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Items: 1 to 20 of 140

1.

Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods.

Gramfort A, Kowalski M, Hämäläinen M.

Phys Med Biol. 2012 Apr 7;57(7):1937-61. doi: 10.1088/0031-9155/57/7/1937. Epub 2012 Mar 16.

2.

Time-frequency mixed-norm estimates: sparse M/EEG imaging with non-stationary source activations.

Gramfort A, Strohmeier D, Haueisen J, Hämäläinen MS, Kowalski M.

Neuroimage. 2013 Apr 15;70:410-22. doi: 10.1016/j.neuroimage.2012.12.051. Epub 2013 Jan 4.

3.

The Iterative Reweighted Mixed-Norm Estimate for Spatio-Temporal MEG/EEG Source Reconstruction.

Strohmeier D, Bekhti Y, Haueisen J, Gramfort A.

IEEE Trans Med Imaging. 2016 Oct;35(10):2218-2228. Epub 2016 Apr 13.

4.

Spatially sparse source cluster modeling by compressive neuromagnetic tomography.

Chang WT, Nummenmaa A, Hsieh JC, Lin FH.

Neuroimage. 2010 Oct 15;53(1):146-60. doi: 10.1016/j.neuroimage.2010.05.013. Epub 2010 May 19.

5.

A distributed spatio-temporal EEG/MEG inverse solver.

Ou W, Hämäläinen MS, Golland P.

Neuroimage. 2009 Feb 1;44(3):932-46. doi: 10.1016/j.neuroimage.2008.05.063. Epub 2008 Jun 14.

6.

Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates.

Lin FH, Witzel T, Ahlfors SP, Stufflebeam SM, Belliveau JW, Hämäläinen MS.

Neuroimage. 2006 May 15;31(1):160-71. Epub 2006 Mar 6.

PMID:
16520063
7.

Sparse current source estimation for MEG using loose orientation constraints.

Chang WT, Ahlfors SP, Lin FH.

Hum Brain Mapp. 2013 Sep;34(9):2190-201. doi: 10.1002/hbm.22057. Epub 2012 Mar 22.

8.

EEG minimum-norm estimation compared with MEG dipole fitting in the localization of somatosensory sources at S1.

Komssi S, Huttunen J, Aronen HJ, Ilmoniemi RJ.

Clin Neurophysiol. 2004 Mar;115(3):534-42.

PMID:
15036048
9.

MNE software for processing MEG and EEG data.

Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, Parkkonen L, Hämäläinen MS.

Neuroimage. 2014 Feb 1;86:446-60. doi: 10.1016/j.neuroimage.2013.10.027. Epub 2013 Oct 24.

10.
11.

Localization Estimation Algorithm (LEA): a supervised prior-based approach for solving the EEG/MEG inverse problem.

Mattout J, Pélégrini-Issac M, Bellio A, Daunizeau J, Benali H.

Inf Process Med Imaging. 2003 Jul;18:536-47.

PMID:
15344486
12.

Quantification of the benefit from integrating MEG and EEG data in minimum l2-norm estimation.

Molins A, Stufflebeam SM, Brown EN, Hämäläinen MS.

Neuroimage. 2008 Sep 1;42(3):1069-77. doi: 10.1016/j.neuroimage.2008.05.064. Epub 2008 Jun 14.

PMID:
18602485
13.

A distributed spatio-temporal EEG/MEG inverse solver.

Ou W, Golland P, Hämäläinen M.

Med Image Comput Comput Assist Interv. 2008;11(Pt 1):26-34.

14.

Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals.

Engemann DA, Gramfort A.

Neuroimage. 2015 Mar;108:328-42. doi: 10.1016/j.neuroimage.2014.12.040. Epub 2014 Dec 23.

PMID:
25541187
15.

Combining sparsity and rotational invariance in EEG/MEG source reconstruction.

Haufe S, Nikulin VV, Ziehe A, Müller KR, Nolte G.

Neuroimage. 2008 Aug 15;42(2):726-38. doi: 10.1016/j.neuroimage.2008.04.246. Epub 2008 May 3.

PMID:
18583157
16.

STATE-SPACE SOLUTIONS TO THE DYNAMIC MAGNETOENCEPHALOGRAPHY INVERSE PROBLEM USING HIGH PERFORMANCE COMPUTING.

Long CJ, Purdon PL, Temereanca S, Desai NU, Hämäläinen MS, Brown EN.

Ann Appl Stat. 2011 Jun 1;5(2B):1207-1228.

17.

A framework for the design of flexible cross-talk functions for spatial filtering of EEG/MEG data: DeFleCT.

Hauk O, Stenroos M.

Hum Brain Mapp. 2014 Apr;35(4):1642-53. doi: 10.1002/hbm.22279. Epub 2013 Apr 24.

18.

Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: depth localization and source separation for focal primary currents.

Lucka F, Pursiainen S, Burger M, Wolters CH.

Neuroimage. 2012 Jul 16;61(4):1364-82. doi: 10.1016/j.neuroimage.2012.04.017. Epub 2012 Apr 17.

PMID:
22537599
19.

Linear inverse solutions: simulations from a realistic head model in MEG.

Soufflet L, Boeijinga PH.

Brain Topogr. 2005 Winter;18(2):87-99. Epub 2005 Dec 5.

PMID:
16341577
20.

Bayesian analysis of the neuromagnetic inverse problem with l(p)-norm priors.

Auranen T, Nummenmaa A, Hämäläinen MS, Jääskeläinen IP, Lampinen J, Vehtari A, Sams M.

Neuroimage. 2005 Jul 1;26(3):870-84. Epub 2005 Apr 8.

PMID:
15955497

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