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function [data] = ft_megplanar(cfg, data)
% FT_MEGPLANAR computes planar MEG gradients gradients for raw data or average
% event-related field data. It can also convert frequency-domain data that was computed
% using FT_FREQANALYSIS, as long as it contains the complex-valued fourierspcrm and not
% only the powspctrm.
% Use as
% [interp] = ft_megplanar(cfg, data)
% where the input data corresponds to the output from FT_PREPROCESSING,
% FT_TIMELOCKANALYSIS or FT_FREQANALYSIS (with output='fourier').
% The configuration should contain
% cfg.planarmethod = string, can be 'sincos', 'orig', 'fitplane', 'sourceproject' (default = 'sincos')
% = Nx1 cell-array with selection of channels (default = 'all'), see FT_CHANNELSELECTION for details
% cfg.trials = 'all' or a selection given as a 1xN vector (default = 'all')
% The methods orig, sincos and fitplane are all based on a neighbourhood interpolation.
% For these methods you need to specify
% cfg.neighbours = neighbourhood structure, see FT_PREPARE_NEIGHBOURS
% In the 'sourceproject' method a minumum current estimate is done using a large number
% of dipoles that are placed in the upper layer of the brain surface, followed by a
% forward computation towards a planar gradiometer array. This requires the
% specification of a volume conduction model of the head and of a source model. The
% 'sourceproject' method is not supported for frequency domain data.
% A dipole layer representing the brain surface must be specified with
% cfg.inwardshift = depth of the source layer relative to the head model surface ,
% (default = 2.5 cm, which is appropriate for a skin-based head model)
% cfg.spheremesh = number of dipoles in the source layer (default = 642)
% cfg.tolerance = tolerance ratio for leadfield matrix inverse based on a truncated svd,
% reflects the relative magnitude of the largest singular value
% to retain (default = 1e-3)
% cfg.headshape = a filename containing headshape, a structure containing a
% single triangulated boundary, or a Nx3 matrix with surface
% points
% If no headshape is specified, the dipole layer will be based on the inner compartment
% of the volume conduction model.
% Optionally, you can modify the leadfields by reducing the rank, i.e. remove the weakest orientation
% cfg.reducerank = 'no', or number (default = 3 for EEG, 2 for MEG)
% cfg.backproject = 'yes' or 'no', determines when reducerank is applied whether the
% lower rank leadfield is projected back onto the original linear
% subspace, or not (default = 'yes')
% The volume conduction model of the head should be specified as
% cfg.headmodel = structure with volume conduction model, see FT_PREPARE_HEADMODEL
% The following cfg fields are optional:
% To facilitate data-handling and distributed computing you can use
% cfg.inputfile = ...
% cfg.outputfile = ...
% If you specify one of these (or both) the input data will be read from a *.mat
% file on disk and/or the output data will be written to a *.mat file. These mat
% files should contain only a single variable, corresponding with the
% input/output structure.
% Copyright (C) 2004-2019, Robert Oostenveld
% Copyright (C) 2020- Robert Oostenveld and Jan-Mathijs Schoffelen
% This file is part of FieldTrip, see
% for the documentation and details.
% FieldTrip is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
% FieldTrip is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% GNU General Public License for more details.
% You should have received a copy of the GNU General Public License
% along with FieldTrip. If not, see <>.
