ALItrack was developed in Dr.Jennifer Ryan's lab under supervision of Dr.Zhongxu liu

Overview

  To create a Matlab package that is able to create features for any given EDF file in a user friendly manner. 
  Furthermore, it aims to provide certain utility-like feature detection functionalities for eye_movement 
  data processed elsewhere.  The package builds on top of (inherits) iTrack by Jason Hubbard, as modified by
  Dr.Liu. It aims to build on top of iTrack as well as improve certain existing functionalities.
  iTrack itself is no longer being developed. 

Available features :

• Reading EDF data into Matlab
• Getting all listed features for all or any selected trials in any given edf file in a few minutes (as of last test, it took 261 seconds to prepare all features for an experiment with 864 trials)
• Total number of trials
• Raw data
• Reading behavioural data
• Basic trial features such as polar data, adaptable onset temporal values, blink removal
• Basic Saccade features found in dataviewer (Eg: duration, amplitude, max/min of sensible feature, location)
• Second order saccade features not found in dataviewer ( Eg: Zcsore, standard deviations, ...)
• Basic fixation features found in dataviewer (see saccades)
• Second order Fixation features not found in dataviwer ( see saccades)
• Trial Specific spatial ROI's (user defined masks, grids, rectangular, circular, elliptic masks)
• Trial specific temporal ROI's ( Event based or temporal based trial onset and offset)
• ROI (temporal or spatial ) specific feature detection (EG: number of saccades in a specified period at a specified location)
• Eyelink like saccade detection (EG: detecting saccades using the eyelinkmethod after cleaning the data)
• Entropy 1,2 measures
• Recurrence measures

Next steps:

In development features: these features have either not been tested yet or have not been fully implemented. However, they are expected to be fully functional in the immediate future and can be used as-is for the sample data used in development.

Machine learning.

Getting Started

Download the repo and add it to your matlab path. Check Demo folder for sample usage.

Prerequisites

Matlab https://github.com/ATajadod94/iTrack

Give examples

Installing

Check Demo folder

Running the tests

Run the participanttests script in src usiing Matlab's (Runtestbutton)

Deployment

using Participant Class

myparticipant = participant(2003, '/Users/ryanlab/Desktop/AliT/Data/ALItracker_Data/2003');
myparticipant.setdata()
myparticipant.setaudio()\

myparticipant.word_saccade_correlator(3,'duration', 'before' , 100 , 'after', 200)

myparticipant.get_trial_features(1:12)
trial = myparticipant.gettrial(1);

using Trial class directly

trial = gettrial(myparticipant,1);
trial.number_of_fixation trial.number_of_saccade trial.duration_of_fixation trial.duration_of_saccade trial.location_of_fixation trial.location_of_saccade_endpoints trial.amplitude_of_saccade trial.deviation_of_duration_of_fixation trial.deviation_of_duration_of_saccade trial.regionsofinterest

Naming Conventions

1. Participant level properties are upper case

Contributing

1. Fork it!
2. Create your feature branch: git checkout -b my-new-feature
3. Commit your changes: git commit -am 'Add some feature'
4. Push to the branch: git push origin my-new-feature
5. Submit a pull request :D

Authors

• Alireza Tajadod - Initial work - Repo

Under direct supervision of Dr.Zhongxu liu

Acknowledgments

• Hat tip to anyone who's code was used