EDGCN: An embedding-driven fusion framework for heterogeneity-aware motor imagery decoding

• EDGCN dynamically generates graph parameters for adaptive EEG decoding. • Spatio-temporal heterogeneity in MI-EEG is modeled via embedding-driven fusion. • Orthogonality-constrained kernels enhance representation diversity and robustness. • EDGCN achieves state-of-the-art accuracy and strong cross-subject generalization. Motor imagery electroencephalography (MI-EEG) captures neural activity associated with imagined motor tasks and has been widely applied in both basic neuroscience and clinical research. However, the intrinsic spatio-temporal heterogeneity of MI-EEG signals and pronounced inter-subject variability present major challenges for accurate decoding. Most existing deep learning methods rely on fixed architectures and shared parameters, which limits their ability to capture the complex, dynamic patterns driven by individual differences. To address these limitations, we propose an Embedding-Driven Graph Convolutional Network (EDGCN), which leverages a heterogeneity-aware spatio-temporal embedding fusion mechanism to adaptively generate graph convolutional kernel parameters from a shared embedding-driven parameter bank. Specifically, we design a Multi-Resolution Temporal Embedding (MRTE) strategy based on multi-resolution power spectral features and a Structure-Aware Spatial Embedding (SASE) mechanism that integrates both local and global connectivity structures. On this basis, we construct a heterogeneity-aware parameter generation mechanism based on Chebyshev graph convolution to effectively capture the spatiotemporal heterogeneity of EEG signals, with an orthogonality-constrained parameter space that enhances diversity and representational fusion. Experimental results demonstrate that the proposed model achieves superior classification accuracies of 86.50% and 90.14% on the BCIC-IV-2a and BCIC-IV-2b datasets, respectively, outperforming current state-of-the-art methods.