Brain signals and images carry different amounts of information. An fMRI voxel or EEG channel compresses the visual experience into a lower-dimensional representation. Previous brain-to-image alignment methods treat this as a straightforward embedding problem: project brain features and image features into a shared space, minimize the distance. But two things go wrong. First, the modality gap — brain signals are inherently lower-resolution than visual features. Second, semantic and perceptual information are entangled in neural activity and can't be cleanly separated.
Kim et al. (arXiv:2603.22721) use hyperbolic space. In hyperbolic geometry, geodesics between two points naturally curve toward the origin, where representational capacity is lower. This geometric property matches the brain's compression: interpolating between semantic and perceptual visual features along hyperbolic geodesics simultaneously fuses and compresses them, mirroring how neural activity encodes visual information at reduced bandwidth.
The result: +17.3% top-1 accuracy improvement on THINGS-EEG and +9.1% on THINGS-MEG for zero-shot brain-to-image retrieval — state of the art.
The through-claim: the right geometry does the work. Euclidean space treats all representational capacities as equal. Hyperbolic space encodes a hierarchy — the origin is coarse, the periphery is detailed. Brain signals live closer to the origin. The alignment improves not because of a better loss function or more data, but because the embedding space matches the information structure.