Temporal Phase Synchrony Disruption in Dyslexia: Anomaly Patterns in Auditory Processing
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AuthorFormoso, Marco A.; Ortiz, Andrés; Martínez Murcia, Francisco Jesús; Aquino Brítez, Diego; Escobar Pérez, Juan José
EEGHilber transformDyslexiaNeural adaptation
Formoso, M.A... [et al.] (2022). Temporal Phase Synchrony Disruption in Dyslexia: Anomaly Patterns in Auditory Processing. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. [https://doi.org/10.1007/978-3-031-06242-1_2]
SponsorshipSpanish Government PGC2018-098813-BC32 PGC2018-098813-B-C31; Junta de Andalucia UMA20-FEDERJA-086 P18-RT-1624; European Commission; BioSiP research group TIC-251; MCIN/AEI by "ESF Investing in your future" PRE2019-087350 MICINN "Juan de la Cierva -Incorporacion" Fellowship; Leeduca research group; Junta de Andalucia Spanish Government
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.