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PHASE is a physiology-guided hierarchical foundation model for intracranial EEG that uses physiological characteristics as explicit learning targets alongside masked latent prediction, setting a new state of the art on five Omni-iEEG clinical tasks and generalizing to unseen institutions.
The paper proposes MAPA, a self-supervised pretraining method with spatial encodings for neural interfaces, which reduces the labeled data needed for accurate brain-computer interface decoding across subjects.