Extended Brain Sources Estimation via Unrolled Optimization Neural Network

Electroencephalography (EEG)/Magnetoencephalography (MEG) source imaging aims to seek an estimation of underlying activated brain sources to explain the observed EEG/MEG recording. Due to the ill-posed nature of inverse problem, solving EEG/MEG Source Imaging (ESI) requires design of regularization or prior terms to guarantee a unique solution. Traditionally, the design of regularization terms is based on preliminary assumptions on the spatio-temporal structure in the source space. In this paper, we propose a novel paradigm to solve the ESI problem by using Unrolled Optimization Neural Network (UONN) (1) to improve the efficiency compared to traditional iterative algorithms; (2) to establish a data-driven way to model the source solution structure instead of using hand-crafted regularizations; (3) to learn the hyperparameter automatically in a data-driven manner. The proposed framework is based on unfolding of the iterative optimization algorithm with neural network modules. The proposed new learning framework is the first one that use the unrolled optimization neural network to solve the ESI problem. The newly designed framework can effectively learn the source extents pattern and achieved significantly improved performance compared to benchmark algorithms.