ELAI-SGCN：EEGベースの感情認識のための説明可能な軽量適応情報知覚型スパイク畳み込みグラフネットワーク
Jingxin Liu1, Zikai Song1, Xihang Qiu1
1Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Ministry of Education, Beijing, 100081, China; School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China.
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.
