How Much Alignment Capacity Does EEG-to-Visual Decoding Need? Retrieval Is Preserved with 42% Fewer Parameters

EEG-to-visual decoding increasingly maps noisy neural responses into expressive pretrained visual spaces that support natural-image retrieval and generation, yet the amount of learned alignment capacity required to preserve useful visual information remains unclear. We isolate this question in ENIGMA by replacing its dense $1024\times 1024$ final residual transformation with rank-224 factors while holding the parent encoder, visual targets, training objective, participant protocol, and evaluation fixed. The intervention reduces trainable EEG-model parameters from 1.405M to 0.815M (41.97%). In a prospectively frozen confirmation across five development-independent participants, mean 200-way single-trial Top-1 is 8.783% for ENIGMA and 8.909% for the compact model, with both one-sided 95% lower bounds above a prespecified $-0.5$-percentage-point retention margin. Compression also lowers effective rank, preserves ENIGMA-compact representation geometry under centered kernel alignment (CKA) and local image neighborhoods, and slightly increases correct-image cosine alignment. At 80 repetitions, the compact representation satisfies a prespecified DINO-content retention criterion when conditioning a fixed SDXL-Turbo/IP-Adapter generator with shuffled-EEG controls. Thus, substantial residual capacity is removable while preserving confirmed retrieval and high-repeat downstream conditioning, making alignment capacity an explicit design variable for EEG-to-visual decoders.