PanoMontage: Learning Montage Fields for Arbitrary-Location EEG Super-Resolution

High-density electroencephalography (EEG) captures fine-grained spatial patterns of brain activity, supporting the study of neural dynamics and the localization of abnormal activity, but its hardware and preparation requirements limit widespread use. EEG spatial super-resolution offers a way to recover dense scalp activity from sparse recordings, but most existing methods synthesize signals only at predefined electrode locations, preventing flexible reconstruction at arbitrary scalp locations. We introduce PanoMontage, which formulates EEG super-resolution as continuous montage field learning. Given sparse EEG observations, PanoMontage constructs a personalized montage field that combines physics-guided waveform estimates with learned spatial context, and queries this field to reconstruct EEG signals at arbitrary scalp locations. To enable montage field learning across heterogeneous electrode configurations, we develop a dynamic-montage self-supervised pretraining scheme that jointly optimizes field construction and query reconstruction on large-scale unlabeled EEG recordings. After fine-tuning on four downstream datasets, PanoMontage achieves the lowest NMSE in 12 of 14 dataset-density settings, with reductions of up to 36.5% relative to the strongest competing baseline in each setting. Controlled comparisons further show that the advantage of montage field learning increases as observations become sparser, highlighting its importance for accurate arbitrary-location EEG super-resolution.