Repeat-Calibrated Neural Representations for Cross-Subject Generalization in Stereoelectroencephalography

Model–brain alignment within recorded individuals does not establish transfer across people with different electrode layouts or across auditory domains. We introduce the Repeated Auditory Stereoelectroencephalography Dataset (RASD) and Repeat-Calibrated Neural Fields (RCNF) to investigate how response reliability and spatial aggregation affect such generalization. RASD records 30 pediatric and adolescent participants listening to one story and six music excerpts, each scheduled three times, linking 2,052 signal-valid contacts to stimulus events and providing 2,050 matched contact coordinates. RCNF uses training-repeat consistency to calibrate contacts before coordinate-based, participant-balanced aggregation, constructing a fixed-dimensional representation for a shared predictor. Four tasks evaluate held-participant prediction on new repetitions, reduced source coverage, and bidirectional story–music transfer; other participants supply neural responses to the test content. Across 21 participants and 161 auditory contacts, calibration improves all four endpoints over uniform reliability weighting after correction for 16 comparisons, with absolute gains of .00069–.00128. Temporal history improves three tasks; the full spatial representation has no corrected advantage over either spatial control. All aligned endpoints exceed time-shifted references. Together, RASD and RCNF make repeat reliability an explicit component of neural representation design, providing a data and method foundation for predictors that can be reused across people and auditory materials.