several fixes/updates

Author: DouweHorsthuis

README.md

@@ -91,7 +91,7 @@ Lastly this script uses eeglab's clean_artifacts function deletes the bad channe
 This script plots all the data in EEGlab as continues data and allows you to delete channels manually. 
 
 #### D_preprocces2
-This script will double check and fix any potential trigger issue we encountered. It saves a Matrix with the information for each individual participant. 
+This script will double check and fix any potential trigger issue we encountered. It saves a Matrix with the information for each individual participant. **This script can be skipped** It is only useful for documenting triggers. We added the [pop_rejcont](https://github.com/wojzaremba/active-delays/blob/master/external_tools/eeglab11_0_4_3b/functions/popfunc/pop_rejcont.m) in the next script and this deletes triggers sometimes, so we need to double check triggers again (see [G_preprocces5](#g_preprocces5)).
 
 #### E_preprocces3
 This script will do an average reference.  
@@ -105,16 +105,22 @@ After that we use [pop_rejcont](https://github.com/wojzaremba/active-delays/blob
 This script loads a file with all the original channels, deletes the externals and uses these file locations to interpolate the channels of the corresponding's subjects data.  
 In the case of 160 channel data, it uses the [transform_n_channels](https://github.com/CognitiveNeuroLab/Interpolating_160ch_to_64ch_eeglab) function to interpolate the remaining channels not to the original 160, but to 64 channel data so that it is the same as all the other data. For this to work Matlab needs to know the location of 2 things, the trannsform_n_channel.m file and the EEG files called 64.set and 64.fdt.
 
+#### G_preprocces5
+In this script, we first make sure that the triggers are still in the right place. Due to the extra cleaning we did with the pop_rejcont function in [E_preprocces3](#e_preprocces3) it is possible that the triggers got deleted if the corresponding continues data were too noisy. If they got deleted, the scripts calculates what the time of the onset of that deleted part of data was and uses that instead as the latency of the trigger.
+
 ### Power Frequency Analysis
-In the G_PSD_pwelsh script, we first make sure that the triggers are still in the right place. Due to the extra cleaning we did with the pop_rejcont function in [E_preprocces3](#e_preprocces3) it is possible that the triggers got deleted if the corresponding continues data were too noisy. If they got deleted, the scripts calculates what the time of the onset of that deleted part of data was and uses that instead as the latency of the trigger. 
+ 
 
 After that we use the the [pwelch function of Matlab](https://www.mathworks.com/help/signal/ref/pwelch.html) and a log tranformation of the results to get the power frequency results.  
 
 # add here what channels we use, for now it's just indivual but we will change this to groups and averages of those groups
 
-### Microstates
+### Microstates Groups & all
 this script follows the code as descibed in Poulsen, A. T., Pedroni, A., Langer, N., & Hansen, L. K. (2018). Microstate EEGlab toolbox: An introductory guide. [See their guide in bioRxiv for more information.](https://www.biorxiv.org/content/10.1101/289850v1)
 
+In the first part the analysis only focuses on the group level. Since we use both eyes open and eyes closed data, we want to check how many microstates are suggested for both, so we can choose the best (same) amount for both. In the case of patient/control group we would need to compare all 4 the suggestions. Running the whole script would take a lot of time that wasn't needed.
+
+The second script will do everything again (since now you know how many microstates you want) and backfits it on the individual EEGs. Giving both plots per subject and adds data to the EEG structure to do stats on.
 
