Skip to content
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Go to file
Cannot retrieve contributors at this time
function [timelock] = ft_timelockanalysis(cfg, data)
% FT_TIMELOCKANALYSIS computes the timelocked average ERP/ERF and optionally computes
% the covariance matrix over the specified time window.
% Use as
% [timelock] = ft_timelockanalysis(cfg, data)
% The data should be organised in a structure as obtained from the FT_PREPROCESSING
% function. The configuration should be according to
% = Nx1 cell-array with selection of channels (default = 'all'), see FT_CHANNELSELECTION for details
% cfg.latency = [begin end] in seconds, or 'all', 'minperiod', 'maxperiod', 'prestim', 'poststim' (default = 'all')
% cfg.trials = 'all' or a selection given as a 1xN vector (default = 'all')
% cfg.keeptrials = 'yes' or 'no', return individual trials or average (default = 'no')
% cfg.nanmean = string, can be 'yes' or 'no' (default = 'yes')
% cfg.normalizevar = 'N' or 'N-1' (default = 'N-1')
% cfg.covariance = 'no' or 'yes' (default = 'no')
% cfg.covariancewindow = [begin end] in seconds, or 'all', 'minperiod', 'maxperiod', 'prestim', 'poststim' (default = 'all')
% cfg.removemean = 'yes' or 'no', for the covariance computation (default = 'yes')
% 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.
% FIXME if input is one raw trial, the covariance is not computed correctly
% Undocumented local options:
% cfg.preproc
% Deprecated options:
% cfg.blcovariance
% cfg.blcovariancewindow
% cfg.normalizecov
% cfg.vartrllength
% Copyright (C) 2018, Jan-Mathijs Schoffelen
% Copyright (C) 2003-2006, Markus Bauer
% Copyright (C) 2003-2022, Robert Oostenveld
% 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
% check if the input data is valid for this function
data = ft_checkdata(data, 'datatype', {'raw+comp', 'raw'}, 'feedback', 'yes', 'hassampleinfo', 'yes');
% 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, 'forbidden', {'normalizecov'});
cfg = ft_checkconfig(cfg, 'forbidden', {'blcovariance', 'blcovariancewindow'});
cfg = ft_checkconfig(cfg, 'renamed', {'blc', 'demean'});
cfg = ft_checkconfig(cfg, 'renamed', {'blcwindow', 'baselinewindow'});
% set the defaults
cfg.preproc = ft_getopt(cfg, 'preproc' , []); = ft_getopt(cfg, 'channel' , 'all');
cfg.latency = ft_getopt(cfg, 'latency' , 'all');
cfg.trials = ft_getopt(cfg, 'trials' , 'all', 1);
cfg.keeptrials = ft_getopt(cfg, 'keeptrials' , 'no');
cfg.vartrllength = ft_getopt(cfg, 'vartrllength' , 0);
cfg.nanmean = ft_getopt(cfg, 'nanmean' , 'yes');
cfg.normalizevar = ft_getopt(cfg, 'normalizevar' , 'N-1');
cfg.covariance = ft_getopt(cfg, 'covariance' , 'no');
cfg.covariancewindow = ft_getopt(cfg, 'covariancewindow' , 'all');
cfg.removemean = ft_getopt(cfg, 'removemean' , 'yes'); = ft_getopt(cfg, 'feedback' , 'text');
% create logical flags for convenience
keeptrials = istrue(cfg.keeptrials);
computecov = istrue(cfg.covariance);
% ensure that the preproc specific options are located in the cfg.preproc substructure
cfg = ft_checkconfig(cfg, 'createsubcfg', {'preproc'});
if ~isempty(cfg.preproc)
% preprocess the data, i.e. apply filtering, baselinecorrection, etc.
fprintf('applying preprocessing options\n');
if ~isfield(cfg.preproc, 'feedback') =;
data = ft_preprocessing(cfg.preproc, data);
[cfg.preproc, data] = rollback_provenance(cfg.preproc, data);
% compute the covariance matrix, if requested
if computecov
tmpcfg = keepfields(cfg, {'trials', 'channel', 'tolerance', 'showcallinfo', 'trackcallinfo', 'trackusage', 'trackdatainfo', 'trackmeminfo', 'tracktimeinfo', 'checksize'});
tmpcfg.latency = cfg.covariancewindow;
datacov = ft_selectdata(tmpcfg, data);
% restore the provenance information
[dum, datacov] = rollback_provenance(cfg, datacov); % not sure what to do here
datacov = ft_checkdata(datacov, 'datatype', 'timelock');
if isfield(datacov, 'trial')
[nrpt, nchan, ntime] = size(datacov.trial);
% if the data structure has only a single trial
nrpt = 1;
[nchan, ntime] = size(datacov.avg);
datacov.trial = shiftdim(datacov.avg, -1);
datacov = rmfield(datacov, 'avg');
datacov.dimord = 'rpt_chan_time';
% pre-allocate memory space for the covariance matrices
if keeptrials
covsig = nan(nrpt, nchan, nchan);
covsig = zeros(nchan, nchan);
allsmp = 0;
% compute the covariance per trial
for k = 1:nrpt
dat = reshape(datacov.trial(k,:,:), [nchan ntime]);
datsmp = isfinite(dat);
if ~all(ismember(sum(datsmp,1), [0 nchan]))
