ELAI-SGCN: An explainable lightweight adaptive information-perceiving spiking graph convolutional network for EEG-based emotion recognition

Background:Emotion recognition is increasingly essential for diagnosing mental disorders like depression and anxiety. Electroencephalography (EEG) is widely adopted for this purpose due to its high temporal resolution and non-invasive nature. However, existing EEG-based models often neglect the brain's dynamic neural connectivity, inadequately modeling spatial topology, and rely on extensive redundant data, increasing computational complexity and limiting performance. Methods: To address these limitations, we propose ELAI-SGCN, a lightweight and explainable framework for EEG analysis. ELAI-SGCN employs a trainable spiking encoder to transform raw EEG signals into sparse spike-based representations, preserving critical temporal dynamics in an event-driven manner to provide critical information for the subsequent Spiking Neural Network model. Simultaneously, the graph convolution module adaptively models inter-regional connectivity through efficient spike-based operations, enabling interpretable and resource-efficient EEG analysis. Results:We validated ELAI-SGCN on the DEAP and SEED emotion recognition datasets. On the DEAP dataset, the model achieved classification accuracies of 87.08 % for valence and 89.96 % for arousal, with only 60.48 K parameters and a computational cost of 0.36 M FLOPs. On the SEED dataset, it reached 94.63 % accuracy for three-class emotion recognition with 99.8 % fewer parameters and a more than 250-fold decrease in FLOPs compared to Dynamic Graph Convolutional Neural Networks. Conclusion:ELAI-SGCN introduces a novel spiking-based dynamic graph convolutional framework that enables efficient and interpretable modeling of EEG spatiotemporal dynamics. ELAI-SGCN outperformed most existing approaches in both accuracy and computational efficiency, demonstrating its suitability for lightweight, real-time EEG-based emotion recognition. Its lightweight design supports deployment on bedside clinical devices, offering a promising solution for emotion recognition and laying a foundation for next-generation intelligent psychological assessment systems with broad clinical potential.