Heuristic Evaluation Of i-Dyslex Tool for Dyslexia Screening

Early detection for dyslexia is crucial in order for children to receive early as well as proper treatment. There are various studies that have focused on early detection of dyslexia, however the results remain limited. Therefore, an easy and user-friendly dyslexia screening tool called i-Dyslex was developed. In order to make sure the tool is free from design and interface problems, heuristic evaluation has been carried out. This paper discusses the heuristic evaluation of i-Dyslex tool for dyslexia screening among expert evaluators. This study adopted ten Usability Heuristics to be included in the questionnaire. Overall result derived from the evaluation is above average mean score, which are neutral (3.00) in one domain. Several comments and feedback from the experts. Both the experts’ evaluation and the feedback were essentials for further improvement of the i-Dyslex tool to ensure meets the user requirement and expectation

Estado del arte sobre tecnologías para el apoyo al diagnostico y manejo de trastornos específicos del aprendizaje, disponible en literatura cientifica y entornos comerciales

Los Trastornos Específicos del Aprendizaje (TEA), se definen como “una dificultad en el aprendizaje y en la utilización de las aptitudes académicas” (American Psychiatric Association, 2014). En el DSM-V1, en su sección de trastornos del desarrollo neurológico, se indica que estos se evidencian por, al menos, un síntoma como: lectura imprecisa, lenta y con esfuerzo de palabras, dificultades para comprender el significado de lo que se lee o para escribir correctamente las palabras; poco dominio del sentido numérico o el cálculo, entre otras, durante un periodo de más de seis meses. Los Trastornos Específicos del Aprendizaje (TEA) se dividen en tres grupos: dificultades en la lectura (dislexia), dificultades en la escritura (disgrafía) y dificultades matemáticas (discalculia). En los últimos años, se han presentado tanto en la literatura técnica y científica, como en el ámbito comercial, tecnologías que buscan mejorar el manejo (diagnóstico y tratamiento) de los TEAs; algunas de ellas orientadas al uso personal o por parte de los padres y otros específicos para los especialistas. Este trabajo de grado establece tendencias y posibles desarrollos tecnológicos para el apoyo al manejo de TEAs, a partir del estado del arte abordado desde dos componentes: por un lado, una revisión de literatura científica, de trabajos realizados en ámbitos académicos, y por el otro una revisión en ambientes comerciales (tiendas de aplicaciones móviles). Los resultados de este trabajo establecen líneas de acción para proyectos futuros del Semillero de Investigación en Tecnologías de Apoyo a la Inclusión.Ingeniero de Sistemaspregrad

Identification of EEG signal patterns between adults with dyslexia and normal controls

Electroencephalography (EEG) is one of the most useful techniques used to represent behaviours of the brain and helps explore valuable insights through the measurement of brain electrical activity. Hence, it plays a vital role in detecting neurological disorders such as epilepsy. Dyslexia is a hidden learning disability with a neurological origin affecting a significant amount of the world population. Studies show unique brain structures and behaviours in individuals with dyslexia and these variations have become more evident with the use of techniques such as EEG, Functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Positron Emission Tomography (PET). In this thesis, we are particularly interested in discussing the use of EEG to explore unique brain activities of adults with dyslexia. We attempt to discover unique EEG signal patterns between adults with dyslexia compared to normal controls while performing tasks that are more challenging for individuals with dyslexia. These tasks include real--‐word reading, nonsense--‐ word reading, passage reading, Rapid Automatized Naming (RAN), writing, typing, browsing the web, table interpretation and typing of random numbers. Each participant was instructed to perform these specific tasks while staying seated in front of a computer screen with the EEG headset setup on his or her head. The EEG signals captured during these tasks were examined using a machine learning classification framework, which includes signal preprocessing, frequency sub--‐band decomposition, feature extraction, classification and verification. Cubic Support Vector Machine (CSVM) classifiers were developed for separate brain regions of each specified task in order to determine the optimal brain regions and EEG sensors that produce the most unique EEG signal patterns between the two groups. The research revealed that adults with dyslexia generated unique EEG signal patterns compared to normal controls while performing the specific tasks. One of the vital discoveries of this research was that the nonsense--‐words classifiers produced higher Validation Accuracies (VA) compared to real--‐ words classifiers, confirming difficulties in phonological decoding skills seen in individuals with dyslexia are reflected in the EEG signal patterns, which was detected in the left parieto--‐occipital. It was also uncovered that all three reading tasks showed the same optimal brain region, and RAN which is known to have a relationship to reading also showed optimal performance in an overlapping region, demonstrating the likelihood that the association between reading and RAN reflects in the EEG signal patterns. Finally, we were able to discover brain regions that produced exclusive EEG signal patterns between the two groups that have not been reported before for writing, typing, web browsing, table interpretation and typing of random numbers