arXiv:2605.10337v2 Announce Type: replace-cross Abstract: Intracranial electrocorticography (ECoG) offers high signal-to-noise access to cortical activity for brain-computer interfaces, yet limited per-patient data has led most prior work to rely on small, subject-specific decoders that neglect information shared across patients. We investigate whether large pretrained scalp-EEG foundation models (EEG FMs) can be adapted to ECoG, enabling cross-patient learning and competitive decoding performance while calibrating to a held-out patient in 10-30 minutes on a single GPU. We introduce CORTEG, a cross-modality transfer framework that combines a pretrained EEG FM backbone, an electrode-aware KNNSoftFourier spatial adapter, a dual-stream tokenizer for low-frequency and high-gamma activity, and a leave-one-subject-out fine-tuning strategy. We evaluate CORTEG on two challenging regression tasks: public finger-trajectory regression ($n=9$) and private audio-envelope regression ($n=16$). CORTEG achieves state-of-the-art mean performance on both tasks, achieving the highest mean correlation among the compared methods on the public finger benchmark and larger, statistically significant gains on the audio task and in low-data-per-patient calibration. We further validate CORTEG on the public BrainTreebank stereo-EEG classification tasks, where it also performs strongly. Furthermore, CORTEG outperforms the compared intracranial-EEG foundation models across the continuous regression tasks. Feature analyses align with neurophysiology, and latent representations capture low-dimensional finger-movement structure. Together, these results provide systematic evidence that scalp-EEG pretraining can be repurposed across intracranial recording modalities and tasks, enabling data-efficient neural decoders that can adapt to new patients.