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Example 4: A Complete Data Processing Script
Note: A complete version of this script is available in Test_Data > Script_Examples > Script4.m. There is also a script named Script4_cleanup.m, which you can use to delete the files created by this script.
Our next example is a more sophisticated and complete script for processing. The script has plenty of internal documentation, so we won't explain it line by line here. However, we'll describe a few new things that were not covered in the previous scripts.
A key difference between this script and the previous scripts is that the previous scripts were designed to make it easy to look at the data from each step with the EEGLAB GUI. This was accomplished by updating the ALLEEG and ALLERP structures at each step. This is useful when you are first learning about scripting. However, once you have the hang of it, it's easier (and requires less memory) to just do everything in the script. At the end, you can look at the EEG with the pop_eegplot() and pop_ploterp() commands (which you can invoke in the command window). You do not even need to launch the EEGLAB GUI to run this script.
ERPsets don't usually take up as much memory as datasets, so you may want to save the final ERP for each subject in ALLERP (as this script does). This can make it easier to do later steps that operate on multiple ERPs (e.g., making grand averages or measuring from multiple subjects' ERPs).
When the script is done running, you can launch the EEGLAB GUI and load in the files that you want to plot. Alternatively, you can plot them from the command line with pop_ploterps. The script saves an ERP for each subject in the first 6 elements in ALLERP, and it saves the unfiltered and filtered grand averages in ALLERP(7) and ALLERP(8), respectively. Thus, you can plot bins 2 and 3 (showing channels 1-17) of the filtered grand average by typing pop_ploterps(ALLERP(8), [2 3], 1:17); on the command line after the script finishes running.
An important element of most scripts—especially ones that take a long time to run—is that they shouldn't try to interact with the user. That is, if you are going to start a script that runs for 2 hours and leave the computer unattended for those 2 hours, you don't want any of the commands to pop up a window that requires you to click on something before the program continues. For example, most ERPLAB commands that create files will, by default, pop up a window if the file already exists, asking you if you want to overwrite the old file. This is a useful safety feature, but it can be an annoyance in a script. For these commands, you may see the parameters 'warning', 'on'. If you change this to 'warning', 'off', the warning window will not be produced if the file exists. The file will simply be overwritten. A good practice is to leave the warnings on the first time you run a script, just to make sure that everything is OK, and then turn the warnings off once you know the script works properly. In the script shown below, the warnings have been turned off.
Here's what the script looks like:
% Clear memory and the command window
clear
clc
% Initialize the ALLERP structure and CURRENTERP
ALLERP = buildERPstruct([]);
CURRENTERP = 0;
% This defines the set of subjects
subject_list = {'S1', 'S2', 'S3', 'S4', 'S5', 'S6'};
nsubj = length(subject_list); % number of subjects
% Path to the parent folder, which contains the data folders for all subjects
home_path = '/Users/luck/Documents/Software_Development/ERPLAB_Toolbox/Test_Data/';
% Set the save_everything variable to 1 to save all of the intermediate files to the hard drive
% Set to 0 to save only the initial and final dataset and ERPset for each subject
save_everything = 1;
% Set the plot_PDFs variable to 1 to create PDF files with the waveforms
% for each subject (set to 0 if you don't want to create the PDF files).
plot_PDFs = 1;
% Loop through all subjects
for s=1:nsubj
fprintf('\n******\nProcessing subject %s\n******\n\n', subject_list{s});
% Path to the folder containing the current subject's data
data_path = [home_path subject_list{s} '/'];
% Check to make sure the dataset file exists
% Initial filename = path plus Subject# plus _EEG.set
sname = [data_path subject_list{s} '_EEG.set'];
if exist(sname, 'file')<=0
fprintf('\n *** WARNING: %s does not exist *** \n', sname);
fprintf('\n *** Skip all processing for this subject *** \n\n');
else
%
% Load original dataset
%
fprintf('\n\n\n**** %s: Loading dataset ****\n\n\n', subject_list{s});
EEG = pop_loadset('filename', [subject_list{s} '_EEG.set'], 'filepath', data_path);
%
% Add the channel locations
% We're assuming the file 'standard-10-5-cap385.elp' is somewhere
% in the path. This can be copied from
% plugins/dipfit2.2/standard_BESA/ inside the eeglab
% folder.
