Bridging Electrophysiology and Hemodynamics: Adapting EEG Foundation Models for EEG-fNIRS Representation Learning

Electroencephalography (EEG) has long served as a fundamental modality for decoding brain activity. Existing EEG foundation models learn transferable neural representations from large-scale electrophysiological recordings, but their unimodal design prevents them from leveraging the complementary hemodynamic information measured by functional near-infrared spectroscopy (fNIRS). Extending these models to multimodal EEG-fNIRS data is non-trivial, as the signals differ sharply in sensor layouts, temporal resolution, and physiological origins. To overcome these challenges, we propose EF-GO, a modular framework that adapts pretrained EEG foundation models for multimodal EEG-fNIRS representation learning without retraining the original EEG backbone. EF-GO first learns fNIRS-specific representations via self-supervised masked reconstruction, then employs Region-prior-guided Graph Aggregation to map heterogeneous EEG and fNIRS channel features into a shared, region-aware graph space. To align their distinct temporal dynamics, EF-GO incorporates a time-aware Neural Ordinary Differential Equation (Neural ODE) objective to capture lagged correspondences between electrophysiological and hemodynamic representations. Experiments across six downstream tasks with two pretrained EEG backbones show that EF-GO outperformed unimodal EEG baselines across most metrics and delivered performance competitive with task-specific EEG-fNIRS models. These results demonstrate the potential of adapting pretrained EEG models for multimodal neurophysiological representation learning.