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Dyslexia

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Machine Learning Based Evaluation of Reading and Writing Difficulties.

Studies in health technology and informatics
The possibility of auto evaluation of reading and writing difficulties was investigated using non-parametric machine learning (ML) regression technique for URAWSS (Understanding Reading and Writing Skills of Schoolchildren) [1] test data of 168 child...

A Reading Model from the Perspective of Japanese Orthography: Connectionist Approach to the Hypothesis of Granularity and Transparency.

Journal of learning disabilities
This study presents a computer simulation model of reading in Japanese syllabic kana and morphographic kanji. The model was based on the simulation model developed by Harm and Seidenberg for reading in English. The purpose of building the current mod...

False memory for orthographically versus semantically similar words in adolescents with dyslexia: a fuzzy-trace theory perspective.

Annals of dyslexia
The presented research was conducted in order to investigate the connections between developmental dyslexia and the functioning of verbatim and gist memory traces-assumed in the fuzzy-trace theory. The participants were 71 high school students (33 wi...

Comparison of Extreme Learning Machine and K-Nearest Neighbour Performance in Classifying EEG Signal of Normal, Poor and Capable Dyslexic Children.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Dyslexia is a specific learning difficulty associated with brain capability in processing numbers and letters. Analysis of Electroencephalogram (EEG) could provide insight information on differences in brain processing. In this work, two machine lear...

Predicting risk of dyslexia with an online gamified test.

PloS one
Dyslexia is a specific learning disorder related to school failure. Detection is both crucial and challenging, especially in languages with transparent orthographies, such as Spanish. To make detecting dyslexia easier, we designed an online gamified ...

EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia.

International journal of neural systems
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...

Dyslexia Diagnosis by EEG Temporal and Spectral Descriptors: An Anomaly Detection Approach.

International journal of neural systems
Diagnosis of learning difficulties is a challenging goal. There are huge number of factors involved in the evaluation procedure that present high variance among the population with the same difficulty. Diagnosis is usually performed by scoring subjec...

Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism.

PloS one
Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heter...

Evaluating a new verbal working memory-balance program: a double-blind, randomized controlled trial study on Iranian children with dyslexia.

BMC neuroscience
BACKGROUND: It is important to improve verbal Working Memory (WM) in reading disability, as it is a key factor in learning. There are commercial verbal WM training programs, which have some short-term effects only on the verbal WM capacity, not readi...

Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis.

Sensors (Basel, Switzerland)
Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are not based on biological markers but on behavioural tests. Although dyslexia diagnosis has been addressed by these tests in clinical practice, it is difficult ...