for epileptic seizures. In this letter, we propose a Dynamic Functional Connectivity neural Network (DynFCNet) for epileptic seizure prediction. The proposed DynFCNet can discover dynamic brain functional connections and generate dynamic functional connectivity graphs, as well as extract non-Euclidean features from multi-channel EEG signals by Graph Convolutional Network (GCN). To take Euclidean features into consideration, Convolutional Neural Network (CNN) as one of the branches is also employed. Further, we incorporated both intra-group loss and inter-group loss to enhance our DynFCNet. Extensive experiments were implemented on a public multi-channel EEG dataset (CHB-MIT). The results confirm that our proposal outperforms the other competitors.