A frozen vision model reads EEG directly: SeeEEG learns only how to display signals and beats EEG foundation models on all eight datasets
EEG foundation models transfer poorly as frozen feature extractors, so SeeEEG takes another route: it renders EEG as images using the montage, filtering and contrast settings of a clinical EEG viewer and trains only those display parameters, leaving the vision backbone frozen. With a frozen DINOv3 ViT-B as encoder, SeeEEG exceeds seven EEG foundation models in balanced accuracy on all eight clinical, sleep, affective and brain-computer interface datasets, reaching a mean rank of 1.3 by Cohen or AUROC against 2.9 for the strongest baseline, REVE.