Beyond Layout Compatibility: Understanding and Reusing Spatial Priors in EEG Foundation Models

EEG foundation models aim to transfer knowledge from large-scale pre-training across heterogeneous acquisition settings, where positional representations link signal content to electrode location. Yet accommodating an electrode layout does not ensure effective use of pre-trained positional knowledge. We theoretically analyze how positional parameterization shapes electrode-specific learning, motivating the reuse of independently parameterized electrode vectors. Building on this analysis, we propose RADAR, a framework centered on Spatial Positional Remapping (SPR). SPR uses pre-trained electrode vectors as spatial anchors to initialize target encodings through geometric remapping, with backbone-specific interfaces enabling cross-model reuse. RADAR further incorporates Spatio-Temporal Regularization (STR) and Distributionally Robust Optimization (DRO) to examine whether positional reuse complements data- and optimization-level improvements. Across multiple EEG decoding tasks and foundation models, RADAR improves downstream performance, while SPR outperforms alternative positional encodings in most evaluated settings. Further analyses show that the learned spatial information remains useful outside its original electrode configurations and model architecture, with SPR providing additional gains under matched auxiliary training conditions. Taken together, these results show that layout-compatible models can still benefit from better use of positional knowledge, highlighting spatial prior reuse as a way to translate large-scale pre-training into greater downstream gains.