Most models in cognitive and computational neuroscience trained on one subject do not generalize to other subjects due to individual differences. An ideal individual-to-individual neural converter is expected to generate real neural signals of one subject from those of another one, which can overcome the problem of individual differences for cognitive and computational models. In this study, we propose a novel individual-to-individual EEG converter, called EEG2EEG, inspired by generative models in computer vision. We applied THINGS EEG2 dataset to train and test 72 independent EEG2EEG models corresponding to 72 pairs across 9 subjects. Our results demonstrate that EEG2EEG is able to effectively learn the mapping of neural representations in EEG signals from one subject to another and achieve high conversion performance. Additionally, the generated EEG signals contain clearer representations of visual information than that can be obtained from real data. This method establishes a novel and state-of-the-art framework for neural conversion of EEG signals, which can realize a flexible and high-performance mapping from individual to individual and provide insight for both neural engineering and cognitive neuroscience.

翻译：认知与计算神经科学领域的大多数模型在训练于单个受试者时，因个体差异而无法推广至其他受试者。理想的个体间神经信号转换器应能根据某受试者的真实神经信号生成另一受试者的对应信号，从而克服认知与计算模型中个体差异的难题。本研究受计算机视觉中生成模型的启发，提出了一种新颖的个体间脑电图转换器EEG2EEG。我们采用THINGS EEG2数据集，对9名受试者构成的72组配对模型进行了训练与测试，共构建了72个独立的EEG2EEG模型。结果表明，EEG2EEG能有效学习不同受试者脑电图信号中神经表征的映射关系，并实现高精度转换性能。此外，生成的脑电图信号比真实数据包含更清晰的视觉信息表征。该方法构建了全新的、前沿的脑电图神经信号转换框架，可实现灵活且高性能的个体间映射，为神经工程与认知神经科学提供了重要启示。