Deep-Meg: A deep learning approach for magnetoencephalograhic inverse problem solutions

This manuscript introduces a deep learning algorithm designed for spatial and temporal source reconstruction based on signals captured by MEG devices. Estimating brain signals at the source level is a significant challenge in magnetoencephalographic (MEG) data processing. Traditional algorithms excel in temporal resolution but face limitations in spatial resolution due to the inherently ill-posed nature of the problem. However, precise localization of pathological tissues is often crucial for providing reliable information to clinicians, which makes this a key area for improvement. Deep learning solutions have emerged as promising candidates for high-resolution signal estimation in this context. The proposed approach, called 'Deep-MEG', utilizes a hybrid neural network architecture capable of extracting both temporal and spatial information from MEG sensor signals. Unlike traditional methods, the algorithm can handle the entire brain, making it suitable for imaging not just cortical sources but also subcortical ones. To validate its performance, the authors conducted simulations with multiple active sources using a realistic forward model and compared the results with those from various state-of-the-art reconstruction algorithms.Clinical relevance- This study represent a first approach for accurate deep source localization and reconstruction leading to diagnosis support to clinicians.