Subject-Independent Magnetoencephalographic Source Localization by a Multilayer Perceptron

We describe a system that localizes a single dipole to reasonable accu- racy from noisy magnetoencephalographic (MEG) measurements in real time. At its core is a multilayer perceptron (MLP) trained to map sen- sor signals and head position to dipole location. Including head position overcomes the previous need to retrain the MLP for each subject and ses- sion. 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 0.7 ms with an average error of 0.90 cm.