Speed and accuracy of dyslexic versus typical word recognition: an eye-movement investigation

Developmental dyslexia is often characterized by a dual deficit in both word recognition accuracy and general processing speed. While previous research into dyslexic word recognition may have suffered from speed-accuracy trade-off, the present study employed a novel eye tracking task that is less prone to such confounds. Participants (10 dyslexics and 12 controls) were asked to look at real word stimuli, and to ignore simultaneously presented non-word stimuli, while their eye-movements were recorded. Improvements in word recognition accuracy over time were modeled in terms of a continuous non-linear function. The words’ rhyme consistency and the non-words’ lexicality (unpronounceable, pronounceable, pseudohomophone) were manipulated within-subjects. Speed related measures derived from the model fits confirmed generally slower processing in dyslexics, and showed a rhyme consistency effect in both dyslexics and controls. In terms of overall error rate, dyslexics (but not controls) performed less accurately on rhyme-inconsistent words, suggesting a representational deficit for such words in dyslexics. Interestingly, neither group showed a pseudohomophone effect in speed or accuracy, which might call the task-independent pervasiveness of this effect into question. The present results illustrate the importance of distinguishing between speed- vs. accuracy related effects for our understanding of dyslexic word recognition

Toward the language oscillogenome

Language has been argued to arise, both ontogenetically and phylogenetically, from specific patterns of brain wiring. We argue that it can further be shown that core features of language processing emerge from particular phasal and cross-frequency coupling properties of neural oscillations; what has been referred to as the language 'oscillome.' It is expected that basic aspects of the language oscillome result from genetic guidance, what we will here call the language 'oscillogenome,' for which we will put forward a list of candidate genes. We have considered genes for altered brain rhythmicity in conditions involving language deficits: autism spectrum disorders, schizophrenia, specific language impairment and dyslexia. These selected genes map on to aspects of brain function, particularly on to neurotransmitter function. We stress that caution should be adopted in the construction of any oscillogenome, given the range of potential roles particular localized frequency bands have in cognition. Our aim is to propose a set of genome-to-language linking hypotheses that, given testing, would grant explanatory power to brain rhythms with respect to language processing and evolution.Economic and Social Research Council scholarship 1474910Ministerio de Economía y Competitividad (España) FFI2016-78034-C2-2-

Relevant test set using feature selection algorithm for early detection of dyslexia

The objective of feature selection is to find the most relevant features for classification. Thus, the dimensionality of the information will be reduced and may improve classification’s accuracy. This paper proposed a minimum set of relevant questions that can be used for early detection of dyslexia. In this research, we investigated and proposed a feature selection algorithm that is correlation based feature selection (CFS) and generate classification modelsbased on five different classifiers namely Bayes Net, Simple Logistic and Decision Table. This paper used dataset collected from a computer based screening test developed consists of 50 questions. The result shows that the new set of question suggested from the feature selection algorithm was significantly achieved 100% accuracy of classification and less time was taken for conducting screening test among students.Keywords: feature selection; dyslexic children; computer based screening test

Kiddo disleksia mobile application: a dyslexia screen tool in Malay language

Identification of potential dyslexic among Malaysia student is crucial to ensure earlier intervention is given. Current practice in the Malaysian school system, screening test has been given based on teacher’s observation and intervention. The low number of student enrol in special education indicate this task is difficult. It is due to untrained and lack of awareness regarding dyslexia among Malaysia teacher. Therefore, the easy, simple and accurate screen test tool are required. Kiddo Disleksia, a dyslexia screening tool based on mobile application has been designed and developed. The application exploits the uses of text, video, audio and graphic in multimedia to screen student with dyslexia based on visual and auditory. Kiddo Disleksia is developed by using Malay language as it’s specifically develops for student in earlier learning to recognize and distinguish the alphabet. Besides, this application able to use as learning tool for dyslexia student to identify alphabet letter and phonic (alphabet sound). The mobile gamed based approach is used to screen the student with fun and innovative way. A usability study has been conducted towards 20 dyslexia students and result shows 85% of respondent feel the application able to help them learn easier. They agree that Kiddo Disleksia able to screen the dyslexia student with fun and easy way

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

The development of an ontology model for early identification of children with specific learning disabilities

Ontology-based knowledge representation is explored in special education environment as not much attention has been given to the area of specific learning disabilities such as dyslexia, dysgraphia and dyscalculia. Therefore, this paper aims to capture the knowledge in special education domain, represent the knowledge using ontology-based approach and make it efficient for early identification of children who might have specific learning disabilities. In this paper, the step-by-step development process of the ontology is presented by following the five phases of ontological engineering approach, which consists of specification, conceptualization, formalization, implementation, and maintenance. The details of the ontological model’s content and structure is built and the applicability of the ontology for early identification and recommendation is demonstrated