  We present a deep learning solution to the problem of localization of
magnetoencephalography (MEG) brain signals. The proposed deep model
architectures are tuned for single and multiple time point MEG data, and can
estimate varying numbers of dipole sources. Results from simulated MEG data on
the cortical surface of a real human subject demonstrated improvements against
the popular RAP-MUSIC localization algorithm in specific scenarios with varying
SNR levels, inter-source correlation values, and number of sources.
Importantly, the deep learning models had robust performance to forward model
errors and a significant reduction in computation time, to a fraction of 1 ms,
paving the way to real-time MEG source localization.
