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

TL;DR

This paper introduces DST-CedNet, a novel deep learning approach for electromagnetic source imaging that leverages data synthesis and a convolutional encoder-decoder network to improve source localization accuracy.

Contribution

The paper presents a new data-synthesis strategy and a convolutional encoder-decoder network for ESI, overcoming limitations of traditional prior-based methods.

Findings

Outperforms state-of-the-art ESI methods in numerical experiments

Robustly estimates source signals across various configurations

Effective in real MEG and EEG datasets

Abstract

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.