论文标题
通过同时进行EEG-FMRI的转码，潜在的神经源恢复
Latent neural source recovery via transcoding of simultaneous EEG-fMRI
论文作者
论文摘要
同时EEG-FMRI是一种多模式神经影像学技术，可提供互补的空间和时间分辨率来推断神经活动的潜在源空间。在本文中，我们在转码的框架内解决了此推论问题 - 从特定的编码（模态）映射到解码（潜在源空间），然后将潜在源空间编码到其他模态。具体而言，我们开发了一种对称方法，该方法由循环卷积转码器组成，该卷积转码器将EEG转换为fMRI，反之亦然。如果没有任何先验了解血液动力学响应函数或铅场矩阵，该方法利用了模态和潜在源空间之间的时间和空间关系来学习这些映射。对于实际的EEG-FMRI数据，我们显示了在一个看不见的数据上可以将模式从一个到另一个以及恢复的源空间的转化方式。除了启用一种对称性推断潜在源空间的新方法外，该方法还可以看作是低成本的计算神经影像学 - 即，从“低成本” EEG数据中生成“昂贵” fMRI BOLD图像。
Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that provides complementary spatial and temporal resolution for inferring a latent source space of neural activity. In this paper we address this inference problem within the framework of transcoding -- mapping from a specific encoding (modality) to a decoding (the latent source space) and then encoding the latent source space to the other modality. Specifically, we develop a symmetric method consisting of a cyclic convolutional transcoder that transcodes EEG to fMRI and vice versa. Without any prior knowledge of either the hemodynamic response function or lead field matrix, the method exploits the temporal and spatial relationships between the modalities and latent source spaces to learn these mappings. We show, for real EEG-fMRI data, how well the modalities can be transcoded from one to another as well as the source spaces that are recovered, all on unseen data. In addition to enabling a new way to symmetrically infer a latent source space, the method can also be seen as low-cost computational neuroimaging -- i.e. generating an 'expensive' fMRI BOLD image from 'low cost' EEG data.
