This is a toolkit to analyse and visualize eye tracking data. It provides the following functionality:

Feature Extraction

This involves extraction of parameters or meta-data related to blinks, fixations, saccades, microsaccades and pupil diameter. The features extracted are as follows:

Blink Fixations Saccades Microsaccades Pupil Revisits to AOI/ROI
Count Count Count Count Size Count
Avg Duration Avg Duration Velocity Velocity Time to Peak First Pass Duration
Max Duration Max Duration Amplitude Amplitude Peak Size Second Pass Duration
  Duration Duration Avg Size  
      Slope  
      Area Under Curve  

Statistical Analysis

After extraction of features, PyTrack can perform tests such as the student T-Test, Welch T-Test, ANOVA, RMANOVA, n-way ANOVA and Mixed ANOVA. The between and within group factors can be specified.

Visualization

PyTrack can generate a variety of plots. The visualization is through an interactive GUI. The plots that can be generated are as follows:

  1. Fixation plot
  2. Individual subject gaze heat map
  3. Aggregate subject gaze heat map
  4. Dynamic pupil size and gaze plot
  5. Microsaccade position and velocity plot
  6. Microsaccade main sequence plot

Installation

PyTrack is built for Python3 because support for the Python2 is going to be stopped at the end of 2019. In order to install PyTrack please use any of the following:

python3 -m pip install PyTrack-NTU
pip install PyTrack-NTU
pip3 install PyTrack-NTU

Please make sure that pip is for Python3 and not Python2. Python3 can be found here or Anaconda Python3 can be found here.

NOTE: Python3 can be installed alongside Python2

Sample Data

In order to test the toolkit some sample data in SMI, EyeLink and Tobii formats can be found here. The .txt file in the folder describes the data found. The SMI and Tobii files have been taken from here.

Using PyTrack

Setup

Before running the framework, lets setup the folder so PyTrack can read and save all the generated figures in one central location and things are organised.

Create a directory structure like the one shown below. It is essential for the listed directories to be present for the proper functioning of PyTrack.

NOTE: The sample data has a folder called NTU_Experiment which is already organised in the following manner. It can be used as reference.

[Experiment-Name]
|
└── Data/
│   │   subject_001.[asc/txt/tsv/...]
│   │   subject_002.[asc/txt/tsv/...]
|   |__ ......
│
└── Stimulus/
│   │   stim_1.[jpg/jpeg]
│   │   stim_2.[jpg/jpeg]
|   |__ ......
|
└── [Experiment-Name].json

[Experiment-Name] stands for the name of your experiment. The rest of the steps will use NTU_Experiment as the [Experiment-Name] folder.

Now, follow these steps:

  1. Place the data of all your subjects in the Data folder under the main NTU_Experiment folder. Make sure the name of each of the data files is the name of the subjects/paticipants. Replace all spaces( ) with underscores (_).

    eg. waffle_buttersnaps.asc or subject_001.asc

  2. For proper visualization of gaze data, its best if you include the stimuli presented during your experiment inside the Stimuli folder. Make sure the images have either jpg, jpeg or png extensions and the names match the names of the stimuli as present in your recorded data.

    eg. stim_1.jpg or random_picture.png

  3. The last and final step to setup the experiment directory is to include the experiment description json file. This file should contain the essential details of your experiment. It contains specifications regarding your experiment such as the stimuli you wish to analyse or the participants/subjects you wish to include. Mentioned below is the json file structure. The content below can be copied and pasted in a file called NTU_Experiment.json

    • Experiment_name” should be the same name as the json file without the extension and “Path” should be the absolute path to your experiment directory without the final “/” at the end.
    • The subjects should be added under the “Subjects” field. You may specify one or more groups of division for your subjects (recommended for between group statistical analysis). There must be atleast 1 group.
    • The stimuli names should be added under the “Stimuli” field and again you may specify one or more types (recommended for between/within stimulus type statistical analysis). There must be atleast 1 type.
    • The “Control_Questions” field is optional. In case you have some stimuli that should be used to standardise/normalise features extracted from all stimuli, specify the names here. These stimuli must be present under the “Stimuli” field under one of the types.
    • The field marked “Columns_of_interest” should not be altered.
    • Under “Analysis_Params”, just change the values of “Sampling_Freq”, “Display_height” and “Display_width” to match the values of your experiment.

