Jun, Sung Chan and Pearlmutter, Barak A. and Nolte, Guido
MEG source localization using an MLP with a distributed output representation.
IEEE transactions on biomedical engineering, 50 (6).
786 - 789 .
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) 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 Soft-MLP localizer to real MEG data separated by a blind source separation algorithm, and compare the Soft-NMP dipole locations to those of a conventional system.
||Copyright Notice "©2003 IEEE. Reprinted from IEEE transactions on biomedical engineering. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1203818&isnumber=27110
||Magnetoencephalography; Medical signal processing; Multilayer perceptrons; 30 ms; 36 ms; Cartesian coordinates; Hybrid Soft-MLP-start-LM systems; Blind source separation algorithm; Computation time decrease; Decoding strategies; Dipole location distributed representation; Explicit Cartesian coordinate representation; Hidden layer; Real brain noise; Sensor measurements.
||Faculty of Science and Engineering > Computer Science
||20 May 2009 16:53
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