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You can also download the necessary files here.

In our case, we will want to perform a series of operations which will be repeated over participants:

  • We will first “load” our participants (say their participant number –but it could be anything)
 subject={'100' '101' '103' '104'};
  • Then we will specify how we are going through the loop. In this instance we want each instance of ppts to be passed through. The little voice in the computer goes like this: “for each instance of ppts…”
subject={'100' '101' '103' '104'};
for i=1:length(subject)
  • Last thing, we need make stuff happen. In this instance the first thing we will probably want to do is to load a *.cnt file.
subject={'100' '101'};
for i=1:length(subject)
  EEG = doSomething();

If we run this, MATLAB will doSomething() to the object EEG for all two participants.

2. Preparation of "NightCrew" script

The steps that we have taken so far, at least some operations, are pretty slow (ICA). Before we carry those out there is not much that we can do with the data in terms of analysis. So the plan is to run these steps at night when it doesn't matter whether we can use our computer or not.

For this reason we will talk of two kinds of scripts:

  • “Night Crew”, runs at night and shouldn't include steps with which we often play around.
  • “Dat Crew”, run during the day. They're usually scripts that take no more than 5 minutes to run. It will usually includes steps which we might have to re-configurate or runs steps which give us results we need to attend to directly.

Now is the time to get some serious business going on:

wait 2 seconds

We will go through each of the GUI steps again but this time look at how they can be implemented using command lines.

1. Load CNT

 EEG = pop_loadcnt([subject{i} '.cnt'], 'dataformat', 'int32'); 

pop_loadcnt() loads a CNT file, makes an EEG structure and write it into the EEG object. [subject{i} '.cnt'] is the filename (it can contain a path as well. Instead of manually writing the file name 101.cnt we replace this by passing through the current value of the array subject. The {i} instructs MATLAB to pull out the value equal to the cell the loop is currently reading. 'dataformat', 'int32' are optional parameters. Here they indicate MATLAB that the file format is in 32bits.

2. Load electrode positions, append channel for online reference, label appended channel as the reference

%add electrode positions, and specify online ref
EEG = pop_chanedit(EEG,'append',66,'changefield',{67 'labels' 'Cz'},...
'setref',{'1:67' 'Cz'});

This one line (although it looks slightly long) condenses the cumbersome first steps of re-referenceing: 'append',66 appends a channel after channel number 66.
'changefield', {67 'labels' 'Cz'} changes information about channel 67 namely, its label.
'lookup', '/Users…cap385.elp' looks-up locations for all electrodes in the BESA file.
'setref',{'1:67' 'Cz'} labels Cz as the current reference to all channels.

One final step in that part is needed: referencing to the common average:

%add electrode positions, and specify online ref
EEG = pop_chanedit(EEG,'append',66,'changefield',{67 'labels' 'Cz'},...
'setref',{'1:67' 'Cz'});
%re-reference to common average & reconstruct signal at current reference
EEG = pop_reref( EEG, [],'refloc',struct('labels',{'Cz'},'type',{''},'theta',{0},'radius',{0},'X',{5.2047e-15},'Y',{0},'Z',{85},'sph_theta',{0},'sph_phi',{90},'sph_radius',{85},'urchan',{67},'ref',{'Cz'},'datachan',{0}), 'exclude',[65 66]);

Again this looks like a long one but we don't need to worry much: [] means “all channels” and therefore computes an average reference 'refloc instructs to reconstruct the signal of the online reference electrode. It takes the long struc() as input, which contains the name of the channel, the location and the fact that it is the current reference. 'exlude', [65 66] makes sure channels 65 66 (VEOG & HEOG) do not take part in the average reference, since these channels are not part of the scalp and would add noise to the data.

3. Pre-epoching (file size reduction)

%make epochs for the following triggers
EEG = pop_epoch( EEG, {  '111'  '112'  '113'  '114'  '121'  '122'  '123'  '124'  '131'  '132'  '133'  '134'  '141'  '142'  '143'  '144'  '151'  '152'  '153'  '154'  '211'  '212'  '213'  '214'  '221'  '222'  '223'  '224'  '231'  '232'  '233'  '234'  '241'  '242'  '243'  '244'  '251'  '252'  '253'  '254'  }, [-0.1 1], 'epochinfo', 'yes');
%remove baseline
EEG = pop_rmbase( EEG, [-100 0]);

This one is easy: {'111' '112' … '245'} lists all trigger types for which we want to create epochs. [-0.1 1] min max limits (in seconds) of your epochs 'epochinfo' is set to true and will populate the data set with the even info (you want this on).

We also want to baseline correct those epochs that is what pop_rmbase does. [-100 0] sets the min max limites in MILLIseconds this time.

4. Filtering

  EEG  = pop_basicfilter( EEG,  1:67 , 'Cutoff', [ 0.1 30], 'Design', 'butter', 'Filter', 'bandpass', 'Order',  4 );

5. ICA decomposition

EEG = pop_runica(EEG, 'extended',1,'interupt','on');

6. Other useful commands

Here are some useful commands that you may want to use after some operations, like save intermediary sets for some sets (to which you think you might want to go back), or check that the set contains no problem (especially useful after modifying events and epochs).

  • change the name of a set
EEG.setname = 'NewSetName';
  • save a set
EEG = pop_saveset(EEG, 'filename','101.set','filepath', '/User.../setfolder/');
  • check set consistency
EEG = pop_checkset(EEG);


Now that we have brushed up a few things about loops and now that we have seen a few useful commands for EEGLAB.
Let's see if we can write a simple routine that loads a *.cnt file and exports it (or save it) as an EEGLAB dataset.


One last very handy piece of code to know is the try-catch “loop”

Whenever something goes wrong between the try and catch MATLAB will jump to the statement just after catch and will continue. You can use that to jump over a buggy dataset for example:

  • Here would be the implementation that avoids banging your head on the table 10 times because you've lost a night of processing
subject={'100' '101' '103' '104'};
for i=1:length(subject)
    EEG = pop_loadcnt([subject{i} '.cnt'], 'dataformat', 'int32');
    disp([‘Something went wrong with participant’ subject{i} ‘ Skipping to next participant’]);


Now that we have brushed up a few things about loops and now that we have every command line corresponding to our processing pipeline your task is to write a fully functional Night Crew batch.

  • Making sure that it cycles over participants and
  • that it saves the a set after epoching/baseline, and after filtering in case the filter or ICA step crash.
  • make sure your loop doesn't get stuck because of a buggy dataset.
  • save final dataset. (NOTE. when saving datasets you might want to think of a way to name the dataset with the subject information)
eeglabsessh1b.1391012727.txt.gz · Last modified: 2014/10/08 03:14 (external edit)