A fundamental question in brain–computer interfaces (BCIs) is how much visual information can be decoded from time-resolved electrophysiological signals. Here, we propose FuzzyAlign, an alignment framework driven by fuzzy similarity, to establish a benchmark and explore the integration of large pretrained vision models with neural decoding. FuzzyAlign creates a shared latent space between large-scale electrophysiological activity and artificial visual representations, enabling similarity-weighted alignment. A convolutional model combined with fuzzy attention is used to capture temporal and spatial patterns across neural recordings. Using this fuzzy-enhanced framework, we achieve strong visual decoding performance with 1024-channel macaque multiunit activity and state-of-the-art results on human electroencephalography and magnetoencephalography, covering both object identification and image reconstruction via diffusion-based generative models. FuzzyAlign further resolves the spatial and temporal organization of primate visual object recognition, revealing biologically plausible hierarchical processing across brain areas and time. These findings demonstrate the effectiveness of incorporating fuzzy logic into computational brain models, offering a high-performing and interpretable approach for bridging neural and artificial vision systems.
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