[FieldTrip] Follow up question: Computing pca variables (i.e. Latent, and coefficient variables) after ft_componentanalysis

[Schoffelen, J.M. (Jan Mathijs)]

perhaps you may want to check the ‘avg’ field.

JM

On 29 Sep 2017, at 12:19, Isac Sehlstedt <isac.sehlstedt at psy.gu.se<mailto:isac.sehlstedt at psy.gu.se>> wrote:

Dear fieldtripers,

This is a kind reminder of a follow-up question to a previous question with the same mail-topic.

I have included my code below to show what I am doing (in case I have made errors) and print screens (follow dropbox-link below) of the variables I get after the ft_componentanalysis that I get.

Sadly, I cannot see any variable named comp.trial (see ft_componentanalysis-result1.tiff, or ft_componentanalysis-result2.tiff). Also, when running the PCA in matlab, I get a coefficient array that has as many entries as there are time-points in my trials (see matlab_pca_results.tiff) . Why am I not getting that in ft? Is it possible to get that using ft?

Pictures are found using this link:
https://www.dropbox.com/sh/k6ax6bvjb5yi13l/AADvwBzGduIXlCLaPBS4_ELya?dl=0

Very Best,
Isac


————————————————— The code —————————————————

clear all; close all;

%% Load
load('averages_for_ft.mat')



%% define layout
cfg = [];
cfg.elec=PreOdd_ft{1, 1}.elec;
cfg.rotate=90; %rotation around the z-axis in degrees (default = [], which means automatic)
layout = ft_prepare_layout(cfg)



%% Make the computations

% Dummy varibles
Cond1 = [];
Cond2 = [];
theDiff = [];
theDiff_ft = {};


%% Start loop



for i=1:size(Cond1_ft,2)



    %Get the basic condtitions
    curr_Cond2 = Cond2_ft{i}.avg;
    curr_Cond1 = Cond1_ft{i}.avg;



    %Get the basic condtitions
    cfg = [];
    curr_Cond2_ft = ft_timelockanalysis(cfg, Cond2_ft{i});
    curr_Cond1_ft = ft_timelockanalysis(cfg, Cond1_ft{i});



    % Then take the difference of the averages using ft_math
    cfg  = [];
    cfg.operation = 'subtract';
    cfg.parameter = 'avg';
    curr_difference = ft_math(cfg,curr_Cond1_ft,curr_Cond2_ft);
    curr_difference_avg = curr_difference.avg;



    % Creating a struct with the subjectwise differences between conditions
    theDiff_ft{i} = curr_difference



    % constructing concatenated averaged sets for the PCA.
    Cond2 = [Cond2 curr_Cond2];
    Cond1 = [Cond1 curr_Cond1];
    theDiff = [theDiff curr_difference_avg];
end



%% Create dummy subjects in order to run the PCA over subjects



dummy_Cond2 = Cond2_ft{1};
dummy_Cond2.avg = Cond2;
dummy_Cond2.time = 1:1:size(Cond2,2);



dummy_Cond1 = Cond1_ft{1};
dummy_Cond1.avg = Cond1;
dummy_Cond1.time = 1:1:size(Cond1,2);



dummy_theDiff = Cond1_ft{1};
dummy_theDiff.avg = theDiff;
dummy_theDiff.time = 1:1:size(theDiff,2);



%% Run the PCA



cfg = [];
cfg.method  = 'pca';
cfg.layout = layout;

Cond1_comp = ft_componentanalysis(cfg, dummy_Cond1);
Cond2_comp = ft_componentanalysis(cfg, dummy_Cond2);
theDiff_comp = ft_componentanalysis(cfg, dummy_theDiff);



%% Revert back to subject level

cfgCond2 = [];
cfgCond2.unmixing = Cond2_comp.unmixing;
cfgCond2.topolabel = Cond2_comp.topolabel;



cfgCond1 = [];
cfgCond1.unmixing = Cond1_comp.unmixing;
cfgCond1.topolabel = Cond1_comp.topolabel;



cfgtheDiff = [];
cfgtheDiff.unmixing = theDiff_comp.unmixing;
cfgtheDiff.topolabel = theDiff_comp.topolabel;



for i=1:size(Cond1_ft,2)
    Cond1_rs{i} = ft_componentanalysis(cfgCond1, Cond1_ft{i});
    Cond2_rs{i} = ft_componentanalysis(cfgCond2, Cond2_ft{i});
    theDiff_rs{i}= ft_componentanalysis(cfgtheDiff, theDiff_ft{i} );
end
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