Learning Neurovascular Codes for EEG-to-fMRI Synthesis

In this paper, we introduce **Neurovascular Codes** as shared EEG representations for fMRI generation. We first train a separate EEG encoder to predict each fMRI signal, learning target-specific temporal and spectral features from the same EEG window. After training, we freeze the encoders and project their concatenated hidden features into a shared neurovascular code. Conditioned on this code, *stochastic interpolant* transforms Gaussian noise into a vector of fMRI signals, allowing different responses to be sampled for the same EEG window. We evaluate our approach on eight public simultaneous EEG-fMRI datasets, including four resting-state and four task-based datasets with 61,646 volume-level pairs. Under within-subject and held-out-subject evaluation, our model improves average Pearson correlation over the strongest encoder baselines by 19.1% and 19.8%, respectively. The corresponding improvements over the strongest generator baselines are 18.8% and 47.9%. Beyond reconstruction accuracy, we also evaluate the generated signals using subject-identification classifiers trained on real fMRI. Our method achieves the highest mean accuracy among the evaluated generators. We hope that our work opens up new opportunities for cross-modal brain modeling.