Bridging scalp and intracranial EEG in BCI via pretrained neural representations and geometric constraint embedding

TL;DR

This paper introduces a novel framework that enhances EEG signals by integrating neural representations and geometric constraints, aiming to bridge the gap with intracranial EEG for improved brain-computer interface performance.

Contribution

It proposes a unified data-driven and prior knowledge-driven approach that models neural signal propagation and synthesizes enhanced EEG signals using a diffusion process.

Findings

Enhanced EEG signals recover neural activity patterns lost during propagation

The framework improves the signal-to-noise ratio and spatial resolution of EEG signals

Performance of BCIs can be improved by better modeling neural signal transmission

Abstract

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 framework maps static cortical anatomy onto dynamic constraints governing neural signal propagation and integrates general-purpose neural representations extracted by a pre-trained large EEG model to explicitly model signal transmission through the brain. Enhanced EEG signals are then synthesized via a multidimensional representation diffusion process. Numerous experimental results demonstrate that the generated enhanced EEG signals effectively recover the neural activity patterns lost during propagation through the brain. This finding indicates that the performance ceiling of BCIs is constrained not only by acquisition hardware but also by the depth to which the generative model resolves the mechanisms of neural signal propagation. Collectively, the proposed framework provides a viable pathway toward acquiring high-fidelity neural signals at low cost.