Generate your neural signals from mine: individual-to-individual EEG converters
Generate your neural signals from mine: individual-to-individual EEG converters
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从我的神经信号中生成您的神经信号：个体到个体脑电图转换器
DOI：
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Julie D. Golomb
中科院分区：
文献类型：
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作者：
Zitong Lu;Julie D. Golomb
由于个体差异，认知和计算神经科学中的大多数模型都针对一个主题进行训练，并不适用于其他主题。理想的个体-个体神经转换器可以从一个个体产生另一个个体的真实神经信号，这可以克服认知和计算模型中个体差异的问题。在这项研究中，我们提出了一种新颖的个体到个体脑电转换器，称为EEG2EEG，其灵感来自于计算机视觉中的生成性模型。我们应用Things EEG2数据集训练和测试了72个独立的EEG2EEG模型，对应于9个受试者的72对。结果表明，EEG2EEG能够有效地学习脑电信号中神经表征从一个主体到另一个主体的映射，并获得较高的转换性能。此外，与从真实数据中获得的信息相比，生成的EEG信号包含更清晰的视觉信息表示。该方法为脑电信号的神经转换建立了一种新颖而先进的框架，可以实现从个体到个体的灵活而高效的映射，为神经工程和认知神经科学提供了洞察力。
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.