UniPopT: Where Does Intracranial EEG Help a Brain-Signal Foundation Model?

Intracranial EEG (iEEG) is often described as an untapped resource for brain-signal foundation models, yet whether it improves decoding has not been established: the one prior report of a gain from joint EEG–iEEG pre-training used a model that carries no electrode coordinates. We present UniPopT, a Population Transformer that ingests scalp EEG and iEEG through one interface: a frozen single-channel encoder turns every electrode into a token, a spherical-coordinate and modality positional encoding places it, and the aggregator is pre-trained on both modalities with next-segment and replaced-token objectives. On the official evaluation splits of two widely used clinical EEG corpora UniPopT improves on existing methods trained on the same recordings; on the external Brain4FMs benchmark it obtains the best mean rank on the scalp-EEG leaderboards and the top accuracy on the intracranial ones. Decomposing the gain on the same external benchmark, we find that the full model beats every ablated variant, that the gain enters through iEEG in the aggregator's pre-training corpus, and that the spherical encoding is what lets the model exploit electrode montages it never saw during pre-training. Beyond benchmark accuracy, we analyse how decoding scales with the amount of fine-tuning data per subject and show that most of the gain from additional electrodes arrives through the spherical coordinates.