LBTAR: Learnable Brain-Topology-Guided Autoregressive Channel Recovery for EEG Spatial Super-Resolution

High-density electroencephalography (EEG) captures rich spatial information, but its acquisition and deployment costs are relatively high. Low-density EEG is easier to acquire, but its limited spatial coverage makes it difficult to characterize regional brain activity at a fine spatial scale. Therefore, reconstructing high-density EEG from low-density observations has become an important problem in EEG spatial super-resolution. Existing methods mostly adopt one-shot reconstruction or fixed-order autoregressive recovery, which limits their ability to adapt to sample-specific channel dependencies and dynamically evolving recovery states. To address this limitation, we propose LBTAR, which replaces predefined recovery paths with a state-dependent sequential recovery process. LBTAR treats EEG channels as nodes and integrates channel-to-region assignments, inter-regional adjacency relationships, and electrode spatial information to construct a brain-region-structure-guided channel topology. It further jointly considers the currently visible context, the remaining candidate set, the dynamic channel topology, and channel-level prediction reliability to adaptively select the next group of channels for recovery. At each step, the mask-conditioned reconstruction network produces full-channel predictions, while only the selected channel signals are written back to progressively update the recovery state. Channel-level uncertainty is further used to adjust subsequent topology and recovery decisions, thereby mitigating error propagation. Experiments on three public datasets, namely SEED, SEED-IV, and Localize-MI, show that LBTAR achieves favorable time-domain reconstruction performance and frequency-domain consistency across multiple spatial super-resolution ratios, while maintaining relatively low computational overhead and stable performance at high missing-channel ratios. Ablation experiments further validate the effectiveness of dynamic recovery, brain-region structural priors, and uncertainty-aware modeling.