Rethinking Self-Supervised Learning for Intracranial EEG: Electrode-Origin Prediction

Intracranial EEG (iEEG) foundation models aim to learn general neural representations that transfer across subjects, tasks, and institutions. While existing models pursue this goal using objectives adapted from other modalities, we design the pretraining target around iEEG's persistent implanted electrodes. Our model learns through electrode-origin prediction: it identifies which training electrode produced each channel's waveform across changing neural activity. Its shallow encoder, comprising one main Transformer block and a lightweight local-context module, achieves state-of-the-art overall iMindBench performance with frozen features and linear readouts, exceeding the prior foundation-model leader by approximately 1.7 AUROC percentage points. Separate models exceed that leader's published mean using ten minutes of unique neural recordings (0.41% of its pretraining duration) or 71k transferred encoder parameters (0.36% of its encoder size). Matched ablations show stronger transfer from electrode-origin prediction than from the tested reconstruction objectives under the same architecture. Scaling analyses show aggregate gains with more data and larger models, but the gains vary by task. These results suggest that objective design, not scale alone, matters for iEEG foundation models. Code: https://anonymous.4open.science/r/puppet-0926/README.md.