Eye tracking is an emerging technology with a wide spectrum of applications, including non-invasive neurocognitive diagnosis. An advantage of the use of eye trackers is in the improved assessment of indirect latent information about several aspects of the subjects’ neurophysiology. The path to uncover and take advantage of the meaning and implications of this information, however, is still in its very early stages. In this work, we apply ordinal patterns transition networks as a means to identify subjects with dyslexia in simple text reading experiments. We registered the tracking signal of the eye movements of several subjects (either normal or with diagnosed dyslexia). The evolution of the left-to-right movement over time was analyzed using ordinal patterns, and the transitions between patterns were analyzed and characterized. The relative frequencies of these transitions were used as feature descriptors, with which a classifier was trained. The classifier is able to distinguish typically developed vs dyslexic subjects with almost 100% accuracy only analyzing the relative frequency of the eye movement transition from one particular permutation pattern (plain left to right) to four other patterns including itself. This characterization helps understand differences in the underlying cognitive behavior of these two groups of subjects and also paves the way to several other potentially fruitful analyses applied to other neurocognitive conditions and tests.

1.
K.
Rayner
, “
Eye movements in reading: Models and data
,”
J. Eye Mov. Res.
2
,
1
10
(
2009
).
2.
R.
Engbert
,
A.
Nuthmann
,
E. M.
Richter
, and
R.
Kliegl
, “
Swift: A dynamical model of saccade generation during reading
,”
Psychol. Rev.
112
,
777
813
(
2005
).
3.
F. J.
Martos
and
J.
Vila
, “
Differences in eye movements control among dyslexic, retarded and normal readers in the Spanish population
,”
Read. Writ.
2
,
175
188
(
1990
).
4.
G. F.
Eden
,
J. F.
Stein
,
H. M.
Wood
, and
F. B.
Wood
, “
Differences in eye movements and reading problems in dyslexic and normal children
,”
Vision Res.
34
,
1345
1358
(
1994
).
5.
F.
Hutzler
and
H.
Wimmer
, “
Eye movements of dyslexic children when reading in a regular orthography
,”
Brain Lang.
89
,
235
242
(
2004
).
6.
J. I.
Specht
,
L.
Dimieri
,
E.
Urdapilleta
, and
G.
Gasaneo
, “
Minimal dynamical description of eye movements
,”
Eur. Phys. J. B
90
,
1
12
(
2017
).
7.
A. L.
Frapiccini
,
J. A.
Del Punta
,
K. V.
Rodriguez
,
L.
Dimieri
, and
G.
Gasaneo
, “
A simple model to analyse the activation force in eyeball movements
,”
Eur. Phys. J. B
93
,
1
10
(
2020
).
8.
S.
Bouzat
,
F. M. L.
Freije
, and
G.
Gasaneo
, “
Inertial movements of the iris as the origin of postsaccadic oscillations
,”
Phys. Rev. Lett.
120
,
178101
(
2018
).
9.
D. G.
Stephen
and
D.
Mirman
, “
Interactions dominate the dynamics of visual cognition
,”
Cognition
115
,
154
165
(
2010
).
10.
G.
Boccignone
,
Advanced Statistical Methods for Eye Movement Analysis and Modelling: A Gentle Introduction
(
Springer
,
Cham
,
2019
), Chap. 9, pp. 309–405.
11.
M. L.
Freije
,
A. A. J.
Gandica
,
J. I.
Specht
,
G.
Gasaneo
,
C. A.
Delrieux
,
B.
Stosic
,
T.
Stosic
, and
R.
de Luis-Garcia
, “Multifractal detrended fluctuation analysis of eye-tracking data,” in VipIMAGE 2017. ECCOMAS 2017, Lecture Notes in Computational Vision and Biomechanics, edited by J. Tavares and R. N. Jorge (International Publishing AG, 2017), pp. 476–484.
12.
F.
Avila
,
C.
Delrieux
, and
G.
Gasaneo
, “
Complexity analysis of eye-tracking trajectories
,”
Eur. Phys. J. B
92
,
1
7
(
2019
).
13.
F. R.
Iaconis
,
A. A. J.
Gandica
,
J. A. D.
Punta
,
C. A.
Delrieux
, and
G.
Gasaneo
, “
Information-theoretic characterization of eye-tracking signals with relation to cognitive tasks
,”
Chaos
31
,
033107
(
2021
).
14.
J. B.
Borges
,
H. S.
Ramos
,
R. A.
Mini
,
O. A.
Rosso
,
A. C.
Frery
, and
A. A.
Loureiro
, “
Learning and distinguishing time series dynamics via ordinal patterns transition graphs
,”
Appl. Math. Comput.
362
,
124554
(
2019
).
15.
M.
Nyström
and
K.
Holmkvist
, “
An adaptive algorithm for fixation, saccade, and glissade detection in eyetracking data
,”
Behav. Res. Methods
42
,
188
204
(
2010
).
16.
C.
Bandt
and
B.
Pompe
, “
Permutation entropy: A natural complexity measure for time series
,”
Phys. Rev. Lett.
88
,
174102
(
2002
).
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