When a brain-AI model
does not have every modality.
Missing-modality learning, cross-modal distillation, and modality-aware models for deployable brain AI systems.
In real-world clinical and scientific settings, patients or research subjects rarely have a complete set of modalities available at every timepoint. Models that require all modalities simultaneously fail gracefully when data is missing.
Cross-modal distillation
Neumage uses cross-modal knowledge distillation, where a teacher model trained on full multimodal data (e.g. EEG + MRI + fMRI) transfers representation capability to lightweight student encoders that can run when only EEG or structural MRI is available.
Graceful degradation
By learning modular representation spaces, the Neumage fusion engine maintains inference stability and calibrated confidence bounds even when one or more signal streams are missing or corrupted by noise.
Source: Neumage Robustness & Evaluation Team, 2026.
Build with
brain data.
Explore our platform or discuss your research workflow with the team.