Bridging the Hierarchies: Neuro-Inspired Hierarchy-Aware Alignment for Visual Neural Decoding

Visual neural decoding aims to retrieve perceived visual stimuli from neural signals and is an important research direction in brain–computer interfaces. Although EEG- and MEG-based methods have made promising progress, they insufficiently account for the neuroscientific priors that visual processing is hierarchically organized and information propagates progressively along cortical pathways. Two key limitations remain: I) incomplete inter-hierarchy correspondence, as existing methods associate neural and visual representations at a selected semantic level, leaving correspondence between the two representational hierarchies as a whole insufficiently captured; and II) insufficient modeling of intra-hierarchy propagation, as existing alignment strategies typically treat semantic levels in isolation, without modeling progressive information propagation across successive levels within each hierarchy. We propose Neuro-Inspired Hierarchy-Aware Alignment (NIA) to jointly address these limitations. NIA integrates a Hierarchy-Partitioned Channel Transformer (HPCT), Hierarchy-Matched Contrastive Learning (HMCL), and a Parallel Dual-Hierarchy Cascade (PDHC) to organize neural representations into a hierarchy, establish structured correspondence between the neural and visual representational hierarchies, and model progressive shallow-to-deep propagation within both hierarchies. Experiments on THINGS-EEG2 and THINGS-MEG demonstrate state-of-the-art zero-shot brain-to-image retrieval. On THINGS-EEG2, NIA improves Top-1/Top-5 accuracy by 9.5/1.3 and 9.9/10.0 percentage points under the intra- and inter-subject settings, respectively, validating the effectiveness of inter-hierarchy correspondence and intra-hierarchy progressive information propagation.