Review of EEG-based pattern classification frameworks for dyslexia

Dyslexia is a disability that causes difficulties in reading and writing despite average intelligence. This hidden disability often goes undetected since dyslexics are normal and healthy in every other way. Electroencephalography (EEG) is one of the upcoming methods being researched for identifying unique brain activation patterns in dyslexics. The aims of this paper are to examine pros and cons of existing EEG-based pattern classification frameworks for dyslexia and recommend optimisations through the findings to assist future research. A critical analysis of the literature is conducted focusing on each framework’s (1) data collection, (2) pre-processing, (3) analysis and (4) classification methods. A wide range of inputs as well as classification approaches has been experimented for the improvement in EEG-based pattern classification frameworks. It was uncovered that incorporating reading- and writing-related tasks to experiments used in data collection may help improve these frameworks instead of using only simple tasks, and those unwanted artefacts caused by body movements in the EEG signals during reading and writing activities could be minimised using artefact subspace reconstruction. Further, support vector machine is identified as a promising classifier to be used in EEG-based pattern classification frameworks for dyslexia

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

EEG Signal Analysis of Writing and Typing between Adults with Dyslexia and Normal Controls

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, plays a vital role in detecting neurological conditions. In this paper, we identify some unique EEG patterns pertaining to dyslexia, which is a learning disability with a neurological origin. Although EEG signals hold important insights of brain behaviours, uncovering these insights are not always straightforward due to its complexity. We tackle this using machine learning and uncover unique EEG signals generated in adults with dyslexia during writing and typing as well as optimal EEG electrodes and brain regions for classification. This study revealed that the greater level of difficulties seen in individuals with dyslexia during writing and typing compared to normal controls are reflected in the brainwave signal patterns

Mobile Dyslexic Specialized Digital Game-based Learning Object for Learning Letters (DOLL)

Ability to read is a unanimous privilege we have as human. Therefore, research and development on Learning Object (LO) specially built for learning disabilities children is crucial. The issues for this project are the problem encounter by dyslexic students in output generation and information processes; and the factors that affect the effectiveness of mobile learning specially built for dyslexic. The focuses of this project are to propose guidelines and to facilitate student with visual dyslexia, or/and auditory dyslexia or/and dysgraphia; specifically in learning basic Malay language letters with interactive teaching method. Research has been made to identify the focused type of dyslexia, determine the needs of dyslexic; and the effect of graphic and animation on the efficiency of teaching technique. New multimedia-based learning object is being proposed to attract interest of dyslexic children to learn letters in fun approach and improve their recalling skills in recognizing name, shape and sound of letters. The main elements of the proposed learning object are animation, oral narration and digital gamebased. The theoretical framework proposed in this study is based on Principles of Teaching Program for Dyslexics, Stansfield Instructional Strategies, and Game-based Learning Object Framework. This project is carried out using ADDIE Instructional Design Model using Adobe Flash Professional cs5.5. A user experience testing is conducted with the dyslexic children. The result of the user experience testing showed more than half of the students would like to use the LO repeatedly. In addition, teachers agreed that DOLL act as a good new teaching tool in facilitating teaching process for dyslexic students

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 amplitude modulated (AM) noise with slow-rythmic prosodic (0.5–1Hz), syllabic (4–8Hz) or the phoneme (12–40Hz) 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 (�<0.005 ) 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

Tiled Sparse Coding in Eigenspaces for Image Classification

The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. These alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are first partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. Then, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. Our system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.MCIN/ AEI/10.13039/501100011033/FEDER “Una manera de hacer Europa” under the RTI2018- 098913-B100 projectConsejería de890 Economía, Innovación, Ciencia y Empleo (Junta de Andalucía)FEDER under CV20-45250, A- TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 project

Mobile Dyslexic Specialized Digital Game-based Learning Object for Learning Letters (DOLL)

Ability to read is a unanimous privilege we have as human. Therefore, research and development on Learning Object (LO) specially built for learning disabilities children is crucial. The issues for this project are the problem encounter by dyslexic students in output generation and information processes; and the factors that affect the effectiveness of mobile learning specially built for dyslexic. The focuses of this project are to propose guidelines and to facilitate student with visual dyslexia, or/and auditory dyslexia or/and dysgraphia; specifically in learning basic Malay language letters with interactive teaching method. Research has been made to identify the focused type of dyslexia, determine the needs of dyslexic; and the effect of graphic and animation on the efficiency of teaching technique. New multimedia-based learning object is being proposed to attract interest of dyslexic children to learn letters in fun approach and improve their recalling skills in recognizing name, shape and sound of letters. The main elements of the proposed learning object are animation, oral narration and digital gamebased. The theoretical framework proposed in this study is based on Principles of Teaching Program for Dyslexics, Stansfield Instructional Strategies, and Game-based Learning Object Framework. This project is carried out using ADDIE Instructional Design Model using Adobe Flash Professional cs5.5. A user experience testing is conducted with the dyslexic children. The result of the user experience testing showed more than half of the students would like to use the LO repeatedly. In addition, teachers agreed that DOLL act as a good new teaching tool in facilitating teaching process for dyslexic students