demo meeg dataset operationsΒΆ

Matlab output: demo_meeg_dataset_operations

%% MEEG dataset operations
%
% This example shows MVPA analyses performed on MEEG data.
%
% The input dataset involved a paradigm with electrical median nerve
% stimulation for durations of 2s at 20Hz.
%
%
% The code presented here can be adapted for other MEEG analyses, but
% there are a few potential caveats:
% * assignment of targets (labels of conditions) is based here on
%   stimulation periods versus pre-stimulation periods. In typical
%   analyses the targets should be based on different trial conditions, for
%   example as set a FieldTrip .trialinfo field.
% * assignment of chunks (parts of the data that are assumed to be
%   independent) is done randomly. In typical analyses they can be based on
%   different runs, with one chunk value per run.
% * the time window used for analyses is rather small. This means that in
%   particular for time-freq analysis a lot of data is missing, especially
%   for early and late timepoints in the lower frequency bands. For typical
%   analyses it may be preferred to use a wider time window.
% * the current examples do not perform baseline corrections or signal
%   normalizations, which may reduce discriminatory power.
%
% Note: running this code requires FieldTrip.
%
% #   For CoSMoMVPA's copyright information and license terms,   #
% #   see the COPYING file distributed with CoSMoMVPA.           #


%% get timelock data in CoSMoMVPA format

% set configuration
config=cosmo_config();
data_path=fullfile(config.tutorial_data_path,'meg_20hz');

% show dataset information
readme_fn=fullfile(data_path,'README');
cosmo_type(readme_fn);

% reset citation list
cosmo_check_external('-tic');

% load data
data_fn=fullfile(data_path,'subj102_B01_20Hz_timelock.mat');
data_tl=load(data_fn);

% convert to cosmomvpa struct
ds_tl=cosmo_meeg_dataset(data_tl);

% set the target (trial condition)
ds_tl.sa.targets=ds_tl.sa.trialinfo(:,1); % 1=pre, 2=post

% in addition give a label to each trial
index2label={'pre','post'}; % 1=pre, 2=peri/post
ds_tl.sa.labels=cellfun(@(x)index2label(x),num2cell(ds_tl.sa.targets));

% just to check everything is ok
cosmo_check_dataset(ds_tl);

%% Print some information about dataset

% print the size. Note that the number of features is the number of time
% points times the number of channels.
[ns,nf]=size(ds_tl.samples);
fprintf('There are %d samples and %d features\n', ns, nf);

fdim_labels=ds_tl.a.fdim.labels;
fdim_values=ds_tl.a.fdim.values;
ndim=numel(fdim_labels);
for dim=1:ndim
    dim_label=fdim_labels{dim};
    nvalues=numel(fdim_values{dim});
    fprintf('Dimension %d (%s) has %d values\n', dim, dim_label, nvalues);
end

%% Simple data manipulations
% select five trials from post-stim period
post_mask=cosmo_match(ds_tl.sa.labels,'post');
ds_tl_post=cosmo_slice(ds_tl,post_mask);
select_trials=2:2:18;

% the last argument indicates to slice along the first dimension.
% The value 1 is the default and can be omitted
ds_trial=cosmo_slice(ds_tl_post, select_trials, 1);

% select a single channel (multiple channels are also possible but
% makes plotting the samples directly a bit more tricky)
chan={'MEG1042'};
chan_msk=cosmo_dim_match(ds_trial,'chan', chan);

% slice again, this time along the feature dimension (dimension 2),
% to get a new dataset with just one channel
ds_trial_chan=cosmo_slice(ds_trial, chan_msk, 2);

% plot five individual trials
figure();
subplot(2,1,1);
plot(data_tl.time, ds_trial_chan.samples');
title(sprintf('%d trials of sensor %s', numel(select_trials), chan{1}));

% use original full data to select data in just one channel
chan_msk=cosmo_dim_match(ds_tl,'chan', chan);
ds_chan=cosmo_slice(ds_tl, chan_msk, 2);

ds_mean=cosmo_fx(ds_chan,@(x)mean(x,1),'targets');
% plot signal time course averaged over all samples
% note: the 'pre' data has been shifted in time to match the 'post' data
subplot(2,1,2);
[time_dim, time_index]=cosmo_dim_find(ds_mean,'time');
assert(time_dim==2); % it is a feature dimension
plot(ds_mean.a.fdim.values{time_index},ds_mean.samples);
title(sprintf('average of sensor %s', chan{1}));
legend(ds_mean.sa.labels);

% Show citation information
cosmo_check_external('-cite');