document: Fast accurate MEG source localization using a multilayer perceptron trained with real brain noise Iterative gradient methods such as Levenberg-Marquardt -LRB- LM -RRB- are in widespread use for source localization from electroencephalographic -LRB- EEG -RRB- and magnetoencephalographic -LRB- MEG -RRB- signals . Unfortunately , LM depends sensitively on the initial guess , necessitating repeated runs . This , combined with LM 's high per-step cost , makes its computational burden quite high . To reduce this burden , we trained a multilayer perceptron -LRB- MLP -RRB- as a realtime localizer . We used an analytical model of quasistatic electromagnetic propagation through a spherical head to map randomly chosen dipoles to sensor activities according to the sensor geometry of a 4D Neuroimaging Neuromag-122 MEG system , and trained a MLP to invert this mapping in the absence of noise or in the presence of various sorts of noise such as white Gaussian noise , correlated noise , or real brain noise . A MLP structure was chosen to trade off computation and accuracy . This MLP was trained four times , with each type of noise . We measured the effects of initial guesses on LM performance , which motivated a hybrid MLP-start-LM method , in which the trained MLP initializes LM . We also compared the localization performance of LM , MLPs , and hybrid MLP-start-LMs for realistic brain signals . Trained MLPs are much faster than other methods , while the hybrid MLP-start-LMs are faster and more accurate than fixed-4-start-LM . In particular , the hybrid MLP-start-LM initialized by a MLP trained with the real brain noise dataset is 60 times faster and is comparable in accuracy to random-20-start-LM , and this hybrid system -LRB- localization error : 0.28 cm , computation time : 36 ms -RRB- shows almost as good performance as optimal-1-start-LM -LRB- localization error : 0.23 cm , computation time : 22 ms -RRB- , which initializes LM with the correct dipole location . MLPs trained with noise perform better than the MLP trained without noise , and the MLP trained with real brain noise is almost as good an initial guesser for LM as the correct dipole location
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extractive_keyphrases: meg source localization multilayer perceptron real brain noise analytical model quasistatic electromagnetic propagation spherical head white gaussian noise correlated noise iterative gradient methods
abstractive_keyphrases: fast accurate localization real-time localizer computation accuracy levenberg-marquardt method forward model