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

arXiv:2512.19097v3 Announce Type: replace Abstract: 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 fix

Source: arXiv cs.LG — read the full report at the original publisher.

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