Bridging scalp and intracranial EEG in BCI via pretrained neural representations and geometric constraint embedding
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. To address this challenge, this study proposes a unified data-and prior knowledge-driven framework for EEG-iEEG representational enhancement. Guided by the principle that "geometric structure dictates function", the fram...
| Authors | Yihang Dong, Changhong Jing, Shuqiang Wang |
|---|---|
| Primary category | q-bio.NC |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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