Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for Source Estimation in Neuronal Networks

Neural currents in the brain can be estimated from MEG/EEG recordings by solving the inverse problem (Hamalainen et al. 1993; Mosher, Leahy, and Lewis 1999) . The inverse problem is ill - posed: several current distributions can produce the same or similar electric and magnetic fields outside the head and the e stimates therefore become sensitive to measurement noise (Hamalainen et al. 1993; Helmholtz 1853) . These difficulties limit the spatial resolution and reliability of neural current estimates derived from MEG/EEG signals. To deal with this ill - posedness of the inve rse problem, constraints limiting the space of possible neural current configurations and regularization are often used. Solving the inverse problem requires a forward model that calculates the MEG/EEG signals from given current distributions in the brain (Sarvas 1987; Mosher, Leahy, and Lewis 1999; Stenroos, Hunold, and Haueisen 2014) . Popular methods for solving the inverse problem include discrete current dipole models (Schneider 1972; Scherg and Cramon 1985; Moshe r, Lewis, and Leahy 1992; Leahy et al. 1998) as well as distributed current models (Hamalainen and Ilmoniemi 1994; Uutela, Hamalainen, and Somersalo 1999; Baillet, Mosher, and Leahy 2001; Stenbacka et al. 2002) . Importantly, most source estimation methods are derived sample by sample, i.e., without assuming any relationship between the current distributions across time.