SAUNA: Source-Anchored Unsupervised Neural Adaptation for Cross-Subject EEG Decoding

Decoding seen images from EEG has advanced rapidly, but much of that progress has been measured $\text{within}$ subjects, with a decoder trained and evaluated on the same person. However, practical adoption will require a more challenging cross-subject setting, in which the decoder must generalize to a person whose labeled data was never observed. We ask what actually changes when a decoder trained on some brains is applied to an unseen brain. A contrastive decoder aligns two representation spaces, an image side and a signal side, and we answer this question for each side by direct measurement. On the image side, we show that the depth at which a vision backbone should supply its alignment target is a property of the visual hierarchy rather than of the individual. The alignment target can therefore be $\text{anchored}$: estimated once from source subjects and shared by every new subject. Implemented as per-dimension selection over the depth of a multi-backbone feature pool, the anchored target yields a zero-shot decoder that attains \textbf{37.72\\%} 200-way top-1 accuracy on THINGS-EEG2, surpassing the strongest published result of 24.0%. On the signal side, we show that about half of the attainable accuracy is subject-specific and beyond the reach of source data alone. To recover this component without labels, we introduce SAUNA (Source-Anchored Unsupervised Neural Adaptation), which uses only $\text{unlabeled}$ EEG from the target subject and never observes the trial-to-image pairing. SAUNA reads three properties of the subject's signal from such data: its position relative to the image manifold, its distribution shape, and its trial noise. One objective is instantiated for each property and optimized jointly with the anchor, so that the representation adapts without drifting from image space. SAUNA raises accuracy from 37.25% for a matched control to \textbf{47.77\\%}, a gain that is positive for all ten held-out subjects, recovers roughly a third of the subject-specific gap, and is, to our knowledge, the strongest label-free cross-subject result reported to date.