TGSD: Topology-Guided State-Space Diffusion Framework for EEG Spatial Super-Resolution
Low-density EEG is more suitable for wearable and IoT-based brain sensing, but sparse electrode sampling often lacks sufficient spatial information to characterize cross-regional neural activity. EEG spatial super-resolution aims to recover dense-channel EEG from sparse recordings, yet remains challenging because channel missingness typically occurs at the whole-channel level, spatiotemporal dependencies over the full electrode layout are often underexplored, and the mapping from sparse to dense signals is inherently ambiguous. To address these issues, we propose TGSD, a topology-guided state-space diffusion framework for EEG spatial super-resolution. TGSD first employs a Hierarchical Spa...
| Authors | Zijian Kang, Weiming Zeng, Yueyang Li et al. |
|---|---|
| Primary category | eess.SP |
| Published | 22 May 2026 |
| Last updated at source | 4 June 2026 |
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