Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network. (arXiv:2010.12876v6 [eess.IV] UPDATED)

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 …

arxiv data data-synthesis encoder encoder-decoder imaging network