FunctionalMap: Learning Data-Driven Coordinates for Cross-Subject Transformer Modeling of Heterogeneous Intracranial Recordings

Aggregating intracranial recordings across subjects is challenging because electrode count, placement, and regional coverage vary widely. Standard anatomical coordinates such as MNI provide a shared reference frame, but often fail to capture true functional similarity: even at matched anatomical coordinates, the targeted brain region and underlying neural dynamics can differ substantially between individuals. We propose FunctionalMap, a scalable representation-learning framework that learns a data-driven functional coordinate system for intracranial neural recordings. An encoder trained with contrastive objectives maps short local field potential segments into a subject-agnostic embedding space, inducing electrode identities that are locality-sensitive to region-specific neural signatures rather than anatomical location. These functional coordinates are then used as tokens for a transformer that models inter-regional relationships across variable channel sets. We evaluate this framework on a 20-subject basal ganglia–thalamic dataset collected during flexible rest/movement recording sessions with heterogeneous electrode layouts. The learned functional space forms clear, region-consistent clusters, supports within-subject discrimination, and transfers zero-shot to unseen channels. In cross-region LFP prediction, a single transformer using functional coordinates outperforms models using MNI coordinates or region labels, without subject-specific heads or fine-tuning. Performance improves with more training subjects; functional coordinates also improve spectral band-power prediction, and the masked-region result replicates with separately trained models on a public human cohort. On public mouse visual-behavior recordings, a trained functional encoder supports transformer modeling of pooled hit-versus-miss behavior prediction in unseen animals (zero-shot) and improves upon anatomical coordinates.