  We present a novel solution to the problem of localizing
magnetoencephalography (MEG) and electroencephalography (EEG) brain signals.
The solution is sequential and iterative, and is based on minimizing the
least-squares criterion by the Alternating Projection algorithm. Results from
simulated and experimental MEG data from a human subject demonstrated robust
performance, with consistently superior localization accuracy than scanning
methods belonging to the beamformer and multiple-signal classification (MUSIC)
families. Importantly, the proposed solution is more robust to forward model
errors resulting from head rotations and translations, with a significant
advantage in highly correlated sources.
