# Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network

arXiv: https://arxiv.org/abs/2010.12876 · HTML: https://mlsanity.com/paper/2010.12876/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow](https://mlsanity.com/paper/2603.03354/) (2026)
- [Enhancing Brain Source Reconstruction by Initializing 3D Neural Networks with Physical Inverse Solutions](https://mlsanity.com/paper/2411.00143/) (2024)
- [EMAG: Differentiable 4D Gaussian Mixture Splatting for EEG Spatial Super-Resolution](https://mlsanity.com/paper/2605.29731/) (2026)

## Abstract (arXiv)

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.