| Literature DB >> 26358034 |
Javier Andreu-Perez1, Celine Solnais2, Kumuthan Sriskandarajah3.
Abstract
Recent advances in the reliability of the eye-tracking methodology as well as the increasing availability of affordable non-intrusive technology have opened the door to new research opportunities in a variety of areas and applications. This has raised increasing interest within disciplines such as medicine, business and education for analysing human perceptual and psychological processes based on eye-tracking data. However, most of the currently available software requires programming skills and focuses on the analysis of a limited set of eye-movement measures (e.g., saccades and fixations), thus excluding other measures of interest to the classification of a determined state or condition. This paper describes 'EALab', a MATLAB toolbox aimed at easing the extraction, multivariate analysis and classification stages of eye-activity data collected from commercial and independent eye trackers. The processing implemented in this toolbox enables to evaluate variables extracted from a wide range of measures including saccades, fixations, blinks, pupil diameter and glissades. Using EALab does not require any programming and the analysis can be performed through a user-friendly graphical user interface (GUI) consisting of three processing modules: 1) eye-activity measure extraction interface, 2) variable selection and analysis interface, and 3) classification interface.Entities:
Keywords: Classification; Computer software; Eye tracking; Machine learning; Multivariate analysis; Neuroinformatics
Mesh:
Year: 2016 PMID: 26358034 DOI: 10.1007/s12021-015-9275-4
Source DB: PubMed Journal: Neuroinformatics ISSN: 1539-2791