%
fprintf('\n\n\n**** %s: Adding channel location info ****\n\n\n', subject_list{s});
EEG = pop_chanedit(EEG, 'lookup','standard-10-5-cap385.elp');
% Save dataset with _Chan suffix instead of _EEG
EEG.setname = [subject_list{s} '_Chan']; % name for the dataset menu
if (save_everything)
EEG = pop_saveset(EEG, 'filename', [EEG.setname '.set'], 'filepath', data_path);
end
%
% Create EVENTLIST and save (pop_editeventlist adds _elist suffix)
%
fprintf('\n\n\n**** %s: Creating eventlist ****\n\n\n', subject_list{s});
EEG = pop_creabasiceventlist( EEG , 'AlphanumericCleaning', 'on', 'BoundaryNumeric', { -99 }, 'BoundaryString', { 'boundary' });
EEG.setname = [EEG.setname '_elist']; % name for the dataset menu
if (save_everything)
EEG = pop_saveset(EEG, 'filename', [EEG.setname '.set'], 'filepath', data_path);
end
%
% High-pass filter the EEG
% Channels = 1 to 16; High-pass cutoff at 0.1 Hz;
% No lowpass filter; Order of the filter = 2.
% Type of filter = "Butterworth"; Remove DC offset; Filter
% "between" boundary events
%
fprintf('\n\n\n**** %s: High-pass filtering EEG at 0.1 Hz ****\n\n\n', subject_list{s});
EEG = pop_basicfilter( EEG, 1:16 , 'Boundary', 'boundary' 'Cutoff', 0.1, 'Design', 'butter', 'Filter', 'highpass', 'Order', 2);
EEG.setname = [EEG.setname '_hpfilt'];
if (save_everything)
EEG = pop_saveset(EEG, 'filename', [EEG.setname '.set'], 'filepath', data_path);
end
%
% Use Channel operations to insert a bipolar EOG channel
% and re-reference to the average of the earlobes
% Equations are stored in 'chanops_reref_biveog.txt', which
% must be in the home directory for the experiment.
% Save output with _ref suffix.
%
EEG = pop_eegchanoperator(EEG, [home_path 'chanops_reref_biveog.txt']);
EEG.setname = [EEG.setname '_ref'];
if (save_everything)
EEG = pop_saveset(EEG, 'filename', [EEG.setname '.set'], 'filepath', data_path);
end
%
% Use Binlister to sort the bins and save with _bins suffix
% We are assuming that 'binlister_demo_1.txt' is present in the
% home folder.
%
fprintf('\n\n\n**** %s: Running BinLister ****\n\n\n', subject_list{s});
EEG = pop_binlister( EEG , 'BDF', [home_path 'binlister_demo_1.txt'], 'IndexEL', 1, 'SendEL2', 'EEG', 'Voutput', 'EEG' );
EEG.setname = [EEG.setname '_bins'];
if (save_everything)
EEG = pop_saveset(EEG, 'filename', [EEG.setname '.set'], 'filepath', data_path);
end
%
% Extracts bin-based epochs (200 ms pre-stim, 800 ms post-stim. Baseline correction by pre-stim window)
% Then save with _be suffix
%
fprintf('\n\n\n**** %s: Bin-based epoching ****\n\n\n', subject_list{s});
EEG = pop_epochbin( EEG , [-200.0 800.0], 'pre');
EEG.setname = [EEG.setname '_epochs'];
if (save_everything)
EEG = pop_saveset(EEG, 'filename', [EEG.setname '.set'], 'filepath', data_path);
end
% Two rounds of artifact detection, then export eventlist just for fun
% Save the processed EEG to disk because the next step will be averaging
fprintf('\n\n\n**** %s: Artifact detection (moving window peak-to-peak and step function) ****\n\n\n', subject_list{s});
%
% Artifact detection. Moving window. Test window = [-200
% 798]; Threshold = 100 uV; Window width = 200 ms;
% Window step = 50 ms; Channels = 1 to 17; Flags to be activated = 1 & 4
%
EEG = pop_artmwppth( EEG , 'Channel', 1:17, 'Flag', [ 1 4], 'Threshold', 100, 'Twindow', [ -200 798], 'Windowsize', 200, 'Windowstep', 50 );
%
% Artifact detection. Step-like artifacts in the bipolar
% VEOG channel (channel 14, created earlier with Channel Operations)
% Threshold = 30 uV; Window width = 400 ms;
% Window step = 10 ms; Flags to be activated = 1 & 3
EEG = pop_artstep( EEG , 'Channel', 14, 'Flag', [ 1 3], 'Threshold', 30, 'Twindow', [ -200 798], 'Windowsize', 400, 'Windowstep', 10 );
EEG.setname = [EEG.setname '_ar'];
EEG = pop_saveset(EEG, 'filename', [EEG.setname '.set'], 'filepath', data_path);
EEG = pop_exporteegeventlist(EEG, 'Filename', [data_path subject_list{s} '_eventlist_ar.txt']);
% Report percentage of rejected trials (collapsed across all bins)
artifact_proportion = getardetection(EEG);
fprintf('%s: Percentage of rejected trials was %1.2f\n', subject_list{s}, artifact_proportion);
%
% Averaging. Only good trials. Include standard deviation. Save to disk.
