神经科学与认知科学
突破级
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CORTEG investigates whether pretrained scalp-EEG foundation models can be adapted to intracranial ECoG decoding, reducing the need for extensive per-patient calibration.
The method combines a pretrained EEG backbone, electrode-aware spatial adaptation, dual-stream tokenization for low-frequency and high-gamma activity, and leave-one-subject-out fine-tuning.
The reported results show competitive or improved decoding on finger trajectory and audio envelope tasks, especially in low-data calibration settings.
For BCI and NeuroAI, the paper is useful because it treats cross-modality transfer as a deployment problem for patient adaptation, not merely as another representation-learning benchmark.