论文标题
脑电图综合脑电图：深入学习候选人吗？
EEG to fMRI Synthesis: Is Deep Learning a candidate?
论文作者
论文摘要
信号，图像和视频生成的进步基础是生成医学成像任务的重大突破，包括大脑图像合成。尽管如此，可以从大脑电生理学绘制功能性磁性磁性成像（fMRI）的程度仍然在很大程度上没有探索。这项工作提供了有关如何使用从神经处理到综合脑电图（EEG）数据合成fMRI数据的最新原则的首个全面观点。鉴于血流动力学和电生理信号的独特时空性质，该问题被提出为学习具有高度相似结构的多元时间序列之间的映射函数的任务。进行了最先进的合成方法的比较，包括自动编码器，生成对抗网络和成对学习。结果强调了脑电图对fMRI大脑图像映射的可行性，从而指出了当前进步在机器学习中的作用，并显示了即将贡献的相关性，以进一步提高性能。脑电图到功能磁共振成像合成提供了一种增强和增强大脑图像数据的方法，并确保访问大脑活动监测的更负担得起，便携式和持久的方案。该手稿中使用的代码可在GitHub中获得，数据集是开源的。
Advances on signal, image and video generation underly major breakthroughs on generative medical imaging tasks, including Brain Image Synthesis. Still, the extent to which functional Magnetic Ressonance Imaging (fMRI) can be mapped from the brain electrophysiology remains largely unexplored. This work provides the first comprehensive view on how to use state-of-the-art principles from Neural Processing to synthesize fMRI data from electroencephalographic (EEG) data. Given the distinct spatiotemporal nature of haemodynamic and electrophysiological signals, this problem is formulated as the task of learning a mapping function between multivariate time series with highly dissimilar structures. A comparison of state-of-the-art synthesis approaches, including Autoencoders, Generative Adversarial Networks and Pairwise Learning, is undertaken. Results highlight the feasibility of EEG to fMRI brain image mappings, pinpointing the role of current advances in Machine Learning and showing the relevance of upcoming contributions to further improve performance. EEG to fMRI synthesis offers a way to enhance and augment brain image data, and guarantee access to more affordable, portable and long-lasting protocols of brain activity monitoring. The code used in this manuscript is available in Github and the datasets are open source.
