Multiresolution geodesic Bayesian algorithms for estimating the spatial extent and shape of distributed sources: Monte Carlo simulations comparing multiscale sparse Bayesian learning (mSBL), sequential mSBL (smSBL), and Matching Pursuit (mMP)

Introduction The source current density distribution generating the electromagnetic signals measured in MEG/EEG is of variable spatial extent and shape. [...]algorithms that can accurately estimate variably distributed sources of arbitrary shapes are needed. Conclusions This study suggests that mSBL...

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Published in:NeuroImage (Orlando, Fla.) Fla.), 2009-07, Vol.47, p.S145-S145
Main Authors: Ramírez, R.R., Baillet, S.
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Language:English
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Algorithms
Estimates
Methods
Noise
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description Introduction The source current density distribution generating the electromagnetic signals measured in MEG/EEG is of variable spatial extent and shape. [...]algorithms that can accurately estimate variably distributed sources of arbitrary shapes are needed. Conclusions This study suggests that mSBL should be preferred for high SNR data over smSBL and mMP, but that the latter algorithms provide fast alternatives for low SNR data. [...]accurate estimation of distributed sources may require data denoising (e.g., SSS, ICA, etc) and accurate forward models.
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subjects Algorithms
Estimates
Methods
Noise
title Multiresolution geodesic Bayesian algorithms for estimating the spatial extent and shape of distributed sources: Monte Carlo simulations comparing multiscale sparse Bayesian learning (mSBL), sequential mSBL (smSBL), and Matching Pursuit (mMP)
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