Title: DIVER-1 : Deep Integration of Vast Electrophysiological Recordings at Scale
Primary area: applications to neuroscience & cognitive science
Keywords: neuroscience, EEG, Foundation Model, self-supervised learning

Abstract: Electrophysiological signals such as EEG and iEEG are central to neuroscience, brain–computer interfaces, and clinical applications, yet existing foundation models remain limited in scale despite clear evidence that scaling improves performance. We introduce DIVER-1, a family of multimodal electrophysiology foundation models trained on the largest and most diverse corpus to date—59k hours (1.6M channel-hours) from over 17k subjects—and scaled up to 1.82B parameters. We present the first systematic scaling law analysis for this domain, showing that they follow data-constrained scaling laws: smaller models trained for extended epochs consistently outperform larger models trained briefly. This behavior contrasts both with language/vision scaling trends and with prior electrophysiology foundation models that emphasized model size over training duration. To achieve strong performance, we design architectural innovations including any-variate attention, sliding temporal conditional positional encoding, and multi-domain reconstruction. DIVER-1 sets new state-of-the-art or competitive results across diverse neural decoding tasks and establishes concrete guidelines for efficient scaling and resource allocation in electrophysiology foundation model development.