Electromagnetic source imaging (ESI) requires solving a highly ill-posed inverse problem. To seek a unique solution, traditional ESI methods impose various forms of priors that may not accurately reflect the actual source properties, which may hinder their broad applications. To overcome this limitation, in this paper a novel data-synthesized spatio-temporally convolutional encoder-decoder network method termed DST-CedNet is proposed for ESI. DST-CedNet recasts ESI as a machine learning problem, where discriminative learning and latent-space representations are integrated in a convolutional encoder-decoder network (CedNet) to learn a robust mapping from the measured electroencephalography/magnetoencephalography (E/MEG) signals to the brain activity. In particular, by incorporating prior knowledge regarding dynamical brain activities, a novel data synthesis strategy is devised to generate large-scale samples for effectively training CedNet. This stands in contrast to traditional ESI methods where the prior information is often enforced via constraints primarily aimed for mathematical convenience. Extensive numerical experiments as well as analysis of a real MEG and Epilepsy EEG dataset demonstrate that DST-CedNet outperforms several state-of-the-art ESI methods in robustly estimating source signals under a variety of source configurations.

翻译：电磁源成像(ESI)要求解决高度不良的反向问题。为了寻求一种独特的解决办法,传统的 ESI 方法将各种可能无法准确反映实际源特性的预言形式强加于人,这可能阻碍其广泛的应用。为了克服这一局限性,本文件提出了名为 DST-CedNet 的新的数据合成系统网络方法。DST-CedNet 重新将 ESI 重新定位为机器学习问题,将歧视性学习和潜在空间表现纳入同级电解码网络(CedNet),以便从测量的电脑成像学/磁成像学(E/MEG)信号中学习强有力的绘图。特别是,通过吸收关于动态脑活动的先前知识,设计了一种新的数据合成战略,为有效培训CedNet生成大规模样本。这与传统的 ESI 方法形成对照,即以前的信息往往是通过主要为数学便利而实施的限制而执行的。 ST-C 快速数字网络(EEG-EG-I) 的多种数据模型展示了EG-S-C 的多种真实的EVI-C 数据模型模型分析,作为E-E-C-C-sviewsemislational 的多种数据模型的模型,作为一种真实的EG-EG-EG-smal-sem-smal-smalsmal 的模型的模型的模型的模型的模型,作为一种分析。