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Merge pull request #32 from DouweHorsthuis/2023_update
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,50 +1,52 @@ | ||
| % EEGLAB merge sets, and creates .set file | ||
| % by Douwe Horsthuis updated on 6/21/2021 | ||
| % by Douwe Horsthuis updated on 5/29/2023 | ||
| % ------------------------------------------------ | ||
| subject_list = {'ID_participant1' 'ID_participant2' 'ID_etc'}; %all the IDs for the indivual particpants | ||
| home_path = 'C:\Users\dohorsth\Desktop\cued-boss\'; %place data is (something like 'C:\data\') | ||
| save_path = 'C:\Users\dohorsth\Desktop\cystinosis\cued boss\data\'; %only for the first script, in case you load your data from somewhere special but want to save it somewhere else | ||
| filename = 'cued_boss'; % if your bdf file has a name besides the ID of the participant (e.g. oddball_paradigm) | ||
| blocks = 1; % the amount of BDF files. if different participant have different amounts of blocks, run those participant separate | ||
| subject_list = {'Participant_1_ID' 'Participant_2_ID'}; | ||
| home_path = 'C:\Users\whereisyourdata\'; %place data is (something like 'C:\data\') | ||
| save_path = 'D:\wheretosaveyourdatat\'; %only for the first script, in case you load your data from somewhere special but want to save it somewhere else | ||
| readme_file ='yes';%'yes' or 'no', if yes using [EEG]= readme_to_EEG(EEG, data_path) to read readme file | ||
| eye_tracking ='no';%'yes' or 'no', if yes using edf_to_figure(data_path)to create a figure with avg gaze | ||
| Pausing_yn='y'; %y or n,if you want to first only run people with one block, which is how data is usually collected in our lab, it'll pause if it finds more files | ||
| if strcmpi(readme_file,'yes') || strcmpi(eye_tracking,'yes')% if yes we need to add the functions to the file path | ||
| file_loc=[fileparts(matlab.desktop.editor.getActiveFilename),filesep]; | ||
| addpath(genpath(file_loc));%adding path to your scripts so that the functions are found | ||
| file_loc=[fileparts(matlab.desktop.editor.getActiveFilename),filesep]; | ||
| addpath(genpath(file_loc));%adding path to your scripts so that the functions are found | ||
| end | ||
| for s = 1:length(subject_list) | ||
| clear ALLEEG | ||
| eeglab | ||
| close all | ||
| data_path = [home_path subject_list{s} '\']; | ||
| disp([data_path subject_list{s} '_' filename '.bdf']) | ||
| save_path_indv=[save_path subject_list{s} '\']; | ||
| if blocks == 1 | ||
| %if participants have only 1 block, load only this one file | ||
| EEG = pop_biosig([data_path subject_list{s} '_' filename '_1.bdf']); | ||
| else | ||
| for bdf_bl = 1:blocks | ||
| %if participants have only 1 block, load only this one file | ||
| filename_quick=dir([data_path '/*.BDF']); | ||
| if size(filename_quick,1) == 1 | ||
| EEG = pop_biosig([data_path filename_quick.name]); | ||
| else | ||
| for bdf_bl = 1:size(filename_quick,1) | ||
| if strcmp(Pausing_yn,'y') | ||
| disp(['too many blocks for participant ' subject_list{s}]) | ||
| pause () | ||
| end | ||
| %if participants have more than one block, load the blocks in a row | ||
| %your files need to have the same name, except for a increasing number at the end (e.g. id#_file_1.bdf id#_file_2) | ||
| EEG = pop_biosig([data_path subject_list{s} '_' filename '_' num2str(bdf_bl) '.bdf']); | ||
| EEG = pop_biosig([data_path filename_quick(bdf_bl).name]); | ||
| [ALLEEG, ~] = eeg_store(ALLEEG, EEG, CURRENTSET); | ||
| mkdir(save_path_indv) %if individual folders do not exist | ||
| end | ||
| %since there are more than 1 files, they need to be merged to one big .set file. | ||
| EEG = pop_mergeset( ALLEEG, 1:blocks, 0); | ||
| EEG = pop_mergeset( ALLEEG, 1:size(filename_quick,1), 0); | ||
| end | ||
| mkdir(save_path_indv) %if individual folders do not exist | ||
| %adding info to the EEG structure | ||
| if strcmpi(readme_file,'yes') | ||
| [EEG]= readme_to_EEG(EEG, data_path); | ||
| [EEG]= readme_to_EEG(EEG, data_path); | ||
| else | ||
| EEG.subject = subject_list{s}; %subject ID | ||
| EEG.subject = subject_list{s}; %subject ID | ||
| end | ||
| if strcmpi(eye_tracking,'yes') | ||
| edf_to_figure(data_path); | ||
| saveas(gcf,[data_path subject_list{s} '_ET']) | ||
| saveas(gcf,[save_path_indv subject_list{s} '_ET']) | ||
| close all | ||
| end | ||
| %save the bdf as a .set file | ||
|
|
||
| EEG = pop_saveset( EEG, 'filename',[subject_list{s} '.set'],'filepath',data_path); | ||
