Cortical alignment improves eeg model accuracy across montages
CortexBridge: Cortical Alignment of EEG Montages for Foundation Models
Artificial Intelligence
Summary
EEG devices measure brain activity using different layouts of electrodes, which makes it hard for computer models trained on one layout to work well with others. The authors propose CortexBridge, a small addition that aligns these different layouts to a common brain map, letting existing models understand data from varied electrode setups. Testing on several brain-computer interface tasks showed CortexBridge generally improved accuracy. The authors also found that the method can reveal meaningful brain activity patterns depending on the task.
What this means in practice
- •For bci system developers: Improve the compatibility of pretrained EEG models with different electrode layouts in brain-computer interface applications.
- •For medical device engineers: Enhance EEG signal analysis tools to work with diverse clinical electrode configurations by aligning them on a shared cortical map.
Authors
Jiazhen Hong, Xiaotian Zhou, Zihao Ding, Kailong Wang, Yu Wu
Abstract
Electroencephalography (EEG) foundation models are often pretrained with a fixed channel vocabulary or a limited set of montages, making transfer difficult when electrode layouts change. We propose CortexBridge, a lightweight adapter that combines EEG features with electrode and atlas coordinates to map arbitrary montages into a shared cortical latent space. Evaluated with three frozen foundation models on five brain-computer interface (BCI) datasets from the Mother of All BCI Benchmarks (MOABB), CortexBridge improves performance in 13 of 15 evaluations. The gains in balanced accuracy average 0.80% for EEGPT, 0.70% for LaBraM, and 3.26% for CBraMod, with a maximum gain of 13.02% on 12-class steady-state visual evoked potential (SSVEP) classification. Visualizations of the learned atlas representations reveal task-dependent spatial patterns, with SSVEP showing a more concentrated representation in the Yeo Visual network than auditory P300. These results establish cortical alignment as a learnable and anatomically grounded routing mechanism from heterogeneous EEG montages to pretrained foundation models.