Showing 7 of total 7 results (show query)
tombeesley
eyetools:Analyse Eye Data
Enables the automation of actions across the pipeline, including initial steps of transforming binocular data and gap repair to event-based processing such as fixations, saccades, and entry/duration in Areas of Interest (AOIs). It also offers visualisation of eye movement and AOI entries. These tools take relatively raw (trial, time, x, and y form) data and can be used to return fixations, saccades, and AOI entries and time spent in AOIs. As the tools rely on this basic data format, the functions can work with data from any eye tracking device. Implements fixation and saccade detection using methods proposed by Salvucci and Goldberg (2000) <doi:10.1145/355017.355028>.
Maintained by Tom Beesley. Last updated 3 months ago.
areas-of-interestattention-visualizationcognitive-sciencedwell-time-algorithmeye-trackereye-trackingeyetrackingggplot2psychologypsychology-experimentssaccadestobiitobii-eye-trackervisualization
14.8 match 4 stars 5.45 score 8 scripts
alexander-pastukhov
saccadr:Extract Saccades via an Ensemble of Methods Approach
A modular and extendable approach to extract (micro)saccades from gaze samples via an ensemble of methods. Although there is an agreement about a general definition of a saccade, the more specific details are harder to agree upon. Therefore, there are numerous algorithms that extract saccades based on various heuristics, which differ in the assumptions about velocity, acceleration, etc. The package uses three methods (Engbert and Kliegl (2003) <doi:10.1016/S0042-6989(03)00084-1>, Otero-Millan et al. (2014)<doi:10.1167/14.2.18>, and Nyström and Holmqvist (2010) <doi:10.3758/BRM.42.1.188>) to label individual samples and then applies a majority vote approach to identify saccades. The package includes three methods but can be extended via custom functions. It also uses a modular approach to compute velocity and acceleration from noisy samples. Finally, you can obtain methods votes per gaze sample instead of saccades.
Maintained by Alexander Pastukhov. Last updated 2 years ago.
10.3 match 4 stars 4.90 score 8 scripts
a-hurst
eyelinker:Import ASC Files from EyeLink Eye Trackers
Imports plain-text ASC data files from EyeLink eye trackers into (relatively) tidy data frames for analysis and visualization.
Maintained by Austin Hurst. Last updated 4 years ago.
6.0 match 7 stars 5.45 score 20 scriptsalexander-pastukhov
eyelinkReader:Import Gaze Data for EyeLink Eye Tracker
Import gaze data from edf files generated by the SR Research <https://www.sr-research.com/> EyeLink eye tracker. Gaze data, both recorded events and samples, is imported per trial. The package allows to extract events of interest, such as saccades, blinks, etc. as well as recorded variables and custom events (areas of interest, triggers) into separate tables. The package requires EDF API library that can be obtained at <https://www.sr-research.com/support/>.
Maintained by Alexander Pastukhov. Last updated 3 months ago.
edfeye-trackingeyelinksr-researchcpp
4.2 match 13 stars 6.52 score 34 scripts
schw4b
emov:Eye Movement Analysis Package for Fixation and Saccade Detection
Fixation and saccade detection in eye movement recordings. This package implements a dispersion-based algorithm (I-DT) proposed by Salvucci & Goldberg (2000) which detects fixation duration and position.
Maintained by Simon Schwab. Last updated 9 years ago.
3.4 match 14 stars 3.85 score 4 scripts
drjohanlk
kollaR:Filtering, Visualization and Analysis of Eye Tracking Data
Functions for analysing eye tracking data, including event detection (I-VT, I-DT and two means clustering), visualizations and area of interest (AOI) based analyses. See separate documentation for each function. The principles underlying I-VT and I-DT filters are described in Salvucci & Goldberg (2000,\doi{10.1145/355017.355028}). Two-means clustering is described in Hessels et al. (2017, \doi{10.3758/s13428-016-0822-1}).
Maintained by Johan Lundin Kleberg. Last updated 24 days ago.
9.9 match 1.30 score
cran
eyeTrackR:Organising and Analysing Eye-Tracking Data
A set of functions for organising and analysing datasets from experiments run using 'Eyelink' eye-trackers. Organising functions help to clean and prepare eye-tracking datasets for analysis, and mark up key events such as display changes and responses made by participants. Analysing functions help to create means for a wide range of standard measures (such as 'mean fixation durations'), which can then be fed into the appropriate statistical analyses and graphing packages as necessary.
Maintained by Hayward Godwin. Last updated 5 years ago.
2.0 match 1.00 score