Title: Cross-Subject Integration of Multi-Region Neural Signals via Functional Embedding.
Primary area: applications to neuroscience & cognitive science
Keywords: Subject-agnostic representation learning, Contrastive learning, Functional embedding, Electrode functional identity, Intracranial neural recordings, Local field potentials, Multi-region modeling, Variable-channel inputs, Transformer time-series models, Zero-shot generalization, Few-shot adaptation, Neural decoding, Pretraining, Cross-subject aggregation

Abstract: Aggregating intracranial recordings across subjects is challenging since electrode count, placement, and covered regions vary widely. Spatial normalization methods like MNI coordinates offer a shared anatomical reference, but often fail to capture true functional similarity, particularly when localization is imprecise; even at matched anatomical coordinates, the targeted brain region and underlying neural dynamics  can differ substantially between individuals. We propose a scalable representation-learning framework that (i) learns a subject-agnostic functional identity for each electrode from multi-region local field potentials using a Siamese encoder with contrastive objectives, inducing an embedding geometry that is locality-sensitive to region-specific neural signatures, and (ii) tokenizes these embeddings for a transformer that models inter-regional relationships with a variable number of channels. We evaluate on a 20-subject dataset spanning basal ganglia–thalamic regions collected during flexible rest/movement periods with heterogeneous electrode layouts. The learned functional space supports accurate within-subject discrimination and forms clear, region-consistent clusters; it transfers zero-shot to unseen channels. The transformer, operating on functional tokens without subject-specific heads or supervision, captures cross-region dependencies and enables reconstruction of queried channels, providing a subject-agnostic backbone for downstream decoding. Together, these results indicate a path toward large-scale, cross-subject aggregation and pretraining for intracranial neural data where strict task structure and uniform sensor placement are unavailable.