Fixed use of old/unused variable name for Yuen's t test of FWs.

Updated header in the decoding analyses script

EXAMPLE_run_decoding_analyses.m

@@ -1,13 +1,12 @@
 % EXAMPLE_run_decoding_analyses.m
 %
-% This script is used for specifying configuration settings for DDTBOX and
-% running decoding analyses on specified subjects and discrimination
-% groups. An explanation of each configurable parameter is described below.
-% Please make copies of this script for your own projects.
-% 
+% This script is used for configuring and running decoding analyses in DDTBOX.  
+% A brief explanation of each configurable parameter is described below.
 % More information on each analysis setting, as well as a tutorial on how
 % to run MVPA in DDTBOX, can be found in the DDTBOX wiki, 
 % available at: https://github.com/DDTBOX/DDTBOX/wiki
+%
+% Please make copies of this script for your own projects.
 % 
 % This script calls decoding_erp.m
 %
@@ -33,29 +32,30 @@
 
 %% Housekeeping
 
+% Clears the workspace and closes all windows
 clear variables;
 close all;
 
 
 
-%% Select Subject Datasets and Discrimination Groups
+%% Select Subject Datasets and Discrimination Groups (dcgs)
 
 % Set the subject datasets on which to perform MVPA
-sbj_todo = [1:4];
+sbj_todo = [1:10];
 
-% Enter the discrimination group(s) for classification. 
+% Enter the discrimination groups (dcgs) for decoding analyses. 
 % Each discrimination group should be in a separate cell entry.
+% Decoding analyses will be run for all dcgs listed here.
 % e.g. dcgs_for_analyses{1} = [1];
 % Two discrimination groups can be entered when using cross-condition decoding.
 % (SVM trained using the first entry/dcg, tested on the second entry/dcg)
 % e.g. dcgs_for_analyses{1} = [1, 2];
 dcgs_for_analyses{1} = [1];
+dcgs_for_analyses{2} = [2];
 
 % Perform cross-condition decoding? 
-% 0 = no
-% 1 = train 1st dcg, test 2nd dcg
-% 2 = train 2nd dcg, test 1st dcg
-cross = 1;
+% 0 = No / 1 = Yes
+cross = 0;
 
 
 
@@ -64,20 +64,20 @@
 % Enter the name of the study (for labeling saved decoding results files)
 study_name = 'EXAMPLE';
 
-% Base directory path (where single subject EEG datasets and channel locations information will be stored)
-bdir = '/Users/danielfeuerriegel/Desktop/DDTBOX Project/MVPA_WORKSHOP/';
+% Base directory path (where single subject EEG datasets and channel locations files are stored)
+bdir = '/Users/username/Desktop/My Project/DDTBOX Analyses/';
 
 % Output directory (where decoding results will be saved)
-output_dir = '/Users/danielfeuerriegel/Desktop/DDTBOX Project/MVPA_WORKSHOP/DECODING_RESULTS/Test/';
+output_dir = '/Users/username/Desktop/My Project/DDTBOX Analyses/Decoding Results/';
 
 % Filepaths of single subject datasets (relative to the base directory)
 sbj_code = {...
 
-    ['DATA/sbj1/SBJ1_full'];... % subject 1
-    ['DATA/sbj2/SBJ2_full'];... % subject 2 
-    ['DATA/sbj3/SBJ3_full'];... % subject 3
-    ['DATA/sbj4/SBJ4_full'];... % subject 4
-    ['DATA/sbj5/SBJ5_full'];... % subject 5
+    ['EEG Data/sbj1'];... % subject 1
+    ['EEG Data/sbj2'];... % subject 2 
+    ['EEG Data/sbj3'];... % subject 3
+    ['EEG Data/sbj4'];... % subject 4
+    ['EEG Data/sbj5'];... % subject 5
 
     };
 
@@ -90,7 +90,6 @@
 
 
 
-
 %% EEG Dataset Information
 
 nchannels = 64; % Number of channels
@@ -99,8 +98,7 @@
 
 % For plotting single subject temporal decoding results
 channel_names_file = 'channel_inf.mat'; % Name of .mat file containing channel labels and channel locations
-channellocs = [bdir, 'locations/']; % Path of directory containing channel information file
-
+channellocs = [bdir, 'channel locations/']; % Path of directory containing channel information file
 
 
 
@@ -120,8 +118,8 @@
 % Enter the condition numbers of the conditions to discriminate between
 % Usage: dcg{discrimination group number} = [condition 1, condition 2];
 % Example: dcg{1} = [1, 2]; to compare conditions 1 and 2 for dcg 1
-dcg{1} = [1, 3]; 
-dcg{2} = [2, 4]; 
+dcg{1} = [1, 2]; 
+dcg{2} = [3, 4]; 
 
 % Support Vector Regression (SVR) condition labels
 % Enter the array entry containing condition labels for each discrimination

analyse_feature_weights_erp.m

@@ -293,7 +293,7 @@
 
                     zero_data_temp = zeros(length(temp_z), 1); % Make vector of zeroes for single-sample comparison
                     [h,p, ~, ~, ~, ~, ~, ~] = yuend_ttest(temp_z, zero_data_temp, ...
-                        'percent', ANALYSIS.trimming_fw, 'alpha', ANALYSIS.pstats, ...
+                        'percent', ANALYSIS.fw.trimming, 'alpha', ANALYSIS.pstats, ...
                         'tail', ANALYSIS.fw.ttest_tail);
 
                 end % of if ANALYSIS.fw.use_robust
@@ -470,7 +470,7 @@
             elseif ANALYSIS.fw.use_robust == 1 % Yuen's t test
 
                 zero_data_temp = zeros(length(temp), 1); % Make vector of zeroes for single-sample comparison
-                [h,p, ~, ~, ~, ~, ~, ~] = yuend_ttest(temp, zero_data_temp, 'percent', ANALYSIS.trimming_fw, 'alpha', ANALYSIS.pstats, 'tail', ANALYSIS.fw.ttest_tail);
+                [h,p, ~, ~, ~, ~, ~, ~] = yuend_ttest(temp, zero_data_temp, 'percent', ANALYSIS.fw.trimming, 'alpha', ANALYSIS.pstats, 'tail', ANALYSIS.fw.ttest_tail);
 
             end % of if ANALYSIS.fw.use_robust
 

decoding_erp.m

@@ -442,7 +442,9 @@ function decoding_erp(cfg)
                         % use specifiied varaible condition only
 
                         if isfield(cfg.regress_data, 'SVR_labels') % Check whether SVR_labels was loaded from regression labels file
+
                             temp_training_labels = cfg.regress_data.SVR_labels{cfg.regr_todo}(1:(ntrs_set * cfg.cross_val_steps));
+
                         else % If using old SVR labels matrices DDTBOX will automatically convert to an array
 
                             fprintf('\n WARNING: SVR labels are stored as a matrix. Coverting to cell array SVR_labels.\n Each cell number corresponds to a column in the SVR labels matrix.\n\n');