Source Localization of Brain Electromagnetic Fields
博士 === 國立中央大學 === 物理研究所 === 97 === A recursive scheme aiming at obtaining sparse and focal brain electromagnetic source distribution is proposed based on the interpretation that the weighted minimum norm is the minimum norm estimates of amplitudes on grid points for the source distribution specified...
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| Format: | Others |
| Language: | en_US |
| Published: |
2009
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| Online Access: | http://ndltd.ncl.edu.tw/handle/27025365466154286193 |
| Summary: | 博士 === 國立中央大學 === 物理研究所 === 97 === A recursive scheme aiming at obtaining sparse and focal brain electromagnetic source distribution is proposed based on the interpretation that the weighted minimum norm is the minimum norm estimates of amplitudes on grid points for the source distribution specified by the diagonal elements of the weight matrix. The source distribution is updated so that, at each grid point, the number of current dipoles equals the total source strength estimate of the pre-specified current dipoles. The source strength of a pre-specified free orientation current dipole is estimated by projecting the vector of minimum norm estimate to the space spanned by the corresponding three column vectors of the resolution matrix. The norm of the projected vector yields the source strength estimate of the current dipole. Exact inverse solutions are obtained by this source iteration of minimum norm (SIMN) algorithm for noiseless MEG signals from multi-point sources provided the sources are sufficiently sparse and there are no substantial cancellations among the signals of the sources. For noisy data, a set of “noise sources” is introduced. The diagonal matrix formed by the “noise source numbers” plays the role of regularization matrix and Tikhonov regularization is applied to initialize the “noise-source numbers”. The noise tolerance of SIMN can be optimized by applying depth weighting to the lead fields with a suitable depth weighting parameter. Applications to the source localization of real EEG and MEG data are also presented.
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