Jun Barak

Publications of Jun Barak

• MEG Source Localization using an MLP with a Distributed

  Authors: Sung Chan, Jun Barak, A. Pearlmutter, Guido Nolte

  01/2003;

  We present a system that takes realistic magnetoencephalographic (MEG) signals and localizes a single dipole to reasonable accuracy in real time. At its heart is a multilayer perceptron (MLP) whichWe present a system that takes realistic magnetoencephalographic (MEG) signals and localizes a single dipole to reasonable accuracy in real time. At its heart is a multilayer perceptron (MLP) which takes the sensor measurements as inputs, uses one hidden layer, and generates as outputs the amplitudes of receptive fields holding a distributed representation of the dipole location. We trained this Soft-MLP on dipolar sources with real brain noise and converted the network's output into an explicit Cartesian coordinate representation of the dipole location using two different decoding strategies. The proposed Soft-MLPs are much more accurate than previous networks which output source locations in Cartesian coordinates. Hybrid Soft-MLP-start-LM systems, in which the Soft-MLP output initializes Levenberg-Marquardt, retained their accuracy of 0.28 cm with a decrease in computation time from 36 ms to 30 ms. We apply the SoftMLP localizer to real MEG data separated by a blind source separation algorithm, and compare the Soft-MLP dipole locations to those of a conventional system.

• Realtime MEG source localization with realistic noise

  Authors: Sung Chan, Jun Barak, A. Pearlmutter, Guido Nolte

  07/2001;

  Iterative gradient methods like Levenberg-Marquardt (LM) are in widespread use for source localization from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Unfortunately LMIterative gradient methods like Levenberg-Marquardt (LM) are in widespread use for source localization from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Unfortunately LM depends sensitively on the initial guess, particularly (and counterintuitively) at higher signal-to-noise ratios, 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 (MLP) as a real-time localizer. We used an analytical model of quasistatic electromagnetic propagation through the head to map randomly chosen dipoles to sensor activities, and trained an MLP 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-start-LM. 1