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

Integrating large language models and active inference to understand eye movements in reading and dyslexia

We present a novel computational model employing hierarchical active inference to simulate reading and eye movements. The model characterizes linguistic processing as inference over a hierarchical generative model, facilitating predictions and inferences at various levels of granularity, from syllables to sentences. Our approach combines the strengths of large language models for realistic textual predictions and active inference for guiding eye movements to informative textual information, enabling the testing of predictions. The model exhibits proficiency in reading both known and unknown words and sentences, adhering to the distinction between lexical and nonlexical routes in dual-route theories of reading. Notably, our model permits the exploration of maladaptive inference effects on eye movements during reading, such as in dyslexia. To simulate this condition, we attenuate the contribution of priors during the reading process, leading to incorrect inferences and a more fragmented reading style, characterized by a greater number of shorter saccades. This alignment with empirical findings regarding eye movements in dyslexic individuals highlights the model's potential to aid in understanding the cognitive processes underlying reading and eye movements, as well as how reading deficits associated with dyslexia may emerge from maladaptive predictive processing. In summary, our model represents a significant advancement in comprehending the intricate cognitive processes involved in reading and eye movements, with potential implications for understanding and addressing dyslexia through the simulation of maladaptive inference. It may offer valuable insights into this condition and contribute to the development of more effective interventions for treatment.Comment: 23 pages, 1 Appendix, 11 Tables, 9 Figure

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

Hidden Markov model finds behavioral patterns of users working with a headmouse driven writing tool

We studied user behaviors when the cursor is directed by a head in a simple control task. We used an intelligent writing tool called Dasher. Hidden Markov models (HMMs) were applied to separate behavioral patterns. We found that a similar It is argued that the recognition of general application specific behavioral patterns should he of help for adaptive human-computer interfaces

Multimodality in Online Education: A Comparative Study

The commencement of the decade brought along with it a grave pandemic and in response the movement of education forums predominantly into the online world. With a surge in the usage of online video conferencing platforms and tools to better gauge student understanding, there needs to be a mechanism to assess whether instructors can grasp the extent to which students understand the subject and their response to the educational stimuli. The current systems consider only a single cue with a lack of focus in the educational domain. Thus, there is a necessity for the measurement of an all-encompassing holistic overview of the students' reaction to the subject matter. This paper highlights the need for a multimodal approach to affect recognition and its deployment in the online classroom while considering four cues, posture and gesture, facial, eye tracking and verbal recognition. It compares the various machine learning models available for each cue and provides the most suitable approach given the available dataset and parameters of classroom footage. A multimodal approach derived from weighted majority voting is proposed by combining the most fitting models from this analysis of individual cues based on accuracy, ease of procuring data corpus, sensitivity and any major drawbacks

Shape analysis of the human brain.

Autism is a complex developmental disability that has dramatically increased in prevalence, having a decisive impact on the health and behavior of children. Methods used to detect and recommend therapies have been much debated in the medical community because of the subjective nature of diagnosing autism. In order to provide an alternative method for understanding autism, the current work has developed a 3-dimensional state-of-the-art shape based analysis of the human brain to aid in creating more accurate diagnostic assessments and guided risk analyses for individuals with neurological conditions, such as autism. Methods: The aim of this work was to assess whether the shape of the human brain can be used as a reliable source of information for determining whether an individual will be diagnosed with autism. The study was conducted using multi-center databases of magnetic resonance images of the human brain. The subjects in the databases were analyzed using a series of algorithms consisting of bias correction, skull stripping, multi-label brain segmentation, 3-dimensional mesh construction, spherical harmonic decomposition, registration, and classification. The software algorithms were developed as an original contribution of this dissertation in collaboration with the BioImaging Laboratory at the University of Louisville Speed School of Engineering. The classification of each subject was used to construct diagnoses and therapeutic risk assessments for each patient. Results: A reliable metric for making neurological diagnoses and constructing therapeutic risk assessment for individuals has been identified. The metric was explored in populations of individuals having autism spectrum disorders, dyslexia, Alzheimers disease, and lung cancer. Conclusion: Currently, the clinical applicability and benefits of the proposed software approach are being discussed by the broader community of doctors, therapists, and parents for use in improving current methods by which autism spectrum disorders are diagnosed and understood

Functional Magnetic Resonance Imaging

"Functional Magnetic Resonance Imaging - Advanced Neuroimaging Applications" is a concise book on applied methods of fMRI used in assessment of cognitive functions in brain and neuropsychological evaluation using motor-sensory activities, language, orthographic disabilities in children. The book will serve the purpose of applied neuropsychological evaluation methods in neuropsychological research projects, as well as relatively experienced psychologists and neuroscientists. Chapters are arranged in the order of basic concepts of fMRI and physiological basis of fMRI after event-related stimulus in first two chapters followed by new concepts of fMRI applied in constraint-induced movement therapy; reliability analysis; refractory SMA epilepsy; consciousness states; rule-guided behavioral analysis; orthographic frequency neighbor analysis for phonological activation; and quantitative multimodal spectroscopic fMRI to evaluate different neuropsychological states