@misc{10481/78112,
year = {2022},
month = {5},
url = {https://hdl.handle.net/10481/78112},
abstract = {The search for a dyslexia diagnosis based on exclusively
objective methods is currently a challenging task. Usually, this disorder
is analyzed by means of behavioral tests prone to errors due to their subjective
nature; e.g. the subject’s mood while doing the test can affect the
results. Understanding the brain processes involved is key to proportionate
a correct analysis and avoid these types of problems. It is in this task,
biomarkers like electroencephalograms can help to obtain an objective
measurement of the brain behavior that can be used to perform several
analyses and ultimately making a diagnosis, keeping the human interaction
at minimum. In this work, we used recorded electroencephalograms
of children with and without dyslexia while a sound stimulus is played.
We aim to detect whether there are significant differences in adaptation
when the same stimulus is applied at different times. Our results show
that following this process, a machine learning pipeline can be built with
AUC values up to 0.73.},
organization = {Spanish Government	PGC2018-098813-BC32	
PGC2018-098813-B-C31},
organization = {Junta de Andalucia	UMA20-FEDERJA-086	
P18-RT-1624},
organization = {European Commission},
organization = {BioSiP research group	TIC-251},
organization = {MCIN/AEI by "ESF Investing in your future"	PRE2019-087350	
MICINN "Juan de la Cierva -Incorporacion" Fellowship},
organization = {Leeduca research group},
organization = {Junta de Andalucia		
Spanish Government},
publisher = {Springer},
keywords = {EEG},
keywords = {Hilber transform},
keywords = {Dyslexia},
keywords = {Neural adaptation},
title = {Temporal Phase Synchrony Disruption in Dyslexia: Anomaly Patterns in Auditory Processing},
doi = {10.1007/978-3-031-06242-1_2},
author = {Formoso, Marco A. and Ortiz, Andrés and Martínez Murcia, Francisco Jesús and Aquino Brítez, Diego and Escobar Pérez, Juan José},
}