Brain-to-image decoding can work with far fewer repetitions by anchoring both neural and visual representations to a shared reference point, rather than just denoising the query alone.
This paper tackles brain-to-image retrieval with limited neural data. Current methods require averaging 80+ brain scans per image, but the authors show that low-repetition queries fail not just due to noise, but because brain and image representations misalign.