The paper by M. S. Hämäläinen and R. J. Ilmoniemi addresses the problem of determining primary current distributions from measured neuromagnetic fields using estimation theory. The authors propose the use of minimum-norm estimates (MNEs) to describe the current flow in the brain, which are the best estimates when minimal a priori information about the source is available. MNEs are continuous current distributions that can be used for interpolation and extrapolation of measured field patterns. The method is applicable to both neuromagnetic data and electroencephalography (EEG). The paper also discusses the definition of lead fields, which are vector fields describing the sensitivity pattern of magnetometers to primary currents, and the use of regularization to handle numerical instability. Additionally, the authors propose the use of isocontour maps for presenting MEG data, emphasizing the importance of standardization for data comparison across different laboratories and sessions.The paper by M. S. Hämäläinen and R. J. Ilmoniemi addresses the problem of determining primary current distributions from measured neuromagnetic fields using estimation theory. The authors propose the use of minimum-norm estimates (MNEs) to describe the current flow in the brain, which are the best estimates when minimal a priori information about the source is available. MNEs are continuous current distributions that can be used for interpolation and extrapolation of measured field patterns. The method is applicable to both neuromagnetic data and electroencephalography (EEG). The paper also discusses the definition of lead fields, which are vector fields describing the sensitivity pattern of magnetometers to primary currents, and the use of regularization to handle numerical instability. Additionally, the authors propose the use of isocontour maps for presenting MEG data, emphasizing the importance of standardization for data comparison across different laboratories and sessions.