Brainstorm_to_Cosmo.m

cd '' % directory with the files exported from BS, with the first file being the reference file in FieldTrip format
% note that for this script to work the files in the target directory have to be organized
% such as that the first half refer to condition A and the second half to condition B

files=dir('*.mat'); % this will read all the .mat files exported from BS
files.name
% files = files([1:(length(files)-1)]) % if an out.m is already present in the folder, this line will remove it from 'file'
% files.name

real = load(files(1).name) % read the reference file
datasets{1} = real.avg % put the data of the first epoch in datasets
for iFile=2:length(files)
    temp = load(files(iFile).name);
    datasets{iFile} = temp.F;
end
ds0 = cat(3,datasets{:}); % build the 3D matrix
ds0 = permute(ds0, [3 1 2]); % permute

save out.mat ds0 % save matrix in out.m

% the rest of the script contains some minor modifications of the code present
% on the Cosmo website on the following page: http://www.cosmomvpa.org/faq.html#import-brainstorm-data

% Example script showing importing BrainStorm M/EEG time-locked data into
% CoSMoMVPA.
%
% The result is a CoSMoMVPA dataset structure 'ds'
%
% NNO Sep 2017
%
% Input data:
%   '195_trial001.mat' : contains data in FieldTrip timelock struct format,
%                        with data from a single trial, for 701 time points
%                        and 319 channels
%
%                             template_trial =
%
%                                 dimord: 'chan_time'
%                                    avg: [319x701 double]
%                                   time: [1x701 double]
%                                  label: {319x1 cell}
%                                   grad: [1x1 struct]
%
%   'out.mat'          : 3D array of size 180 x 319 x 701
%                           180 trials, 90 of condition A followed by
%                                       90 of condition B
%                           319 channels
%                           701 time points
%
% Indices of the trial conditions
% - the first 90 trials are condition A
% - the nextt 90 trials are condition B
trials_condA=1:90;
trials_condB=91:180;

% load all data
all_data=importdata('out.mat');

% get template trial
template_trial=load(files(1).name);

% set trial condition in a vector
targets=NaN(numel(trials_condA)+numel(trials_condA),1);
targets(trials_condA)=1;
targets(trials_condB)=2;

% count number of trials
n_trials=numel(targets);

% just verify that trial count matches
assert(numel(targets)==size(all_data,1),'trial count mismatch');

% allocate space for output
ds_cell=cell(n_trials,1);

% convert each trial
for k=1:n_trials
    % make a copy from the template
    trial=template_trial;

    % take data from trial
    trial.avg(:,:)=squeeze(all_data(k,:,:));

    % convert to CoSMo struct
    ds_trial=cosmo_meeg_dataset(trial);

    % all trials are assumed to be independent
    ds_trial.sa.chunks=k;

    % set condition
    ds_trial.sa.targets=targets(k);

    % since we are merging trials, this is not an average anymore
    % (this is a bit of a hack since we're not really importing from
    % FieldTrip, but BrainStorm sets (arguablye misleading) the single
    % trial data as if it is an average)
    ds_trial.a.meeg=rmfield(ds_trial.a.meeg,'samples_field');

    % store dataset
    ds_cell{k}=ds_trial;
end

% merge all trials into a big dataset
ds_tl=cosmo_stack(ds_cell);