These steps were created to gather eyetracking information based on specific regions of interest drawn on stimuli from the experimental task. The following assumes your task was image based, ( or images can be created to mimic the task ), there is XML behavioral output from the task, and your eyetracking was collected through Cigal or Viewpoint. The steps are run based (excluding drawing the ROIs).

The first step is to create behavioral XML files using the BXH/XCEDE tools. Please see the documentation for eprime2xml, showplay2xml, eventstable2xml to create the files.

Your resulting event nodes should include (at minimum):

• the image name
• onset
• duration

For example:

  <event type="image" units="sec">
    <onset>425.894</onset>
    <duration>4</duration>
    <value name="imgName">12F_CA_C.bmp</value>
    <value name="regType">positive</value>
    <value name="trialType">FACE</value>
    <value name="response">1</value>
    <value name="RT">244</value>
  </event>

Next, ROIs should be drawn for each image that you wish to calculate eyetracking data. These ROIs should be saved in an XML based format. Chris Petty has written a matlab based function that will let you draw circles/ellipses or squares/rectangles on input images. The result is an XML which can be merged into your behavioral XML using the bxh tool bxh_eventmerge .

All of the following steps are based on this type of ROI.

- The first thing you need is a text file with all the image paths ( the full path to the image is recommended ).

file_names.txt

/home/petty/eyetracking/sys_service/37M_CA_C.bmp
/home/petty/eyetracking/sys_service/HAI_31.bmp

- Also, either add the path to the actual drawing function “imgROI_keyfcn”, or make a copy of it to your own directory ( the most current copy is \\Munin\Data\Programs\User_Scripts\petty\matlab\ )

- Screen resolution is the resolution of the display that the task was run on. The drawing function creates a representation based on that size.

Here is a sample script to run the drawing function:

drawer_sample.m

%% add path to functions
%addpath \\Munin\Data\Programs\User_Scripts\petty\matlab\
addpath ~/net/munin/data/Programs/User_Scripts/petty/matlab/
 
%% full path to your text file, which contains images ( also full path )
fileList = textread('file_names.txt','%s'); %read in list
XML.rois = {}; %create empty XML
outName = 'my_output.xml'; %name of output .xml with all ROIs ( file will overwrite on each save )
 
%screen resolution of task display
xRes = 1024;
yRes = 768;
scrSize = [ xRes yRes ];
 
imgIDX = 0; %leave 0 to start at first image
 
figUD = struct('imgIDX',imgIDX,'XML',XML);
    %loop that finds images
    imgs = {};
    for file=1:length(fileList)
        [path name ext] = fileparts(fileList{file});
        imgs{file} = struct('fname',[name ext],'fpath',fileList{file});
    end
 
%% this opens the drawing window and runs functions: do not edit %%
fscreen = repmat(uint8(0),[scrSize(2),scrSize(1),3]);
axes_h = axes;
imshow(fscreen,'Parent',axes_h,'InitialMagnification',100,'Border','tight');
text(10,60,{'i - image','e - ellipse','r - rectangle','s - save','q - quit' },'HorizontalAlignment','left','BackgroundColor',[.5 .5 .5],'Color',[1 1 1]);
figH = gcf;
set(figH,'Toolbar','none','Resize','off');
set(figH,'UserData',figUD);
hold on;
 
% calls the functions
set(figH,'KeyPressFcn','imgROI_keyfcn(gcbo,axes_h,imgs,scrSize,outName)');

Once the text file is create with full paths to each image, save/run your version of the above script. You should just need to edit the path, screen size, file names. The script will open a representation of the stimulis screen, press “i” to load the first image.

The current keyboard options are (these appear inside of the drawer) :

i - load the next image
e - draw an ellipse/circle
r - draw a rectangle/square
s - save the XML
q - quit

Once the image is loaded, press “e” or “r” to start your first ROI. There will be a small circle or square depending on the shape you selected. Drag the whole ROI to your desired location, then drag the sides or corners to form your desired shape. Once satisfied double-click on the ROI to name it. The name will appear inside the XML node associated with this ROI as well on the screen inside the associated ROI. You can draw multiple ROIs on each image, but be aware that if they overlap it will affect your final “hit” percentage.

Here's an example of a drawn/labled elliptical roi:

Once finished with the image, press “s” to save the current progress. This will add to the output file on each save, so feel free to save after each region. Just make sure you aren't initially overwritting a previous file! Press “i” to move on to the next image in your list, or “q” to quit if you are finished.

Here's an example of the output:

<rois>
  <roi type="block">
    <name>chin</name>
    <origin>695 529</origin>
    <size>100 100</size>
    <units>pixels</units>
    <value name="imgName">oscar.jpg</value>
  </roi>
  <roi type="block">
    <name>chest</name>
    <origin>447 387</origin>
    <size>100 100</size>
    <units>pixels</units>
    <value name="imgName">oscar.jpg</value>
  </roi>
</rois>

Now its necesary to merge the two sets of XML data together using “bxh_eventmerge”. This will insert your ROI data into the associated nodes from your behavioral XML. This must be run on a machine with the xcede tools. The inputs are the behavioral XML and the ROI XML from the previous step.

merge_rois_into_behav.sh

#!/bin/sh
 
bxh_eventmerge \
  --grabexcludeset 'value[@name="imgName"]' \
  --mergeeventpath '//*[local-name()="rois"]/*[local-name()="roi"]' \
  'value[@name="imgName"]' \
  '.' \
  BEHAVIORAL_run.xml \
  my_drawn_ET_ROI.xml
 
##  comments 
# grabexcludeset ignores any matches
# mergeeventpath is the path to the XML nodes from the ROI.xml, the example above represents
#  <rois>
#       <roi/>
#  </rois>
# 'value[@name="imgName"]' means that i am matching on name="imgName" in both behav and ET_ROI files (INPUTQUERY)
# my GRABQUERY is just '.', means grab everything from ET_ROI.xml matches

The above script assumes that the behavioral XML and ROI XML have the images have the same name attribute:

<value name="imgName">oscar.jpg</value>

The script grabs everything except this node, and merges it into your behavioral xml. By default it writes to a new file with “merged-” appended to the input behavioral html.