% $Id$
% these are used by the ft_preamble/ft_postamble function and scripts
ft_revision = '$Id$';
ft_nargin = nargin;
ft_nargout = nargout;
% do the general setup of the function
ft_preamble init
ft_preamble debug
ft_preamble loadvar data
ft_preamble provenance data
% the ft_abort variable is set to true or false in ft_preamble_init
if ft_abort
% store the original input representation of the data, this is used later on to convert it back
isfreq = ft_datatype(data, 'freq');
istlck = ft_datatype(data, 'timelock'); % this will be temporary converted into raw
% check if the input data is valid for this function, this converts the data if needed
data = ft_checkdata(data, 'datatype', {'raw' 'freq'}, 'feedback', 'yes', 'hassampleinfo', 'yes', 'ismeg', 'yes', 'senstype', {'ctf151', 'ctf275', 'bti148', 'bti248', 'itab153', 'yokogawa160', 'yokogawa64'});
if isfreq
if ~isfield(data, 'fourierspctrm'), ft_error('freq data should contain Fourier spectra'); end
% check if the input cfg is valid for this function
cfg = ft_checkconfig(cfg, 'forbidden', {'channels', 'trial'}); % prevent accidental typos, see issue 1729
cfg = ft_checkconfig(cfg, 'renamed', {'hdmfile', 'headmodel'});
cfg = ft_checkconfig(cfg, 'renamed', {'vol', 'headmodel'});
cfg = ft_checkconfig(cfg, 'renamed', {'grid', 'sourcemodel'});
cfg = ft_checkconfig(cfg, 'renamed', {'pruneratio', 'tolerance'});
% set the default configuration = ft_getopt(cfg, 'channel', 'all');
cfg.tolerance = ft_getopt(cfg, 'tolerance', 1e-3);
cfg.trials = ft_getopt(cfg, 'trials', 'all', 1);
cfg.planarmethod = ft_getopt(cfg, 'planarmethod', 'sincos'); = ft_getopt(cfg, 'feedback', 'text');
% check if the input cfg is valid for this function
cfg = ft_checkconfig(cfg, 'renamedval', {'headshape', 'headmodel', []});
if ~strcmp(cfg.planarmethod, 'sourceproject')
% this is limited to reading neighbours from disk and/or selecting channels
% the user should call FT_PREPARE_NEIGHBOURS directly for the actual construction
tmpcfg = keepfields(cfg, {'neighbours', 'channel', 'showcallinfo', 'trackcallinfo', 'trackusage', 'trackdatainfo', 'trackmeminfo', 'tracktimeinfo'});
cfg.neighbours = ft_prepare_neighbours(tmpcfg);
% put the low-level options pertaining to the dipole grid in their own field
cfg = ft_checkconfig(cfg, 'renamed', {'tightgrid', 'tight'}); % this is moved to cfg.sourcemodel.tight by the subsequent createsubcfg
cfg = ft_checkconfig(cfg, 'renamed', {'sourceunits', 'unit'}); % this is moved to cfg.sourcemodel.unit by the subsequent createsubcfg
% put the low-level options pertaining to the sourcemodel in their own field
cfg = ft_checkconfig(cfg, 'createsubcfg', {'sourcemodel'});
% move some fields from cfg.sourcemodel back to the top-level configuration
cfg = ft_checkconfig(cfg, 'createtopcfg', {'sourcemodel'});
% select trials of interest
tmpcfg = keepfields(cfg, {'trials', 'channel', 'showcallinfo', 'trackcallinfo', 'trackusage', 'trackdatainfo', 'trackmeminfo', 'tracktimeinfo'}); % don't keep tolerance, it is used differently here
data = ft_selectdata(tmpcfg, data);
% restore the provenance information
[cfg, data] = rollback_provenance(cfg, data);
if strcmp(cfg.planarmethod, 'sourceproject')
% Do an inverse computation with a simplified distributed source model
% and compute forward again with the axial gradiometer array replaced by
% a planar one.
