PrimateAlign: Unifying Zero-Shot Cross-Species Visual Decoding and Neural Translation

Recent neural decoding advances have successfully translated brain activity into visual representations. Despite neuroscientific evidence for profound similarities in primate visual processing, computational decoding models remain single-species. To address this limitation, we propose PrimateAlign, the first unified framework for cross-species visual decoding. Our approach projects human electroencephalography (EEG) and macaque multi-unit activity (MUA) into a shared multimodal latent space. By combining global semantic matching with a novel composite loss formulation for dense spatial alignment, the framework explicitly preserves both high-level conceptual meaning and fine-grained visual details. This shared representational space creates a computational bridge between distinct biological signals. In doing so, it unlocks a fundamentally novel capability of bidirectional cross-species neural translation on unseen stimuli. Extensive experiments demonstrate that PrimateAlign achieves state-of-the-art image retrieval performance in both modalities, reaching 85.9% (+5.9%) on THINGS-EEG2 and 96.4% (+27.1%) on THINGS-TVSD.