无需标签数据，通过脑电波还原人脑想象的面孔图像。
Self-Calibrating BCIs: Ranking and Recovery of Mental Targets Without Labels
- 基于脑电与图像数据自校准，不依赖标签或预训练解码器。
- 预测的图像相似度与人类感知高度一致，相关性显著。
- 可生成与目标想象几乎无法区分的新图像，用户研究验证有效。
我们研究在无标签信息条件下，从交互过程中采集的脑电（EEG）和图像（感知面孔）配对数据中恢复参与者心中想象的视觉目标（如人脸）的问题。以往方法依赖标签数据，而本工作首次提出自校准框架CURSOR，无需标签或预训练解码器即可实现目标恢复。在自然场景人脸数据上的实验表明，CURSOR能够：(1) 预测图像相似度分数，且该分数与人类感知判断高度相关；(2) 利用这些分数对刺激进行排序，逼近未知的心理目标；(3) 生成在用户研究（N=53）中难以与真实心理目标区分的新图像。
原文摘要 · Abstract (English)
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).
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