GLAFE: Whole-Brain EEG-to-FMRI Prediction via Global–Local Modeling

Functional magnetic resonance imaging (fMRI) provides spatially detailed measurements of whole-brain activity, but its high cost and limited accessibility constrain its use. In contrast, electroencephalography (EEG) is inexpensive, portable, and temporally precise, yet offers limited spatial resolution. Whole-brain EEG-to-fMRI translation could thus enable accessible and detailed mapping of brain activity. Such translation requires both temporal and spatial fidelity: reconstructing the temporal dynamics of individual brain regions of interest (ROIs), while preserving the spatial structure of correlations between them, as captured by their functional connectivity (FC). fMRI signals are composed of a global component, shared across all brain regions, and region-specific fluctuations which deviate from it. Motivated by this structure, we introduce $\text{GLAFE}$, a $\text{G}$lobal-$\text{L}$ocal $\text{A}$utoregressive framework for predicting whole-brain $\text{f}$MRI from $\text{E}$EG. By modeling these components separately, our approach is able to reconstruct temporal dynamics while preserving accurate FC structure. To predict the local signals, GLAFE learns region-specific embeddings that query EEG features through cross-attention, while an autoregressive component uses previously generated whole-brain fMRI to refine these predictions. We evaluate our method across multiple datasets and subject splits, and demonstrate its generalization capabilities to unseen datasets and tasks in a zero-shot setting. Across these evaluations, GLAFE outperforms competitive baselines in both temporal dynamics and FC reconstruction metrics in almost all configurations.