 
 ## License

src/E_preprocces3.m

@@ -1,11 +1,11 @@
 clear variables
 eeglab
-Group = {'Aging' 'Control' 'ASD'}; %'Control'
+Group = { 'ASD' 'Control' 'Aging'}; %
 
 for g=1:length(Group)
     switch Group{g}
         case 'Control'
-            home_path  = '\\data.einsteinmed.org\users\Filip Ana Douwe\Resting state data\Control\';
+            home_path  = 'C:\Users\dohorsth\Desktop\Testing restingstate\Control\';
             subject_list = {'12512' '12648' '12651' '12707' '12727' '12739' '12750' '12815' '12898' '12899' '10033' '10130' '10131' '10158' '10165' '10257' '10281' '10293' '10360' '10369' '10384' '10394' '10407'  '10438' '10446' '10451' '10463' '10467' '10476' '10501' '10526' '10534' '10545' '10561' '10562' '10581' '10585' '10616' '10615' '10620' '10639' '10748' '10780' '10784' '10822' '10858' '10906' '10915' '10929' '10935'  '10844' '10956'  '12005' '12007' '12010' '12215' '12328' '12360' '12413' };% ------------------------------------------------
         case 'ASD'
             home_path  = 'C:\Users\dohorsth\Desktop\Testing restingstate\ASD\';
@@ -25,7 +25,7 @@
         % Path to the folder containing the current subject's data
         data_path  = [home_path subject_list{s} '\\'];
         fprintf('\n\n\n**** %s: Loading dataset ****\n\n\n', subject_list{s});
-        EEG = pop_loadset('filename', [subject_list{s} '_triggerfix.set'], 'filepath', data_path);
+         EEG = pop_loadset('filename', [subject_list{s} '_triggerfix.set'], 'filepath', data_path);
         %re-referencing, if refchan is empty this get's skipped
         if isempty(refchan)~=1 %if no re-reference channels chose this gets skipped
             for j=1:length(EEG.chanlocs)
@@ -64,7 +64,6 @@
         EEG = pop_runica(EEG, 'extended',1,'interupt','on','pca',pca); %using runica function, with the PCA part
         EEG = eeg_checkset( EEG );
         EEG = pop_saveset( EEG, 'filename',[subject_list{s} '_ica.set'],'filepath', data_path);
-        
         %organizing components
         clear bad_components brain_ic muscle_ic eye_ic hearth_ic line_noise_ic channel_ic other_ic
         EEG = iclabel(EEG); %does ICLable function
@@ -92,9 +91,7 @@
             EEG = pop_subcomp( EEG, [bad_components], 0); %excluding the bad components
             close all
         else %instead of only plotting bad components it will plot all components
-            title(subject_list{s}); text( 0.2,0.5, 'there are no eye-components found')
-            pop_topoplot(EEG, 0, 1:length(ICA_components) ,subject_list{s},[ceil(sqrt(length(ICA_components))) ceil(sqrt(length(ICA_components)))] ,0,'electrodes','on');
-            title(subject_list{s});
+            title(subject_list{s}); text( 0.2,0.5, 'there are no eye-components found')    
             print([figure_path subject_list{s} '_Bad_ICs_topos'], '-dpng' ,'-r300');
         end
         title(subject_list{s});

src/F_preprocces4.m

@@ -5,12 +5,12 @@
 clear variables
 eeglab
 close all
-Group = {'Aging' 'ASD'};%'Control'
+Group = {'ASD'};%'Control'
 name_paradigm = 'restingstate'; % this is needed for saving the table at the end
 for g=1:length(Group)
     switch Group{g}
         case 'Control'
-            home_path  = '\\data.einsteinmed.org\users\Filip Ana Douwe\Resting state data\Control\';
+            home_path  = 'C:\Users\dohorsth\Desktop\Testing restingstate\Control\';
             subject_list = {'12512' '12648' '12651' '12707' '12727' '12739' '12750' '12815' '12898' '12899' '10033' '10130' '10131' '10158' '10165' '10257' '10281' '10293' '10360' '10369' '10384' '10394' '10407'  '10438' '10446' '10451' '10463' '10467' '10476' '10501' '10526' '10534' '10545' '10561' '10562' '10581' '10585' '10616' '10615' '10620' '10639' '10748' '10780' '10784' '10822' '10858' '10906' '10915' '10929' '10935'  '10844' '10956'  '12005' '12007' '12010' '12215' '12328' '12360' '12413' };% ------------------------------------------------
         case 'ASD'
             home_path  = 'C:\Users\dohorsth\Desktop\Testing restingstate\ASD\';