eeg-ieeg-bridge-bci

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