We consider the problem of recovering a mental target (e.g., an image of a face) that a participant has in mind from paired EEG (i.e., brain responses) and image (i.e., perceived faces) data collected during interactive sessions without access to labeled information. The problem has been previously explored with labeled data but not via self-calibration, where labeled data is unavailable. Here, we present the first framework and an algorithm, CURSOR, that learns to recover unknown mental targets without access to labeled data or pre-trained decoders. Our experiments on naturalistic images of faces demonstrate that CURSOR can (1) predict image similarity scores that correlate with human perceptual judgments without any label information, (2) use these scores to rank stimuli against an unknown mental target, and (3) generate new stimuli indistinguishable from the unknown mental target (validated via a user study, N=53).

翻译：我们研究在交互式会话中，从无标签信息条件下收集的配对脑电图（即大脑响应）与图像（即感知的面孔）数据中，恢复参与者心理目标（例如一张人脸图像）的问题。该问题先前已在有标签数据条件下得到探索，但尚未通过自校准（即无标签数据可用）的方式实现。本文提出了首个无需标签数据或预训练解码器的框架及算法CURSOR，用于学习恢复未知心理目标。我们在自然主义人脸图像上的实验表明，CURSOR能够：（1）在无任何标签信息的情况下预测与人类感知判断相关的图像相似度分数；（2）利用这些分数对刺激物相对于未知心理目标进行排序；（3）生成与未知心理目标无法区分的新刺激物（通过用户研究验证，N=53）。