    Note: If you wish to analyse only a subset of your stimuli or subjects, specify only the ones of interest in the json file. The analysis and visualization will be done only for the ones mentioned in the json file.

NOTE: A sample json file is present in the NTU_Experiment folder in the sample data. You can just edit it to make your work simpler.

{
   "Experiment_name":"NTU_Experiment",
   "Path":"abcd/efgh/NTU_Experiment",
   "Subjects":{
      "group1":[
         "Subject_01",
         "Subject_02"
      ],
      "group2":[
         "Subject_03",
         "Subject_04"
      ]
   },
   "Stimuli":{
      "Type_1":[
         "Stim_1",
         "Stim_2"
      ],
      "Type_2":[
         "Stim_3",
         "Stim_4"
      ],
   },
   "Control_Questions":[
         "Stim_1"
    ],
   "Columns_of_interest":{
      "EyeTracker":[
         "GazeLeftx",
         "GazeLefty",
         "GazeRightx",
         "GazeRighty",
         "PupilLeft",
         "PupilRight",
         "FixationSeq",
         "GazeAOI"
      ],
      "Extra":[
         "EventSource"
      ]
   },
   "Analysis_Params":{
      "EyeTracker":{
        "Sampling_Freq": 1000,
        "Display_width": 1920,
        "Display_height": 1280
      }
   }
}

Running PyTrack

NOTE: All sample segments shown below are for the NTU_Experiment folder in the sample data.

  1. In order to use the features, the first step is to convert the raw data into a readable format. In order to do so, the following code segment can be used:

    from PyTrack.formatBridge import generateCompatibleFormat
    
    generateCompatibleFormat(exp_path="complete/path/to/NTU_Experiment",
                            device="eyelink",
                            stim_list_mode='NA',
                            start='start_trial',
                            stop='stop_trial',
                            eye='B')
    

    To get a detailed understanding of the parameters of generateCompatibleFormats and modify it to your needs see the documentation here.

  2. The second step is to create an object of the Experiment class.

    from PyTrack.Experiment import Experiment
    
    # Creating an object of the Experiment class
    exp = Experiment(json_file="complete/path/to/NTU_Experiment/NTU_Experiment.json")
    
  3. Now you can run the feature extraction and statistical tests

    # Instantiate the meta_matrix_dict of an Experiment to find and extract all features from the raw data
    exp.metaMatrixInitialisation()
    
    # Calling the function for the statistical analysis of the data
    exp.analyse(parameter_list={"all"},
                between_factor_list=["Subject_type"],
                within_factor_list=["Stimuli_type"],
                statistical_test="anova",
                file_creation=True)
    
    # Does not run any statistical test. Just saves all the data as csv files.
    exp.analyse(parameter_list={"all"},
                statistical_test="None",
                file_creation=True)
    

    To get a detailed understanding of the parameters of the analyse function: here

    To get a detailed understanding of the parameters of the metaMatrixInitialisation function: here

  4. For visualization

    # This function call will open up a GUI which you can use to navigate the entire visualization process
    exp.visualizeData()
    

Advanced Functionality

Statistical Tests

The Experiment class contains a function called analyse() which is used to perform statistical analysis (eg: ANOVA or T test), by default there is only 1 between group factor (“Subject_type”) and 1 within group factor (“Stimuli_type”) that is considered. If additional factors need to be considered they need to added to the json file.

