A Novel State Space Model with Dynamic Graphic Neural Network for EEG Event Detection

Electroencephalography (EEG) is a widely used physiological signal to obtain information of brain activity, and its automatic detection holds significant research importance, which saves doctors' time, improves detection efficiency and accuracy. However, current automatic detection studies face several challenges: large EEG data volumes require substantial time and space for data reading and model training; EEG's long-term dependencies test the temporal feature extraction capabilities of models; and the dynamic changes in brain activity and the non-Euclidean spatial structure between electrodes complicate the acquisition of spatial information. The proposed method uses range-EEG (rEEG) to extract time-frequency features from EEG to reduce data volume and resource consumption. Additionally, the next-generation state-space model Mamba is utilized as a temporal feature extractor to effectively capture the temporal information in EEG data. To address the limitations of state space models (SSMs) in spatial feature extraction, Mamba is combined with Dynamic Graph Neural Networks, creating an efficient model called DG-Mamba for EEG event detection. Testing on seizure detection and sleep stage classification tasks showed that the proposed method improved training speed by 10 times and reduced memory usage to less than one-seventh of the original data while maintaining superior performance. On the TUSZ dataset, DG-Mamba achieved an AUROC of 0.931 for seizure detection and in the sleep stage classification task, the proposed model surpassed all baselines.