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References to implemented methods
This list is far from complete, many more algorithms that have been published in papers are implemented in FieldTrip. If you want to know which paper describes a method in detail and it is not given here, you can ask on the email discussion list.
Solutions for the EEG and MEG forward problem
Forward model solution for MEG, single homogenous sphere
Cuffin BN, Cohen D. Magnetic fields of a dipole in special volume conductor shapes. IEEE Trans Biomed Eng. 1977 Jul;24(4):372-81.
Forward model solution for MEG, multiple sphere
Huang MX, Mosher JC, Leahy RM. A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG. Phys Med Biol. 1999 Feb;44(2):423-40.
Forward model solution for MEG, realistic single shell
Nolte G. The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors. Phys Med Biol. 2003 Nov 21;48(22):3637-52
Forward model solution for EEG, single homogenous and isotropic sphere
R. Kavanagh, T. M. Darccey, D. Lehmann, and D. H. Fender. Evaluation of methods for three-dimensional localization of electric sources in the human brain. IEEE Trans Biomed Eng, 25:421-429, 1978.
Forward model solution for EEG, inhomogenous concentric 4-sphere model
Cuffin BN, Cohen D. Comparison of the magnetoencephalogram and electroencephalogram. Electroencephalogr Clin Neurophysiol. 1979 Aug;47(2):132-46.
Forward model solution for EEG, using BEM
Fuchs M, Kastner J, Wagner M, Hawes S, Ebersole J.S. A standardized boundary element method volume conductor model. Clin Neurophysiol. 2002 May;113(5):702-12.
Oostendorp T, van Oosterom A. The potential distribution generated by surface electrodes in inhomogeneous volume conductors of arbitrary shape. IEEE Trans Biomed Eng. 1991 May;38(5):409-17.
Solutions for the EEG and MEG inverse problem, i.e. source estimation
Beamformer source analysis in the time-domain using LCMV
Van Veen BD, van Drongelen W, Yuchtman M, Suzuki A. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans Biomed Eng. 1997 Sep;44(9):867-80.
Beamformer source analysis in the frequency-domain using DICS
Gross J, Kujala J, Hamalainen M, Timmermann L, Schnitzler A, Salmelin R. Dynamic imaging of coherent sources: Studying neural interactions in the human brain. Proc Natl Acad Sci USA. 2001 Jan 16;98(2):694-9.
Source localization by fitting an equivalent current dipole model
Scherg M. Fundamentals of dipole source potential analysis. In: Auditory evoked magnetic fields and electric potentials. (eds. F. Grandori, M. Hoke and G.L. Romani). Advances in Audiology, vol. 6. Karger, Basel, pp 40-69, 1990.
Distributed source reconstruction using linear estimation
Dale AM, Liu AK, Fischl B, Buckner RL, Belliveau JW, Lewine JD, Halgren E (2000): Dynamic statistical parametric mapping: combining fMRI and MEG to produce high-resolution spatiotemporal maps of cortical activity. Neuron 26:55-67.
Arthur K. Liu, Anders M. Dale, and John W. Belliveau (2002): Monte Carlo Simulation Studies of EEG and MEG Localization Accuracy. Human Brain Mapping 16:47-62.
Fa-Hsuan Lin, Thomas Witzel, Matti S. Hamalainen, Anders M. Dale, John W. Belliveau, and Steven M. Stufflebeam (2004): Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain. NeuroImage 23:582-595.
Frequency analysis
Smoothing in the frequency domain with multiple Slepian tapers
Mitra PP, Pesaran B. Analysis of dynamic brain imaging data. Biophys J. 1999 Feb;76(2):691-708.
Jarvis MR, Mitra PP. Sampling properties of the spectrum and coherency of sequences of action potentials. Neural Comput. 2001 Apr;13(4):717-49.
Convolution in the time-domain with Morlet's wavelets
Tallon-Baudry C, Bertrand O, Delpuech C, Permier J. Oscillatory gamma-band (30-70 Hz) activity induced by a visual search task in humans. J Neurosci. 1997 Jan 15;17(2):722-34.
Spatial realignment of channel-level MEG data
Knosche TR. Transformation of whole-head MEG recordings between different sensor positions. Biomed Tech (Berl). 2002 Mar;47(3):59-62.
Scalp current density mapping
TF Oostendorp, A van Oosterom. The surface Laplacian of the potential: theory and application. IEEE Trans Biomed Eng, 43(4): 394-405, 1996.
F. Perrin, J. Pernier, O. Bertrand, and J. F. Echallier. Spherical splines for scalp potential and curernt density mapping. Electroencephalogr Clin Neurophysiol, 72:184-187, 1989.
Statistics for EEG- and MEG-data
Statistical inference by means of permutation
Maris E., Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods. 2007 Apr 10; [Epub ahead of print] http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=ShowDetailView&TermToSearch=17517438
Maris E., Schoffelen J.M., Fries P. Nonparametric statistical testing of coherence differences. J Neurosci Methods. 2007 Jun 15;163(1):161-75. http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=ShowDetailView&TermToSearch=17395267
Brain-computer interfacing
Van Gerven, M.A.J., Jensen, O. Attention Modulations of Posterior Alpha as a Control Signal for Two-Dimensional Brain-Computer Interfaces. J Neurosci Methods. 2009; 179:78-84. url
Machine learning
Van Gerven, M.A.J., Hesse, C., Jensen, O., Heskes, T. Interpreting Single Trial Data using Groupwise Regularisation. Neuroimage. 2009; accepted for publication.
