Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network.
Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network.
复制标题
通过基于数据合成的卷积编码器-解码器网络进行电磁源成像。
DOI：
10.1109/tnnls.2022.3209925
复制
发表时间：
2022
影响因子：
10.4
通讯作者：
Wei Wu
中科院分区：
文献类型：
--
作者：
Gexin Huang;Ke Liu;Jiawen Liang;Chang Cai;Zheng Hui Gu;Feifei Qi;Yuanqing Li;Zhu Liang Yu;Wei Wu
电磁源成像（ESI）需要求解一个高度不适定的反问题。为了寻求唯一的解决方案，传统的ESI方法施加了各种形式的先验，可能无法准确反映实际源属性，这可能会阻碍其广泛的应用。为了克服这一局限性，本文提出了一种新的数据合成时空卷积编码器-解码器网络（DST-CedNet）方法。DST-CedNet将ESI重新定义为机器学习问题，其中将判别学习和潜在空间表示集成在CedNet中，以从测量的脑电图/脑磁图（E/MEG）信号到大脑活动学习鲁棒映射。特别是，通过结合有关动态大脑活动的先验知识，设计了一种新的数据合成策略，以生成大规模的样本，有效地训练CedNet。这与传统的ESI方法形成对比，在传统的ESI方法中，先验信息通常通过主要旨在数学方便的约束来实施。大量的数值实验以及对真实的脑磁图和癫痫脑电数据集的分析表明，DST-CedNet在各种源配置下稳健地估计源信号方面优于几种最先进的ESI方法。
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 article, a novel data-synthesized spatiotemporally convolutional encoder-decoder network (DST-CedNet) method is proposed for ESI. The DST-CedNet recasts ESI as a machine learning problem, where discriminative learning and latent-space representations are integrated in a 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 the DST-CedNet outperforms several state-of-the-art ESI methods in robustly estimating source signals under a variety of source configurations.