| EEG = pop_saveset( EEG, 'filename',[subject_list{s} '.set'],'filepath',save_path_indv); | ||
| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,30 +1,46 @@ | ||
| % Plotting the raw data to see if there are remaining bad or flat channels | ||
| % Created by Douwe Horsthuis last update 5/21/2021 | ||
| % ------------------------------------------------ | ||
| subject_list = {'some sort of ID' 'a different id for a different particpant'}; | ||
| home_path = 'the main folder where you store your data'; | ||
|
|
||
| group_del_channel=[]; %needed for the plot_group_deleted_chan_location function | ||
| for s=1:length(subject_list) | ||
| clear bad_chan; | ||
| fprintf('\n******\nProcessing subject %s\n******\n\n', subject_list{s}); | ||
| data_path = [home_path subject_list{s} '\']; | ||
| EEG = pop_loadset('filename', [subject_list{s} '_exchn.set'], 'filepath', data_path); | ||
| pop_eegplot( EEG, 1, 1, 1); | ||
| prompt = 'Delete channels? If yes, input them all as strings inside {}. If none hit enter '; | ||
| bad_chan = input(prompt); % | ||
| if isempty(bad_chan) ~=1 | ||
| EEG = pop_select( EEG, 'nochannel',bad_chan); | ||
| EEG = pop_saveset( EEG, 'filename',[subject_list{s} '_exchn.set'],'filepath', data_path); | ||
| eeglab | ||
| Group_list={'Name_grp1' 'Name_grp2'}; | ||
| for gr=2:length(Group_list) | ||
| if strcmpi(Group_list{gr},'Name_grp1') | ||
| subject_list = {'ID_1' 'ID_2'}; | ||
| elseif strcmpi(Group_list{gr},'Name_grp2') | ||
| subject_list = {'ID_3' 'ID_4'}; | ||
| else | ||
| disp('not possible') | ||
| pause() | ||
| end | ||
| close all | ||
| home_path = 'D:\whereisthedata\'; %place data is (something like 'C:\data\') | ||
| %need to add the folder with the functions | ||
| file_loc=[fileparts(matlab.desktop.editor.getActiveFilename),filesep]; | ||
| addpath(genpath(file_loc));%adding path to your scripts so that the functions are found | ||
|
|
||
| for s=1:length(subject_list) | ||
| if s==1 | ||
| group_del_channel=[]; %needed for the plot_group_deleted_chan_location function | ||
| end | ||
| clear bad_chan; | ||
| fprintf('\n******\nProcessing subject %s\n******\n\n', subject_list{s}); | ||
| data_path = [home_path subject_list{s} '\']; | ||
| EEG = pop_loadset('filename', [subject_list{s} '_exchn.set'], 'filepath', data_path); | ||
| pop_eegplot( EEG, 1, 1, 1); | ||
| prompt = 'Delete channels? If yes, input them all as strings inside {}. If none hit enter '; | ||
| bad_chan = input(prompt); % | ||
| if isempty(bad_chan) ~=1 | ||
| EEG = pop_select( EEG, 'nochannel',bad_chan); | ||
| EEG = pop_saveset( EEG, 'filename',[subject_list{s} '_exchn.set'],'filepath', data_path); | ||
| end | ||
| close all | ||
|
|
||
| %% creating figures with deleted and bridged channels | ||
| EEG=plot_deleted_chan_location(EEG,data_path); %plotting the location of deleted chan | ||
| % plotting a topoplot with how many channels get for everyone | ||
| [EEG, group_del_channel]=plot_group_deleted_chan_location(EEG,group_del_channel,home_path); | ||
| %% group quality info, ID / % deleted data / seconds of data left / N - deleted channels | ||
| quality(s,:)=[str2double(subject_list{s}), EEG.deleteddata_wboundries,round(EEG.xmax), length(EEG.del_chan)]; | ||
| %% creating figures with deleted and bridged channels | ||
| EEG=plot_deleted_chan_location(EEG,data_path); %plotting the location of deleted chan | ||
| % plotting a topoplot with how many channels get for everyone | ||
| [EEG, group_del_channel]=plot_group_deleted_chan_location(EEG,group_del_channel,home_path,Group_list{gr},length(subject_list)); | ||
|
|
||
| %% group quality info, ID / % deleted data / seconds of data left / N - deleted channels | ||
| quality(s,:)=[str2double(subject_list{s}), str2double(Group_list{gr}) EEG.deleteddata_wboundries,round(EEG.xmax), length(EEG.del_chan)]; | ||
| end | ||
| save([home_path 'participant_info_' Group_list{gr}], 'quality'); | ||
| end | ||
| load([home_path 'participant_info']) %so we can add | ||
| save([home_path 'participant_info'], 'participant_badchan', 'quality'); |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,89 @@ | ||
| function [EEG, group_del_channel]= plot_group_deleted_chan_location(EEG,group_del_channel,save_path,groupname,total_participants) | ||
| %this function plots all the deleted channel locations in on a scalp map | ||
| %Requires a EEGLAB EEG matrix, with the locations of all channels (before some got delted) | ||
