We propose a novel ℓ₁ℓ₂-norm inverse solver for estimating the sources of EEG/MEG signals. Based on the standard ℓ₁-norm inverse solver, the proposed sparse distributed inverse solver integrates the ℓ₁-norm spatial model with a temporal model of the source signals in order to avoid unstable activation patterns and “spiky” reconstructed signals often produced by the original solvers. The joint spatio-temporal model leads to a cost function with an ℓ₁ℓ₂-norm regularizer whose minimization can be reduced to a convex second-order cone programming problem and efficiently solved using the interior-point method. Validation with simulated and real MEG data shows that the proposed solver yields source time course estimates qualitatively similar to those obtained through dipole fitting, but without the need to specify the number of dipole sources in advance. Furthermore, the ℓ₁ℓ₂-norm solver achieves fewer false positives and a better representation of the source locations than the conventional ℓ₂ minimum-norm estimates.