%
fprintf('\n\n\n**** %s: Averaging ****\n\n\n', subject_list{s});
ERP = pop_averager( EEG, 'Criterion', 'good', 'DSindex', 6, 'ExcludeBoundary', 'on', 'SEM', 'on');
ERP.erpname = [subject_list{s} '_ERPs']; % name for erpset menu
pop_savemyerp(ERP, 'erpname', ERP.erpname, 'filename', [ERP.erpname '.erp'], 'filepath', data_path, 'warning', 'off');
%
% Filtering ERP. Channels = 1 to 17; No high-pass;
% Lowpass cutoff at 30 Hz; Order of the filter = 2.
% Type of filter = "Butterworth"; Do not remove DC offset
%
fprintf('\n\n\n**** %s: Low-pass filtering ERP at 30 Hz ****\n\n\n', subject_list{s});
ERP = pop_filterp( ERP,1:17 , 'Cutoff',30, 'Design', 'butter', 'Filter', 'lowpass', 'Order',2 );
ERP.erpname = [ERP.erpname '_30Hz']; % name for erpset menu
if (save_everything)
pop_savemyerp(ERP, 'erpname', ERP.erpname, 'filename', [ERP.erpname '.erp'], 'filepath', data_path, 'warning', 'off');
end
%
% Bin Operations. Create a difference wave and save with _diff suffix
% Do this on the unfiltered data, so first reload unfiltered file
% Then do a second round of bin operations and save with _plus suffix
%
fprintf('\n\n\n**** %s: Bin Operations (two passes) ****\n\n\n', subject_list{s});
fname = [subject_list{s} '_ERPs.erp']; % Re-create filename for unfiltered ERP
ERP = pop_loaderp( 'filename', fname, 'filepath', data_path ); % Load the file
% Now make the difference wave, directly specifying the
% equation that modifies the existing ERPset
ERP = pop_binoperator( ERP, {'b3= b2-b1 label Rare minus Frequent difference wave' });
ERP.erpname = [ERP.erpname '_diff']; % name for erpset menu
if (save_everything)
pop_savemyerp(ERP, 'erpname', ERP.erpname, 'filename', [ERP.erpname '.erp'], 'filepath', data_path, 'warning', 'off');
end
% Now we will do bin operations using a set of equations
% stored in the file 'bin_equations.txt', which must be in
% the home folder for the experiment
ERP = pop_binoperator( ERP, [home_path 'bin_equations.txt']);
ERP.erpname = [ERP.erpname '_plus']; % name for erpset menu
pop_savemyerp(ERP, 'erpname', ERP.erpname, 'filename', [ERP.erpname '.erp'], 'filepath', data_path, 'warning', 'off');
% Save this final ERP in the ALLERP structure. This is not
% necessary unless you want to see the ERPs in the GUI or if you
% want to access them with another function (e.g., pop_gaverager)
CURRENTERP = CURRENTERP + 1;
ALLERP(CURRENTERP) = ERP;
if (plot_PDFs)
pop_ploterps(ERP, [1 2], 1:17);
pop_exporterplabfigure(ERP, 'Filepath', data_path, 'Format', 'pdf', 'tag', {'ERP_figure' 'Scalp_figure'});
end
end % end of the "if/else" statement that makes sure the file exists
end % end of looping through all subjects
% Make a grand average. The final ERP from each subject was saved in
% ALLERP, and we have nsubj subjects, so the indices of the ERPs to be averaged
% together are 1:nsubj
% We'll also create a filtered version and save it
ERP = pop_gaverager( ALLERP , 'Erpsets', [ 1:nsubj], 'ExcludeNullBin', 'on', 'SEM', 'on' ); %'Criterion', 100 is left over from a previous version
ERP.erpname = 'grand_avg'; % name for erpset menu
ERP = pop_savemyerp(ERP, 'filename', [ERP.erpname '.erp'], 'filepath', home_path, 'warning', 'off');
CURRENTERP = CURRENTERP + 1;
ALLERP(CURRENTERP) = ERP;
ERP = pop_filterp( ERP,1:17 , 'Cutoff',30, 'Design', 'butter', 'Filter', 'lowpass', 'Order',2 );
ERP.erpname = [ERP.erpname '_30Hz']; % name for erpset menu
ERP = pop_savemyerp(ERP, 'filename', [ERP.erpname '.erp'], 'filepath', home_path, 'warning', 'off');
CURRENTERP = CURRENTERP + 1;
ALLERP(CURRENTERP) = ERP;
% Measure the mean amplitude from 300-600 ms in bins 2 and 3, channels 11-13.
% Save the results in a variable named "values" and in a file named
% "measures.txt" in the home folder for the experiment.
values = pop_geterpvalues( ALLERP, [300 600], [2 3], 11:13 , 'Baseline', 'pre', 'Erpsets', [1:nsubj], 'Filename', [home_path 'measures.txt'], 'Filename', 'measurement', 'Measure', 'meanbl', 'Resolution',1 );
fprintf('\n\n\n**** FINISHED ****\n\n\n');