EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia

The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography (EEG) experiments on children listening to amplit...

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Vydáno v:International journal of neural systems Ročník 30; číslo 7; s. 2050037
Hlavní autoři: Martinez-Murcia, Francisco J, Ortiz, Andres, Gorriz, Juan Manuel, Ramirez, Javier, Lopez-Abarejo, Pedro Javier, Lopez-Zamora, Miguel, Luque, Juan Luis
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore 01.07.2020
Témata:
Cerebral Cortex - physiopathology
Child
Connectome - methods
Dyslexia - diagnosis
Dyslexia - physiopathology
Electroencephalography - methods
Humans
Language Tests
Neural Networks, Computer
Psycholinguistics
Speech Perception - physiology
Unsupervised Machine Learning
autoencoder
connectivity
EEG
Periodogram
dyslexia
ISSN:1793-6462, 1793-6462
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Shrnutí:The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography (EEG) experiments on children listening to amplitude modulated (AM) noise with slow-rythmic prosodic (0.5-1[Formula: see text]Hz), syllabic (4-8[Formula: see text]Hz) or the phoneme (12-40[Formula: see text]Hz) rates, aimed at detecting differences in perception of oscillatory sampling that could be associated with dyslexia. The purpose of this work is to check whether these differences exist and how they are related to children's performance in different language and cognitive tasks commonly used to detect dyslexia. To this purpose, temporal and spectral inter-channel EEG connectivity was estimated, and a denoising autoencoder (DAE) was trained to learn a low-dimensional representation of the connectivity matrices. This representation was studied via correlation and classification analysis, which revealed ability in detecting dyslexic subjects with an accuracy higher than 0.8, and balanced accuracy around 0.7. Some features of the DAE representation were significantly correlated ([Formula: see text]) with children's performance in language and cognitive tasks of the phonological hypothesis category such as phonological awareness and rapid symbolic naming, as well as reading efficiency and reading comprehension. Finally, a deeper analysis of the adjacency matrix revealed a reduced bilateral connection between electrodes of the temporal lobe (roughly the primary auditory cortex) in DD subjects, as well as an increased connectivity of the F7 electrode, placed roughly on Broca's area. These results pave the way for a complementary assessment of dyslexia using more objective methodologies such as EEG.
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ISSN:1793-6462
1793-6462
DOI:10.1142/S0129065720500379