% method specific configuration options
cfg.headshape = ft_getopt(cfg, 'headshape', []);
cfg.inwardshift = ft_getopt(cfg, 'inwardshift', 2.5); % this number assumes that all other inputs are in cm
cfg.pruneratio = ft_getopt(cfg, 'pruneratio', 1e-3);
cfg.spheremesh = ft_getopt(cfg, 'spheremesh', 642);
if isfreq
ft_error('the method ''sourceproject'' is not supported for frequency data as input');
% PREPARE_HEADMODEL will match the data labels, the gradiometer labels and the
% volume model labels (in case of a localspheres model) and result in a gradiometer
% definition that only contains the gradiometers that are present in the
% data. This should exclude the non-MEG channels, so the user-defined
% should be overruled
tmpcfg = cfg; = ft_channelselection('MEG',;
[headmodel, axial.grad, tmpcfg] = prepare_headmodel(tmpcfg, data);
% construct the low-level options for the leadfield computation as key-value pairs, these are passed to FT_COMPUTE_LEADFIELD
leadfieldopt = {};
leadfieldopt = ft_setopt(leadfieldopt, 'reducerank', ft_getopt(cfg, 'reducerank'));
leadfieldopt = ft_setopt(leadfieldopt, 'backproject', ft_getopt(cfg, 'backproject'));
leadfieldopt = ft_setopt(leadfieldopt, 'normalize', ft_getopt(cfg, 'normalize'));
leadfieldopt = ft_setopt(leadfieldopt, 'normalizeparam', ft_getopt(cfg, 'normalizeparam'));
leadfieldopt = ft_setopt(leadfieldopt, 'weight', ft_getopt(cfg, 'weight'));
% copy all options that are potentially used in FT_PREPARE_SOURCEMODEL
tmpcfg = keepfields(cfg, {'sourcemodel', 'mri', 'headshape', 'symmetry', 'smooth', 'threshold', 'spheremesh', 'inwardshift', 'xgrid' 'ygrid', 'zgrid', 'resolution', 'tight', 'warpmni', 'template', 'showcallinfo', 'trackcallinfo', 'trackusage', 'trackdatainfo', 'trackmeminfo', 'tracktimeinfo'});
tmpcfg.headmodel = headmodel;
tmpcfg.grad = axial.grad;
% determine the dipole layer that represents the surface of the brain
sourcemodel = ft_prepare_sourcemodel(tmpcfg);
% compute the forward model for the axial gradiometers
ft_info('computing forward model for %d dipoles\n', size(sourcemodel.pos,1));
lfold = ft_compute_leadfield(sourcemodel.pos, axial.grad, headmodel, leadfieldopt{:});
% construct the planar gradient definition and compute its forward model
% this will not work for a localspheres model, compute_leadfield will catch
% the error
planar.grad = constructplanargrad([], axial.grad);
lfnew = ft_compute_leadfield(sourcemodel.pos, planar.grad, headmodel, leadfieldopt{:});
% compute the interpolation matrix
transform = lfnew * ft_inv(lfold, 'method', 'tsvd', 'tolerance', cfg.tolerance);
planarmontage = [];
planarmontage.tra = transform;
planarmontage.labelold = axial.grad.label;
planarmontage.labelnew = planar.grad.label;
% apply the linear transformation to the data
interp = ft_apply_montage(data, planarmontage, 'keepunused', 'yes');
% also apply the linear transformation to the gradiometer definition
interp.grad = ft_apply_montage(data.grad, planarmontage, 'balancename', 'planar', 'keepunused', 'yes');
% ensure there is a type string describing the gradiometer definition
if ~isfield(interp.grad, 'type')
interp.grad.type = [ft_senstype(data.grad) '_planar'];
interp.grad.type = [interp.grad.type '_planar'];
% % interpolate the data towards the planar gradiometers
% for i=1:Ntrials
% ft_info('interpolating trial %d to planar gradiometer\n', i);
% interp.trial{i} = transform * data.trial{i}(dataindx,:);
% end % for Ntrials
% % all planar gradiometer channels are included in the output
% interp.grad = planar.grad;
% interp.label = planar.grad.label;
% % copy the non-gradiometer channels back into the output data
% other = setdiff(1:Nchan, dataindx);
% for i=other
% interp.label{end+1} = data.label{i};
% for j=1:Ntrials
% interp.trial{j}(end+1,:) = data.trial{j}(i,:);
% end
% end
sens = ft_determine_units(data.grad);
chanposnans = any(isnan(sens.chanpos(:))) || any(isnan(sens.chanori(:)));
if chanposnans
if isfield(sens, 'chanposold')
% temporarily replace chanpos and chanorig with the original values
sens.chanpos = sens.chanposold;
sens.chanori = sens.chanoriold;
sens.label = sens.labelold;
sens = rmfield(sens, {'chanposold', 'chanoriold', 'labelold'});
ft_error('The channel positions (and/or orientations) contain NaNs; this prohibits correct behavior of the function. Please replace the input channel definition with one that contains valid channel positions');
end = ft_channelselection(, sens.label); = ft_channelselection(, data.label);
% ensure channel order according to (there might be one check
% too much in here somewhere or in the subfunctions, but I don't care.
