Yet another implementation of some algo for smooth pursuit (following) eye movements, trying to avoid duplicate work already present in publications.

Replication package for our eye-tracking study on programmers' linearity of reading order

Face Detection for the identification of human faces in digital images or video. It can be regarded as a specific case of object-class detection, where the task is to find the locations and sizes of all objects in an image that belongs to a given class.

Real-time asymmetric scotoma simulation built for IMT Atlantique using 3D shutter glasses and Pupil Labs core eyetracker

Scripts for calculating statistical data for word-aoi-groups

Takes a list of hdf5 files, splits them, cleans them up and down-samples them as needed

Some useful functions for mobile eyetracking data analysis

A gaze-controlled mouse cursor using Pupil Capture by Pupil Labs

Dashboard made for Sensing Streetscapes, containing eyetracking data measured in Amsterdam.

Project IRIS for AT-Hackathon 2018

📃 [Finished] Tools for obtain, clean and filtrate data used in "González-Ibáñez, R., Proaño, V., Fuenzalida, G. and Martinez, G. (2017). Effects of a Visual Representation of Search Engine Results on Performance, User Experience, and Effort".

Eye Control Mouse

Repository for the analysis of eyetracking data, collected by the Institute of Mental Health (IMHR) at the University of Texas at Austin.

The script gets a list of words from an excel sheet and will upload them to the following website: http://lsa.colorado.edu/cgi-bin/LSA-matrix.html, "This interface allows you to compare the similarity of multiple texts or terms within a particular LSA space. Each text is compared to all other texts." The results for each subject will be saved in…

Generates metrics file from Tobii .tsv raw data output.

SR Research bindings for Eyelink and Python. Optimized for Psychopy.

The script gets at CSV of stimuli from and will create a CNT region file. If each region has only one corresponding column in CSV file, it uses a simple list from parameter file. However, if there are multiple columns for one or more regions, The script will prompt questions which the user need to answer.

This code allows you to calculate 5 of the most popular saliency metrics AUC-Judd, KLdiv, NSS, CC and Sim to determine how well a deep learning model predicts visual saliency

Eye and face tracking using React, Python, and WebRTC.