  • For example if Gender is to be considered as an additional between group factor then in the json file, under “Subjects”, for each subject, a corresponding dicitionary must be created where you mention the factor name and the corresponding value. Please also note that the square brackets (‘[‘, ‘]’) after group type need to be changed to curly brackets (‘{‘, ‘}’).
  • Similarly for Stimuli, for example, if you are showing Words and Pictures to elicit different responses from a user and you additonally have 2 different brightness levels (“High” and “Low”) then mention Brightness as an additional within group factor.
{
   "Subjects":{
      "group1":{
         "Subject_01": {"Gender": "M"},
         "Subject_02": {"Gender": "F"}
      },
      "group2":{
         "Subject_03": {"Gender": "F"},
         "Subject_04": {"Gender": "M"}
      }
   },
   "Stimuli":{
      "Type_1":{
         "Stim_1": {"Brightness": "High"},
         "Stim_2": {"Brightness": "Low"}
      },
      "Type_2":{
         "Stim_3": {"Brightness": "Low"},
         "Stim_4": {"Brightness": "High"}
      },
   },
}

Sample code segment to use the advanced statistical test:

from PyTrack.Experiment import Experiment

exp = Experiment(json_file="abcd/efgh/NTU_Experiment/NTU_Experiment.json")

exp.metaMatrixInitialisation()

exp.analyse(parameter_list={"all"},
            between_factor_list=["Subject_type", "Gender"],
            within_factor_list=["Stimuli_type", "Brightness"],
            statistical_test="anova",
            file_creation=True)

Accessing extracted features as a dictionary

In case you wish to get the extracted features for a particilar Subject on a particular Stimulus:

from PyTrack.Experiment import Experiment

exp = Experiment(json_file="complete/path/to/NTU_Experiment/NTU_Experiment.json")

subject_name = "sub_333" #specify your own subject's name (must be in json file)
stimulus_name = "Alpha1" #specify your own stimulus name (must be in json file)

# Access metadata dictionary for particular subject and stimulus
exp.metaMatrixInitialisation()
single_meta = exp.getMetaData(sub=subject_name,
                              stim=stimulus_name)

# Access metadata dictionary for particular subject and averaged for stimulus types
exp.metaMatrixInitialisation(average_flag=True)
agg_type_meta = exp.getMetaData(sub=subject_name,
                                 stim=None)

Using PyTrack in Stand-alone mode

The stand-alone design requires only interaction with tyhe Stimulus class. This is recommended if you wish to extract features or visualize data for only 1 subject on a particular stimulus.

from PyTrack.Stimulus import Stimulus
from PyTrack.formatBridge import generateCompatibleFormat
import pandas as pd
import numpy as np


# function to convert data to generate csv file for data file recorded using EyeLink on both eyes and the stimulus name specified in the message section
generateCompatibleFormat(exp_path="/path/to/smi_eyetracker_freeviewing.txt",
                        device="smi",
                        stim_list_mode='NA',
                        start='12',
                        stop='99')

df = pd.read_csv("/path/to/smi_eyetracker_freeviewing.csv")

# Dictionary containing details of recording. Please change the values according to your experiment. If no AOI is desired, set aoi value to [0, 0, Display_width, Display_height]
sensor_dict = {
                  "EyeTracker":
                  {
                     "Sampling_Freq": 1000,
                     "Display_width": 1280,
                     "Display_height": 1024,
                     "aoi": [390, 497, 759, 732]
                  }
               }

# Creating Stimulus object. See the documentation for advanced parameters.
stim = Stimulus(path="path/to/experiment/folder",
               data=df,
               sensor_names=sensor_dict)

# Some functionality usage. See documentation of Stimulus class for advanced use.
stim.findEyeMetaData()
features = stim.sensors["EyeTracker"].metadata  # Getting dictioary of found metadata/features

# Visualization of plots
stim.gazePlot(save_fig=True)
stim.gazeHeatMap(save_fig=True)
stim.visualize()

# Extracting features
MS, ms_count, ms_duration = stim.findMicrosaccades(plot_ms=True)

See the stimulus class for more details on the functions: here

Authors

See also the list of contributors who participated in this project.

Acknowledgments

  • The formatsBridge module was adapted from the work done by Edwin Dalmaijer in PyGazeAnalyser.
  • This work was done under the supervision of Dr. Chng Eng Siong - School of Computer Science and Engineering NTU and in collaboration with Dr. Xu Hong - School of Humanitites and Social Sciences NTU.
  • We extend our thanks to the Department of Computer Science and Engineering Manipal Isntitute of Technology[link] and the Department of Computer Science and Information Systems BITS Pilani, Hyderabad Campus [link].