ASA-STGCN: Adaptive Sparse Awareness-Spatiotemporal Graph Convolutional Network for Multi-Class Motor Imagery EEG Classification

Graph Convolutional Networks (GCNs) have shown promise in motor imagery electroencephalogram (EEG) signals classification by modeling spatial dynamics and brain connectivity. However, over-smoothing remains a challenge, leading to homogenized node features and reduced discrimination. To address this, we propose an Adaptive Sparse Awareness-Spatiotemporal Graph Convolutional Network (ASA-STGCN) that combines adaptive sparse graph convolution with attention mechanisms. Notably, a Graph Sparse Convolutional Network (GSCN) in the Adaptive Sparse Awareness Spatial Module (ASAM) enhances brain region feature selection, while the Graph Node Neighborhood Awareness Layer (GNNAL) applies self-attention to reinforce critical topological relationships. The Multi-scale Temporal Convolution Module (MTCM) captures both transient and sustained temporal dependencies. Experimental results achieve accuracies of 97.2%±3.4% (binary) and 83.6%±4.9% (four-class) on BCIC-IV-2a, 96.6%±3.1% (binary) on BCIC-III IVa, and 83.41%±4.3 (binary) on OpenBMI. Discussion confirms the model's effectiveness and its potential to support EEG-based neurorehabilitation and clinical brain computer interface applications.