Peer-Reviewed Journal Details
Mandatory Fields
Jun, SC;Pearlmutter, BA
2005
January
Human Brain Mapping
Fast robust subject-independent magnetoencephalographic source localization using an artificial neural network
Published
6 ()
Optional Fields
BLIND SOURCE SEPARATION EEG SOURCE LOCALIZATION HUMAN SKULL PHANTOM COMPONENT ANALYSIS DIPOLE LOCALIZATION HEAD MODELS HUMAN BRAIN MEG ARTIFACTS IDENTIFICATION
24
21
34
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 the MLP 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 0.7 ms with an average error of 0.90 cm. A few iterations of a Levenberg-Marquardt routine using the MLP output as its initial guess took 15 ms and improved accuracy to 0.53 cm, which approaches the natural limit on accuracy imposed by 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. (C) 2004 Wiley-Liss, Inc.
HOBOKEN
1065-9471
10.1002/hbm.20068
Grant Details