Why Multimodal Brain AI
is moving toward shared spaces.

Research note · October 2026

EEG, structural MRI, DTI, and fMRI capture fundamentally different aspects of neural biology. Multimodal brain AI is evolving from late concatenation toward continuous representation spaces that preserve modality-specific geometry while enabling joint inference.

The challenge of alignment

Combining high-temporal-resolution signals with high-spatial-resolution 3D volumes is not straightforward. Direct concatenation often leads to modality imbalance, where one source dominates the model's decisions.

Shared latent spaces

Cross-modal contrastive alignment and joint representation fusion project distinct modality encoders into a synchronized latent manifold. This allows downstream downstream models to query shared features regardless of input combinations.

Source: Neumage Multimodal AI Research Group, 2026.