PyTrack

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:

Fixation plot
Individual subject gaze heat map
Aggregate subject gaze heat map
Dynamic pupil size and gaze plot
Microsaccade position and velocity plot
Microsaccade main sequence plot

PyTrack
Table of Contents
Documentation
Installation
Sample Data
Using PyTrack
Advanced Functionality
- Statistical Tests
- Accessing extracted features as a dictionary
Authors
License
Acknowledgments

Documentation

The detailed documentation for the methods and classes of PyTrack can be found here

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

The quickest and most concise way to get started is to go through the Python Notebooks:

getting_started_ExpMode.ipynb
getting_started_SAMode.ipynb
getting_started_OwnData.ipynb: If you have data other than Tobii, SMI or EyeLink.

For some advanced use cases read on, and for viewing the detailed documentation of the different modules see here.

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 particular 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)

Authors

Upamanyu Ghose (github | email)
Arvind A S (github | email)

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

License

This project is licensed under the GPL3 License - see the LICENSE.txt file for details

Acknowledgments

We would like to thank Dr. Dominique Makowski for helping us develop this toolkit.
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]. -->

titoghose/PyTrack