Realtime MEG source localization with realistic noise
Iterative gradient methods like Levenberg-Marquardt (LM) are in widespread use for source localization from electroenceph alographic (EEG) and magnetoencephalographic (MEG) signals. Unfortu nately LM depends sensitively on the initial guess, particularly ( and counterintuitively) at higher signal-to-noise ratios, necessita ting repeated runs. This, combined with LM’s high per-step cost, makes its compu tational burden quite high. To reduce this burden, we trained a multil ayer perceptron (MLP) as a real-time localizer. We used an analytica l model of quasistatic electromagnetic propagation through the he ad to map randomly chosen dipoles to sensor activities, and trained an ML P to invert this mapping in the presence of various sorts of noise. With realistic noise, our MLP is about five hundred times faster than n-start-LM with n = 4 to match accuracies, while our hybrid MLP-start-LM is about four times more accurate and thirteen times faster than 4-startLM.