基于数据合成的卷积编解码网络的电磁源成像
Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network
摘要：
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-解码器网络方法称为DST-CedNet的ESI。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卷积编码器-解码器网络（CedNet）中，以学习从测量的脑电图/脑磁图（E/MEG）信号到大脑活动的鲁棒映射。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一个真实的脑磁图和癫痫脑电数据集的分析表明，DST-CedNet在各种源配置下鲁棒地估计源信号方面优于几种最先进的ESI方法。
英文摘要：
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.
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