% Better one too much than one too little - JMH @ 09/19/12
cfg = struct(cfg);
[neighbsel] = match_str({cfg.neighbours.label},;
cfg.neighbours = cfg.neighbours(neighbsel);
cfg.neighbsel = channelconnectivity(cfg);
assert(any(cfg.neighbsel(:)), 'no neighbours found')
ft_info('average number of neighbours is %.2f\n', mean(sum(cfg.neighbsel)));
Ngrad = length(sens.label);
distance = zeros(Ngrad,Ngrad);
for i=1:size(cfg.neighbsel,1)
j=find(cfg.neighbsel(i, :));
d = sqrt(sum((sens.chanpos(j,:) - repmat(sens.chanpos(i, :), numel(j), 1)).^2, 2));
distance(i,j) = d;
distance(j,i) = d;
ft_info('minimum distance between neighbours is %6.2f %s\n', min(distance(distance~=0)), sens.unit);
ft_info('maximum distance between gradiometers is %6.2f %s\n', max(distance(distance~=0)), sens.unit);
% The following does not work when running in deployed mode because the
% private functions that compute the planar montage are not recognized as
% such and won't be compiled, unless explicitly specified.
% % generically call megplanar_orig megplanar_sincos or megplanar_fitplane
%fun = ['megplanar_' cfg.planarmethod];
%if ~exist(fun, 'file')
% ft_error('unknown method for computation of planar gradient');
%planarmontage = eval([fun '(cfg, data.grad)']);
switch cfg.planarmethod
case 'sincos'
planarmontage = megplanar_sincos(cfg, sens);
case 'orig'
% method specific info that is needed
cfg.distance = distance;
planarmontage = megplanar_orig(cfg, sens);
case 'fitplane'
planarmontage = megplanar_fitplane(cfg, sens);
fun = ['megplanar_' cfg.planarmethod];
if ~exist(fun, 'file')
ft_error('unknown method for computation of planar gradient');
planarmontage = eval([fun '(cfg, data.grad)']);
% apply the linear transformation to the data
interp = ft_apply_montage(data, planarmontage, 'keepunused', 'yes', 'feedback',;
% also apply the linear transformation to the gradiometer definition
interp.grad = ft_apply_montage(sens, planarmontage, 'balancename', 'planar', 'keepunused', 'yes');
% ensure there is a type string describing the gradiometer definition
if ~isfield(interp.grad, 'type')
% put the original gradiometer type in (will get _planar appended)
interp.grad.type = ft_senstype(sens);
interp.grad.type = [interp.grad.type '_planar'];
% add the chanpos info back into the gradiometer description
tmplabel = interp.grad.label;
for k = 1:numel(tmplabel)
if ~isempty(strfind(tmplabel{k}, '_dV')) || ~isempty(strfind(tmplabel{k}, '_dH'))
tmplabel{k} = tmplabel{k}(1:end-3);
[ix,iy] = match_str(tmplabel, sens.label);
interp.grad.chanpos(ix,:) = sens.chanpos(iy,:);
% if the original chanpos contained nans, make sure to put nans in the
% updated one as well, and move the updated chanpos values to chanposold
if chanposnans
interp.grad.chanposold = sens.chanpos;
interp.grad.chanoriold = sens.chanori;
interp.grad.labelold = sens.label;
interp.grad.chanpos = nan(size(interp.grad.chanpos));
interp.grad.chanori = nan(size(interp.grad.chanori));
if istlck
% convert the raw structure back into a timelock structure
interp = ft_checkdata(interp, 'datatype', 'timelock');
% copy the trial specific information into the output
if isfield(data, 'trialinfo')
interp.trialinfo = data.trialinfo;
% copy the sampleinfo field as well
if isfield(data, 'sampleinfo')
interp.sampleinfo = data.sampleinfo;
% do the general cleanup and bookkeeping at the end of the function
ft_postamble debug
ft_postamble previous data
% rename the output variable to accomodate the savevar postamble
data = interp;
ft_postamble provenance data
ft_postamble history data
ft_postamble savevar data