eeg-ieeg-bridge-bci - SKILL.md Agent Skill

name: eeg-ieeg-bridge-bci description: > Bridging scalp EEG and intracranial EEG (iEEG) in BCI via pretrained neural models. Maps non-invasive scalp EEG to iEEG-quality representations, enabling high-fidelity BCI without invasive implants. Uses pretrained models to learn the scalp-to-cortical mapping. Activation: EEG iEEG bridge, scalp to intracranial, BCI translation, non-invasive BCI, cortical reconstruction, EEG-to-iEEG, 脑电皮层映射, 无创BCI version: 1.0.0 metadata: hermes: source_paper: "Bridging scalp and intracranial EEG in BCI via pretrained neural models" arxiv_id: "2604.14202" tags: [eeg, ieeg, bci, translation, pretrained-models, non-invasive]

EEG-to-iEEG Bridge for BCI

Overview

Maps non-invasive scalp EEG signals to intracranial EEG (iEEG) quality representations using pretrained neural models. This enables high-fidelity BCI control without requiring invasive electrode implants.

Core Problem

Scalp EEG suffers from:

Low spatial resolution (smearing through skull)
Volume conduction artifacts
Limited frequency bandwidth
Poor signal-to-noise ratio

iEEG provides high-quality signals but requires surgery. This approach bridges the gap.

Methodology

Stage 1: Shared Representation Learning

Train on paired scalp-iEEG recordings
Learn a shared latent space preserving neural information
Use contrastive learning to align representations

Stage 2: Scalp-to-iEEG Translation

class EEG2iEEGBridge:
    def __init__(self, pretrained_encoder):
        self.encoder = pretrained_encoder  # frozen
        self.mapper = nn.Sequential(
            nn.Linear(latent_dim, hidden_dim),
            nn.GELU(),
            nn.Linear(hidden_dim, ieeg_dim)
        )
    
    def translate(self, scalp_eeg):
        latent = self.encoder(scalp_eeg)
        ieeg_repr = self.mapper(latent)
        return ieeg_repr

Stage 3: BCI Decoding

Use translated iEEG representations for downstream BCI tasks
Achieves near-iEEG decoding accuracy from scalp signals

Key Findings

Pretrained representations significantly improve translation quality
Temporal alignment between scalp and iEEG is critical
Certain brain regions (motor, visual) translate better than others

Applications

Non-invasive BCI with iEEG-level performance
Clinical monitoring without implants
Research requiring high-quality EEG from many subjects

Related Skills

eeg-foundation-models, copilot-assisted-second-thought-bci