A Novel Evaluation of Two Related, and Two Independent Algorithms for Eye Movement Classification during Reading
Date
2018-01
Authors
Friedman, Lee
Rigas, Ioannis
Abdulin, Evgeny
Komogortsev, Oleg
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
This repository contains classified eye-movement data from the submitted paper, "Novel Evaluation of Two Related, and Two Independent Algorithms for Eye Movement Classification during Reading" Lee Friedman, Ioannis Rigas, Evgeny Abdulin and Oleg V. Komogortsev The Department of Computer Science, Texas State University, San Marcos, Texas.
As of 2/19/2018, the third revision is under review at Behavior Research Methods. There are 4 directories included, each with exactly 20 files. These are the 20 files that were evaluated with 4 scoring methods. ONH – These data were scored by the method described in [1]. MNH – These data were scored by the method presented in the manuscript. IRF – These data were scored by the method presented in [2]. EDF – These data were scored by the EyeLink Parser.
File naming convention:
Take, for example, this name: “S_1051_S1_TEX_Class_EyeLink.csv”. This is data from Subject number 1051, recording session 1, the TEX (poetry reading) task and it contains classification data scored by the EyeLink Parser.
“S_1066_S2_TEX_Class_IRF.csv” is data from Subject number 1066, recording session 2, the TEX (poetry reading) task and it contains classification data scored by [2]. “S_1334_S2_TEX_Class_ONH.csv” is data from Subject number 1334, recording session 2, the TEX (poetry reading) task and it contains classification data scored by [1]. Files like “S_1282_S2_TEX_Class_MNH.csv” were scored by the method described in the manuscript.
The first column of every dataset is a msec timestamp. Only the first 26,000 msec of each file were processed for the manuscript.
The second column of every dataset is the horizontal (X) eye position signal in degrees of visual angle. In the case of the ONH and the MNH methods, these position signals were smoothed. See manuscript for details.
The third column of every dataset is the vertical (Y) eye position signal in degrees of visual angle. In the case of the ONH and the MNH methods, these position signals were smoothed. See manuscript for details.
The fourth column of every dataset is the radial velocity of the eye movement signals. Please see manuscript for details of this calculation for every dataset.
The fifth column of each dataset is a classification code, where 1 = fixation, 2 = saccade, 3 = post-saccadic oscillation, 4 = noise or artifact, and 5 is unclassified. Note that the IRF coded data did not use an “unclassified” category.
References:
[1] M. Nystrom and K. Holmqvist, "An adaptive algorithm for fixation, saccade, and glissade detection in eyetracking data," Behav Res Methods, vol. 42, no. 1, pp. 188-204, Feb 2010.
[2] R. Zemblys, D. C. Niehorster, O. Komogortsev, and K. Holmqvist, "Using machine learning to detect events in eye-tracking data," Behav Res Methods, Feb 23 2017.
Description
This is eye movement data for the paper 'A Novel Evaluation of Two Related, and Two Independent Algorithms for Eye Movement Classification during Reading", by Friedman, Rigas, Abdulin and Komogortsev, currently (2/19/2018) submitted for review at Behavior Research Methods.
Keywords
classified eye-movement files, Computer Science