CortexBridge: Cortical Alignment of EEG Montages for Foundation Models

Jiazhen Hong; Xiaotian Zhou; Zihao Ding; Kailong Wang; Yu Wu

cs.AIOct 1, 2026

CortexBridge: Cortical Alignment of EEG Montages for Foundation Models

Authors: Jiazhen Hong, Xiaotian Zhou, Zihao Ding, Kailong Wang, Yu Wu

Organizations: Department of Electrical and Computer Engineering, Rutgers University Piscataway, NJ 08854, USA

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.

Figures & tables

Figure 1 : Common interfaces for heterogeneous EEG montages. Fixed-layout models assume a consistent channel arrangement, vocabulary-driven models rely on predefined channel identities, and coordinate-driven models accommodate unseen electrode locations but remain in scalp space. CortexBridge instead maps arbitrary montages into a shared atlas-indexed cortical space.

Figure 2 : Overview of CortexBridge. The adapter maps montage-specific EEG to atlas ROIs, models cortical interactions in a latent space, and projects the result to the input of a frozen foundation model.

	EEGPT		CBraMod		LaBraM
Dataset	Raw	+CortexBridge	Raw	+CortexBridge	Raw	+CortexBridge
BNCI2014-008	55.49	54.53 $\downarrow$ 0.96	64.92	65.68 $\uparrow$ 0.76	50.43	52.16 $\uparrow$ 1.72
BNCI2015-003	55.84	56.57 $\uparrow$ 0.72	57.43	58.18 $\uparrow$ 0.75	50.62	51.48 $\uparrow$ 0.86
Nakanishi2015	61.67	65.06 $\uparrow$ 3.40	56.48	69.51 $\uparrow$ 13.02	15.56	16.23 $\uparrow$ 0.68
Liu2024	51.88	52.50 $\uparrow$ 0.62	54.35	55.20 $\uparrow$ 0.85	49.56	49.89 $\uparrow$ 0.33
Huebner2017	83.74	83.94 $\uparrow$ 0.20	78.11	79.00 $\uparrow$ 0.89	51.63	51.52 $\downarrow$ 0.11

Table 1: Balanced accuracy (%) under subject-grouped three-fold cross-validation. Blue cells use CortexBridge; arrows show the change from the corresponding raw interface. Bold marks the best result in each row.

Panel of Figure 3 — Figure 3 : Atlas representations learned by CBraMod with CortexBridge. ROI-output RMS is normalized within each task using the 5th and 98th percentiles and clipped to $[0,1]$ ; colors indicate relative spatial distributions.

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Apr 25, 2026, cs.LGJ/K move · Enter open · S save

Channel Adaptation for EEG Foundation Models: A Systematic Benchmark Across Architectures, Tasks, and Training Regimes

Kuntal Kokate, Bruno Aristimunha, Dung Truong, Arnaud Delorme

Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA · Yneuro, Paris, France · Centre de Recherche Cerveau et Cognition, CNRS, Université Paul Sabatier, Toulouse, France

Electroencephalography Foundation Models Parameter-Efficient Adaptation Text-Only Adaptation Frozen Encoder Channel Modeling

Open paper AskPDF arXiv

Scaling EEG foundation models requires pooling data across heterogeneous electrode montages, a prerequisite both for larger pretraining corpora and for downstream deployment. We present the first systematic comparison of four channel adaptation methods (Conv1d projection, spherical spline interpolation (SSI), source-space decomposition, and Riemannian re-centering) across five pretrained EEG foundation models (5M--157M parameters), five downstream tasks, and two training regimes with 10--15 random seeds each. We find that rigid-montage models (BENDR, Neuro-GPT) require external adaptation, while flexible models (EEGPT, CBraMod) match or exceed it natively when fine-tuned but benefit from external methods under frozen-encoder deployment. A probe-SFT asymmetry exists: external adaptation can cause severe negative transfer during fine-tuning of flexible models. The optimal method is architecture-dependent (Conv1d for BENDR, SSI/Riemannian for Neuro-GPT, source-space decomposition for depression detection), and 5M-parameter CBraMod outperforms models up to 31

\times

larger on 4/5 datasets, consistent with independent findings that compact EEG-specific architectures can match larger models.

Abstract

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