Geometry- and Relation-Aware Diffusion for EEG Spatial Super-Resolution

Recent electroencephalography (EEG) spatial super-resolution (SR) methods, while showing improved quality by either directly predicting missing signals from visible channels or adapting latent diffusion-based generative modeling, often lack awareness of physiological spatial structure, thereby constraining spatial generation performance. To address this, we reformulate EEG spatial SR as a structured multi-modal conditional generation problem and introduce TopoDiff, a geometry- and relation-aware diffusion framework. Inspired by how human experts interpret spatial EEG patterns, TopoDiff conditions generation on sparse EEG signals together with two complementary structured views: topology-aware image embeddings derived from EEG topographic representations, which provide global geometric context, and dynamic channel-relation graphs, which encode inter-electrode relationships that evolve with temporal dynamics. This design yields a spatially grounded EEG spatial super-resolution framework with consistent performance improvements. Across multiple EEG datasets spanning diverse applications, including SEED/SEED-IV for emotion recognition, PhysioNet motor imagery (MI/MM), and TUSZ for seizure detection, our method achieves substantial gains in generation fidelity and leads to notable improvements in downstream EEG task performance.