Because the ROI XML has all <roi> nodes inside of the <rois> root, the “–mergeeventpath” flag from above is required.

For example, the following image has 2 associated ROIs that have been merged over:

  <event type="image" units="sec">
    <onset>7</onset>
    <duration>4</duration>
    <value name="imgName">HAI_31.bmp</value>
    <value name="regType">look</value>
    <value name="trialType">HAI</value>
    <value name="response">2</value>
    <value name="RT">1266</value>
    <roi type="block">
      <name>r1</name>
      <origin>278 179</origin>
      <endpt>252 398</endpt>
      <units>pixels</units>
    </roi>
    <roi type="circle">
      <name>c1</name>
      <center>691 373</center>
      <size>280 454</size>
      <units>pixels</units>
    </roi>
  </event>

This will convert either Cigal or Viewpoint wks files into CSV ( comma-separated values ), with distinct headers for each column. If the data is in WKS normalized space, it needs to be converted into screen resolution using the same values used in the ROI drawing step. Also, if WKS data is not time-locked to the zero point of your task, a marker should be sent from your task to the eyetracking recording to mark the zero time.

Cigal files will already be in pixel resolution. Jim has also provided some tools to calibrate in cigal, then apply to the viewpoint software. If these methods have been used, the output will also be in pixel resolution. Otherwise, the valid resolution must be input into the preprocessing step.

Chris Petty has written a perl script (preprocess_eyetracking.pl) to handle the preprocessing with various input flags. Can be copied from \\Munin\Data\Programs\User_Scripts\petty, or run directly from cluster nodes.

USAGE: preprocess_eyetracking.pl [options] --input FILE --output FILENAME --type TYPE
 
where [OPTIONS] may be the following:
   --debug        enables debug mode
   --help         displays this help
   --input        eyetracking input data *
   --output       name of output file *
   --type         type of eyetracking data (raw,cigal) *
   --onsetMarker  character than marks zero point in raw files
   --convertpix   flag to convert to pixel dimensions
   --xres         x_screen resolution (only needed if not in pixels)
   --yres         y_screen resolution (only needed if not in pixels)

perl preprocess_eyetracking.pl --input subj_12345.wks --output subj_12345.preproc.wks --type raw --onsetMarker M --convertpix --xres 1024 --yres 768 

• –input: is your eyetracking data ( either .wks or cigal eyepos file )
• –output: is the name to output your csv file ( .txt, .csv, .wks are all file )
• –type: represents “cigal” or “raw” ( different things happen depending on the type, “raw” represents viewpoint files )

if wks input, –onsetMarker is required if the data is not time-locked to your zero point. The time where –onsetMarker is found within the eyetracking data will be subtracted from all following input times, therefore zeroing the onsets.

if wks input is not in pixel dimensions as mentioned above, –convertpix with tell the script to convert the normalized space into pixel dimensions using the X and Y values ( –xres, –yres )

This output will be used in all future steps.

For example:

VP_Code,TotalTime,DeltaTime,X_Gaze,Y_Gaze,Region,PupilWidth,PupilAspect
12,0,M
10,0.000900000000001455,0.033671,391,231,0,0.0844,1.0000
10,0.034000000000006,0.033072,391,231,0,0.0813,0.9630
10,0.0674000000000063,0.033401,391,231,0,0.0781,0.9615
10,0.100700000000003,0.03333,391,231,0,0.0844,0.9630
10,0.134100000000004,0.033404,391,231,0,0.0781,0.9615
10,0.167400000000001,0.033327,403,229,0,0.0781,0.9615
10,0.200800000000001,0.033382,414,228,0,0.0813,1.0000
10,0.234200000000001,0.033359,414,228,0,0.0813,1.0000
10,0.267600000000002,0.033438,426,227,0,0.0813,1.0000
10,0.300899999999999,0.033295,437,226,0,0.0781,0.9615
10,0.334299999999999,0.033418,449,225,0,0.0781,0.9615
10,0.36760000000001,0.03331,449,225,0,0.0813,1.0000
10,0.40100000000001,0.033379,460,224,0,0.0781,0.9615
10,0.434400000000011,0.03336,472,209,0,0.0781,1.0000
10,0.467800000000011,0.033405,460,224,0,0.0781,0.9615

Hits are considered eyetracking points where gaze was within ROIs defined in step 1, during the duration of each image.

Chris written a perl script (merge_eyetracking.pl), which takes the merged behavioral-ROI xml and the preprocessed eyetracking data from the last step to calculate the hits. The script can be copied from: \\Munin\Data\Programs\User_Scripts\petty, or run directly from cluster nodes.

USAGE: merge_eyetracking.pl [options] --eyetracking FILE --behavioral XML --output XML --type

where [OPTIONS] may be the following:
   --debug #        enables debug mode
   --help         displays this help
   --eyetracking  eyetracking input data *
   --behavioral   behavioral XML input *
   --output       name of output XML *
   --type         type of eyetracking data (raw,cigal) *

• –eyetracking: preprocessed eyetracking CSV file
• –behavioral : behavioral XML ( with ROIs merged )
• –ouput : name of .XML to output file
• –type : cigal or raw ( same type from preprocessing step )

perl merge_eyetracking.pl --eyetracking subj_12345_run1.preproc.wks --behavioral merged-subj12345_run1.xml --output subj1234_run1_roiHit.xml --type raw