ft_error('channel specific NaNs are not supported for covariance computation');
numsmp = sum(datsmp(1,:));
if istrue(cfg.removemean)
dat = ft_preproc_baselinecorrect(dat);
numsmp = max(numsmp-1,1);
dat(~datsmp) = 0;
if keeptrials
covsig(k,:,:) = dat*dat'./numsmp;
covsig = covsig + dat*dat';
allsmp = allsmp + numsmp;
% normalisation will be done after the for-loop
if ~keeptrials
covsig = covsig./allsmp;
% select trials and channels of interest
orgcfg = cfg;
tmpcfg = keepfields(cfg, {'trials', 'channel', 'tolerance', 'latency', 'showcallinfo', 'trackcallinfo', 'trackusage', 'trackdatainfo', 'trackmeminfo', 'tracktimeinfo', 'checksize'});
data = ft_selectdata(tmpcfg, data);
% restore the provenance information
[cfg, data] = rollback_provenance(cfg, data);
% do not use the default option returned by FT_SELECTDATA, but the original one for this function
cfg.nanmean = orgcfg.nanmean;
% do a sanity check
if isempty(data.trial)
if ~isempty(cfg.trials)
ft_error('there are no trials selected');
ft_error('there are no trials in the input data');
if keeptrials
% convert to a timelock structure with trials kept and NaNs for missing data points, when there's only a single trial in the input data
% structure, this leads to an 'avg' field, rather than a 'trial' field, and also the trialinfo is removed, so keep separate before conversion
if isfield(data, 'trialinfo'), trialinfo = data.trialinfo; end
data = ft_checkdata(data, 'datatype', {'timelock+comp' 'timelock'});
if keeptrials && isfield(data, 'trial')
% nothing required here
elseif keeptrials && ~isfield(data, 'trial')
% don't know whether this is a use case
data.trial = shiftdim(data.avg, -1);
if exist('trialinfo', 'var')
data.trialinfo = trialinfo;
elseif ~keeptrials
% whether to normalize the variance with N or N-1, see VAR
normalizewithN = strcmpi(cfg.normalizevar, 'N');
% compute a running sum average/var etc. to save memory
% the code below tries to construct a general time-axis where samples of all trials can fall on
% find the earliest beginning and latest ending
begtime = min(cellfun(@min, data.time));
endtime = max(cellfun(@max, data.time));
% find 'common' sampling rate
fsample = 1./nanmean(cellfun(@mean, cellfun(@diff,data.time, 'uniformoutput', false)));
% estimate number of samples
nsmp = round((endtime-begtime)*fsample) + 1; % numerical round-off issues should be dealt with by this round, as they will/should never cause an extra sample to appear
% construct general time-axis
time = linspace(begtime, endtime, nsmp);
nchan = numel(data.label);
ntrial = numel(data.trial);
% placeholder for running sums
tmpsum = zeros(nchan, length(time));
tmpssq = tmpsum;
tmpdof = tmpsum;
begsmp = nan(ntrial, 1);
endsmp = nan(ntrial, 1);
% do a 2-pass running sum, sacrificing speed for numeric stability
for i=1:ntrial
begsmp(i) = nearest(time, data.time{i}(1));
endsmp(i) = nearest(time, data.time{i}(end));
tmp = data.trial{i};
tmpdof(:,begsmp(i):endsmp(i)) = isfinite(tmp) + tmpdof(:,begsmp(i):endsmp(i));
if istrue(cfg.nanmean)
tmp(~isfinite(tmp)) = 0;
tmpsum(:,begsmp(i):endsmp(i)) = tmp + tmpsum(:,begsmp(i):endsmp(i));
avgmat = tmpsum ./ tmpdof;
tmpsum = zeros(nchan, length(time));
for i=1:ntrial
tmp = data.trial{i};
tmp = tmp - avgmat(:,begsmp(i):endsmp(i));
if istrue(cfg.nanmean)
tmp(~isfinite(tmp)) = 0;
tmpsum(:,begsmp(i):endsmp(i)) = tmp + tmpsum(:,begsmp(i):endsmp(i));
tmpssq(:,begsmp(i):endsmp(i)) = tmp.^2 + tmpssq(:,begsmp(i):endsmp(i));
dofmat = tmpdof;
%avgmat = tmpsum ./ tmpdof;
varmat = tmpssq ./ tmpdof - (tmpsum ./ tmpdof).^2;
if normalizewithN
% just to be sure
varmat(dofmat<=0) = NaN;
varmat = varmat .* (dofmat ./ (dofmat-1));
% see
% the fieldtrip/external/stats/nanvar implementation behaves differently here than Mathworks VAR and NANVAR implementations
varmat(dofmat<=1) = NaN;
% collect the results
timelock = keepfields(data, {'time', 'grad', 'elec', 'opto', 'topo', 'topodimord', 'topolabel', 'unmixing', 'unmixingdimord', 'label'});
if ~keeptrials
timelock.avg = avgmat;
timelock.var = varmat;
timelock.dof = dofmat;
timelock.time = time;
timelock.dimord = 'chan_time';
timelock = copyfields(data, timelock, {'trial' 'sampleinfo', 'trialinfo'});
timelock.dimord = 'rpt_chan_time';
if computecov
timelock.cov = covsig;
% do the general cleanup and bookkeeping at the end of the function
ft_postamble debug
ft_postamble previous data
ft_postamble provenance timelock
ft_postamble history timelock
ft_postamble savevar timelock