Saving data to .MAT

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The umIToolbox offers the possibility to extract the data from several recordings and save to a .mat file to be used outside the toolbox.

This section describes how the data is structured and gives an example on how to work with the extracted data.

Important
We assume that the data was already preprocessed and saved in .mat files and selected in the umIToolbox Analysis tab. To learn how to select the data, click here.

The data structure

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The .mat file is generated using the Save2Mat tool in the Analysis tab from the umIToolbox app. The file contains the lists of groups (list_of_grous), events (list_of_events) and observations (list_of_observations) stored as cell arrays of characters. In addition the data and metadata of each recording are stored in a variable named stats_data.

The stats_data consists of a structure array in which each item corresponds to an acquisition (i.e. recording) selected in the object Tree from umIT's Analysis tab.

Each element of the structure array contains the following fields:

Field	Description
groupID	The name of the experimental group
SubjectID	The name of the subject
AcquisitionID	The name of the acquisition/recording
ModalityID	The name of the recording modality
RecStartDateTime	Timestamp of the recording (start) with format YYYY-MM-DD HH:mm:ss
dataFile	Full path to the .matfile containing the recording's data
labels	Cell array containing labels for each data point. The labels are generated by one of the Analysis functions
observationID	Cell array with the names of each observation (e.g. ROIs)
data	Cell array with the data of each observation (in the same order as observationID)
dataSize	Cell array the size of each element of data
AcquisitionIndx	Index of acquisition. For instance, an AcquisitionIndx of 3 means that this is the third recording of a given Subject
b_isBaseline	Boolean indicating if this recording is considered as baseline
b_hasEvents	Boolean indicating if the data contains events
gIndx	Group index from the list_of_groups list.
sIndx	Subject index. For instance, an sIndx of 2 means that the SubjectID is the second subject in the list of subjects (list not saved)
rIndx	Array of observation indices. The list of indices associated with each observation listed in the observationID field. Each index points to the position of an element in the list_of_observations list
eIndx	Array of event indices. The list of indices associated with each event listed in the list_of_events list. If the data has no events, the index is equal to zero

Example code

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Here is an example on how to use the data extracted from umIT:

            
                             
% In this example, the data consists of scalar values (single values)
% corresponding to the response amplitude of the primary motor cortex 
% following a sensory stimulation.
% The data consists of a single group containing four subjects and
% four acquisition per subject.
%% Load the extracted data:
s = load('test_dataStruct.mat');
%% Get the data of each group
% Here, we will create column vectors of the acquisition indices and data
% values for the observation "M1_R"

% Preallocate variables
x = zeros(length(s.stats_data),1, 'single'); 
y = x;
% Populate x and y variables with the acquisition index and data values
% respectively:
for ind = 1:length(s.stats_data)
    x(ind) =  s.stats_data(ind).AcquisitionIndx;
    % Find observation "M1_R";
    iObs = strcmpi('M1_R', s.stats_data(ind).observationID);
    % Get data from the selected observation:
    y(ind) = s.stats_data(ind).data{iObs};
end
%% Plot data:
% Now, we will use the toolbox's plotting function "stripchart" to plot 
% boxplots of the responses across acquisitions:
ax = stripchart(x,y,'Boxplot', true);
% Customize axis:
ax.Title.String = 'M1 response across time';
ax.XLabel.String = 'Acquisition index';
ax.YLabel.String = 'Response amplitude';
                        
        

Here is the resulting plot:

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