Fast Robust Subject-Independent Magnetoencephalographic Source Localization using an Artificial Neural Network

We describe a system that localizes a single dipole to reasonable accuracy from noisy magnetoencephalographic (MEG) measurements in real time. At its core is a multilayer perceptron (MLP) trained to map sensor signals and head position to dipole location. Including head position overcomes the previous need to retrain for each subject and session. The training dataset was generated by mapping randomly chosen dipoles and head positions through an analytic model and adding noise from real MEG recordings. After training, a localization took 0.3 ms with an average error of 1.04 cm. A few iterations of a Levenberg-Marquardt routine using the MLP's output as its initial guess took 47 ms and improved the accuracy to 0.54 cm. We applied these methods to localize single dipole sources from MEG components isolated by blind source separation and compared the estimated locations to those generated by standard commercial software.