| %The EEG.urchanlocs can be created using EEGLAB's chan location function. | ||
| %Requires group_del_channel=[]; before it enters the participant loop | ||
| %it should be run for several subjects on a loop | ||
| %group_del_channel will keep adding the data of the next participant in the | ||
| %loop and create a group average every time | ||
| % it overwrites the figure it creates everytime it adds a new participant | ||
|
|
||
| labels_all = {EEG.urchanlocs(1:64).labels}.'; %stores all the labels in a new matrix | ||
| labels_good = {EEG.chanlocs.labels}.'; %saves all the channels that are in the excom file | ||
| del_chan=setdiff(labels_all,labels_good); %figurs out the deleted channels | ||
| EEG.del_chan=[]; | ||
| for chan=1:length(del_chan) | ||
| for del=1:64 | ||
| if strcmp(del_chan{chan},EEG.urchanlocs(del).labels) | ||
| EEG.del_chan = [EEG.del_chan;EEG.urchanlocs(del)]; %finds locations etc | ||
| end | ||
| end | ||
| end | ||
|
|
||
| %creating the group channels | ||
| if isempty(EEG.del_chan) %in case there is no bad one | ||
| return | ||
| elseif isempty(group_del_channel) %in case it is the first time it is ran | ||
| group_del_channel= EEG.del_chan; | ||
| else | ||
| group_del_channel= [group_del_channel;EEG.del_chan]; %in case there is already data it'll add it together | ||
| end | ||
| % creating a matrix with all 64 channels and how often they have been deleted | ||
| for i=1:64 | ||
| all_del{i}=EEG.urchanlocs(i).labels; | ||
| end | ||
|
|
||
| channel_amounts_deleted=zeros(1,64); | ||
| for i=1:length(group_del_channel) | ||
| for ii=1:64 %loop through every channel | ||
| if strcmpi(group_del_channel(i).labels,all_del(ii)) | ||
| channel_amounts_deleted(ii)=channel_amounts_deleted(ii)+1; | ||
| end | ||
| end | ||
| end | ||
| plot_chan_labels_2=[]; | ||
| for i=1:64 %all channels | ||
| if nonzeros(channel_amounts_deleted(i)) %this looks if it got at least once deleted | ||
| chan_loc=EEG.urchanlocs(i); %ifso, find location info | ||
| chan_loc.labels=channel_amounts_deleted(i); %replace name by amount deleted | ||
| plot_chan_labels_2=[plot_chan_labels_2;chan_loc] ; %store | ||
| end | ||
| end | ||
| %% wrong way of doing it | ||
| % plot_chan_labels=group_del_channel; | ||
| % for i=1:length(group_del_channel) | ||
| % n_chan=0; | ||
| % for ii=1:length(group_del_channel) %looking for each channel | ||
| % if strcmpi(group_del_channel(i).labels,group_del_channel(ii).labels) %comparing that with each channel | ||
| % n_chan=n_chan+1; | ||
| % end | ||
| % end | ||
| % %once compared to each channel we know how often that channel happens | ||
| % plot_chan_labels(i).labels=num2str(n_chan); | ||
| % end | ||
| % | ||
| % %need to delete the duplicated channels, because you can't have more than 64 | ||
| % for i=length(plot_chan_labels):-1:1 %look at all channels (backwards, because we are deleting stuff and we don't want a crash) | ||
| % if plot_chan_labels(i).labels >1 %then we need to find if duplicate exist | ||
| % for ii=length(plot_chan_labels):-1:1 %looking through all existing deleted chans (backwards, because we are deleting stuff and we don't want a crash) | ||
| % if (plot_chan_labels(ii).urchan == plot_chan_labels(i).urchan) && ii~=i %if orginal chan loc is same AND we are sure we are NOT comparing the same with the same | ||
| % plot_chan_labels(i)=[]; %delete it | ||
| % break %need this or it'll crash once the channel is deleted, it should take care | ||
| % end | ||
| % end | ||
| % end | ||
| % end | ||
| %% plotting | ||
| if isempty(group_del_channel) | ||
| figure('Renderer', 'painters', 'Position', [10 10 375 225]); %this is just an empty figure | ||
| elseif length(group_del_channel)==1 | ||
| plot_chan_labels_2=[plot_chan_labels_2;plot_chan_labels_2]; %need 2 to have the function work, but will just plot 2 the same one | ||
| figure; topoplot([],plot_chan_labels_2, 'style', 'fill', 'electrodes', 'labelpoint', 'chaninfo', EEG.chaninfo); | ||
| plot_chan_labels_2=plot_chan_labels_2(1); | ||
| else | ||
| figure; topoplot([],plot_chan_labels_2, 'style', 'map', 'electrodes','labels', 'chaninfo', EEG.chaninfo,'maplimits' , [-2000 2000] ); | ||
| title(groupname,['Numbers represent how often that channel is deleted in ' total_participants ' participants', 'Interpreter', 'none') | ||
| end | ||
| print([save_path groupname '_deleted_channels'], '-dpng' ,'-r300'); | ||
| close all | ||
| end |
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