Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)
Sung Jun, Barak Pearlmutter
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. A few iterations of a Levenberg-Marquardt routine using the MLP’s output as its initial guess took 15 ms and im- proved the accuracy to 0.53 cm, only slightly above the statistical limits on accuracy imposed by the noise. 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 manually-assisted commercial software.