DIVER-1: Scaling Intracranial EEG Foundation Models for Transferable Representations

Intracranial EEG (iEEG) provides direct, millisecond-scale recordings of human neural activity, but reusable representation learning is difficult because electrode layouts, anatomical coverage, referencing schemes, and recording conditions vary across patients and centers. We introduce DIVER-1, a self-supervised iEEG foundation model for variable-input recordings that combines any-variate electrode–time attention, spatio-temporal resampling, input-conditioned positional embeddings, and multi-domain masked reconstruction without assuming a fixed electrode montage. We pretrain two variants, DIVER-1-0.1s and DIVER-1-1s, on 5,310 hours of ECoG and SEEG spanning 352k channel-hours, roughly 54$\times$ the BrainTreeBank-based pretraining volume. We evaluate DIVER-1 on two held-out benchmarks: iMINDBench for naturalistic decoding and MAYO for seizure detection. DIVER-1-0.1s achieves the best results on both benchmarks — outperforming all prior foundation models and matching the strongest classical method on MAYO—despite being pretrained exclusively on task-free clinical monitoring recordings from external institutions. DIVER-1-1s achieves comparable performance with an order-of-magnitude smaller token budget, offering a practical option for compute-constrained settings. Finally, we present, to our knowledge, the first controlled, compute-aware scaling study of self-supervised iEEG pretraining, varying data scale, subject count, training duration, and model size up to 1.8B parameters. For masked reconstruction, we find that expanding unique recordings and training sufficiently long are more reliable scaling axes than increasing parameter count alone, highlighting the need for broader datasets and self-supervised objectives better aligned with downstream use. Code is available at [link](https://anonymous.4open.science/r/DIVER-1_update).