AI RESEARCH
Bridging scalp and intracranial EEG in BCI via pretrained neural representations and geometric constraint embedding
arXiv CS.AI
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ArXi:2604.14202v1 Announce Type: cross Electroencephalography (EEG) has become one of the key modalities underpinning brain-computer interfaces (BCIs) due to its high temporal resolution, rapid responsiveness, non-invasiveness, low cost, and portability. However, EEG signals are substantially inferior to intracranial EEG (iEEG) in signal-to-noise ratio and local spatial resolution, whereas iEEG suffers from extremely limited clinical accessibility owing to its invasive nature, hindering widespread application.