Where, When, and What Aligns: A Retrieval-Grounded Atlas of EEG–ViT Alignment

The alignment between human brain activity and pretrained vision models has become both a window into biological vision and the engine of EEG-to-image decoding. In this work, we draw the first systematic atlas of the EEG–ViT interface measured in the deployment metric itself, end-to-end 200-way retrieval, resolved jointly over scalp position, epoch time, network depth, sub-layer computation, token readout, and training objective. Analyzing 21 vision models spanning contrastive, supervised, self-distillation, and reconstruction training, we evaluate approximately 21,000 probe cells on all ten THINGS-EEG2 subjects at three trial-count regimes ($N=1/4/80$ averaged test repetitions), with every claim supported by a per-subject sign test. Our analysis reveals three key findings: (1) where and when: the network's depth axis carries a brain timestamp; each block aligns best with a distinct, progressively later moment of the EEG epoch (177 to 201 ms across blocks 0 to 11, in 10/10 subjects), co-registered with the scalp's posterior-to-anterior latency gradient; (2) where in the network: the well-documented mid-depth alignment peak has a mechanism; early blocks write EEG-visible content into the residual stream, the mid-network stream integrates those writes and overtakes every computation, and late computations fade while the stream coasts on what was already written; (3) what: what EEG shares with a vision transformer is low-level visual structure at coarse spatial grain, not fine-grained semantics: alignment is carried by electrodes over early visual cortex, peaks at one-third to one-half of network depth, deeper still under masked autoencoding, and fades exactly where representations turn semantic, in the late layers, unless the training target itself is pixel reconstruction; spatially, a coarse $2\times 2$ to $4\times 4$ pooling of the token grid outperforms the field's global readouts by 6 to 22 points and any single patch by far more, the grain expected of a 63-electrode montage reading through the scalp. Building on these insights, the atlas configures decoders by measurement alone: it distills a five-model fusion pipeline to eight electrodes and a single backbone that surpass the published state of the art, and composes a four-state fusion chain that reaches 97.5% final-epoch accuracy together with the first single-trial accuracy above 22%. Our results suggest that the brain–transformer interface is a lawful and largely unexplored territory, and that deployment-currency measurement offers a new instrument for studying biological vision through transformer computations and vision models through the organization of the brain.