# EEG-to-Image Reconstruction

Cross-subject EEG-to-image reconstruction has relied on semantic embeddings learned from source subjects, and those embeddings drift off the image-embedding manifold for an unlabeled new subject. SPSC keeps object-level semantic neighborhoods during training with an object-mask contrastive regularizer, then at inference selects semantic candidates inside PCA subspaces of image prototypes using unlabeled target EEG batch statistics. Under leave-one-subject-out evaluation across ten subjects on THINGS-EEG, reconstruction rises from 0.552 to 0.713 on CLIP and from 0.612 to 0.745 on Alex.5.

Cross-subject EEG-to-image reconstruction has relied on semantic embeddings learned from source subjects, and those embeddings drift off the image-embedding manifold for an unlabeled new subject. SPSC keeps object-level semantic neighborhoods during training with an object-mask contrastive regularizer, then at inference selects semantic candidates inside PCA subspaces of image prototypes using unlabeled target EEG batch statistics. Under leave-one-subject-out evaluation across ten subjects on THINGS-EEG, reconstruction rises from 0.552 to 0.713 on CLIP and from 0.612 to 0.745 on Alex.5.