Christoforos Christoforou

Understanding the relationship between neural activations and cognitive factors in reading disorders, such as dyslexia, is a key goal in developmental neuroscience. Despite advancements in computational methods for extracting neural components at the aggregate, inter-subject variability in EEG signals make it challenging to perform across-group single-trial analysis on the neural mechanisms associated with reading disorders. Our contribution focuses on using the Neural-Congruency framework to address the issue of inter-subject variability in EEG data, which allows for single-trial analysis across groups of dyslexic and control participants. Preliminary results demonstrate the feasibility of this approach, highlighting its potential to deepen our understanding of the neural mechanisms underlying reading disorders. By providing a more granular and accurate analysis of neural activations, this study lays the groundwork for improved diagnostic and intervention strategies for dyslexia.

The study’s primary contribution is a game-based cognitive intervention tool for young learners with reading difficulties. The second contribution is the game designer's guide tool, which provides a set of recommended design guidelines for designers, developers and researchers that create games for children. The tools were designed and developed in the ReaDi-STANCE project and are accessible on the project’s platform (ReaDi-STANCE platform home page: https://www.cs.ucy.ac.cy/projects/readistance/). The platform has the following main modules integrated: a management support module, an algorithm module to rotate in a predefined order the games the child end-user plays, a prompting stages module to support the child end-user during gameplay, and a data analytics module that researchers can use. Two different groups of experts conducted two evaluations to evaluate: (1) a corpus of game design themes and guidelines for children, extracted from the respective literature, to develop the game designer's guide tool and (2) the extent of application of the design above guidelines in the game-based cognitive intervention tool. We present the intervention impact results and discuss their effect on improving reading skills in poor readers.

The COVID-19 pandemic catalyzed a significant shift towards online learning, revealing the potential of online educational videos as an educational tool in the post-crisis era. As we venture through this evolved educational terrain, it becomes crucial to understand the effectiveness and impact educational video content has on students' engagement and performance. This research explores how different styles of educational video content on STEM (Science Technology, Engineering, and Mathematics) topics impact students' engagement and comprehension using Electroencephalography (EEG) and eye-tracking data of participants viewing the educational video. In particular, we propose a machine learning driven analysis framework to study which EEG and eye-gaze-based metrics are informative of students' engagement and attention to STEM-related educational videos and predict student-population-wide comprehension. Although still in the preliminary stages, our research endeavors to identify correlations between neurophysiological patterns and educational engagement across disciplines.

Electroencephalogram (EEG) recordings of children are often used to study the underlying neural basis of causal factors of reading disorders and dyslexia. However, the inter-subject variability in EEG and the unconstrained nature of reading experiments used to elicit these factors made it challenging for traditional EEG analysis methods to extract neural components of these factors. In this work, we aim to explore the use of novel deep neural network architectures and contrastive learning methods to overcome the methodological limitations of traditional techniques and enhance the extraction process of neural components during reading tasks. Notably, we formulate a neural network architecture to extract EEG embedding using contrastive loss that maximizes the neural congruency in non-dyslexic children compared to children with dyslexia. We plan to evaluate our approach on three EEG datasets involving children with dyslexia performing Rapid Automatized Naming (RAN) and Phonological Processing (PA) tasks. The proposed contrastive learning framework will provide an enhanced tool to facilitate studying the neural underpinnings of naming speed and their association with reading performance and related difficulties.

Measuring simultaneous processing, a reliable predictor of reading development and reading difficulties (RDs), has traditionally involved cognitive tasks that test reaction or response time, which only capture the efficiency at the output processing stage and neglect the internal stages of information processing. However, with eye-tracking methodology, we can reveal the underlying temporal and spatial processes involved in simultaneous processing and investigate whether these processes are equivalent across chronological or reading age groups. This study used eye-tracking to investigate the simultaneous processing abilities of 15 Grade 6 and 15 Grade 3 children with RDs and their chronological-age controls (15 in each Grade). The Grade 3 typical readers were used as reading-level (RL) controls for the Grade 6 RD group. Participants were required to listen to a question and then point to a picture among four competing illustrations demonstrating the spatial relationship raised in the question. Two eye movements (fixations and saccades) were recorded using the EyeLink 1000 Plus eye-tracking system. The results showed that the Grade 3 RD group produced more and longer fixations than their CA controls, indicating that the pattern of eye movements of young children with RD is typically deficient compared to that of their typically developing counterparts when processing verbal and spatial stimuli simultaneously. However, no differences were observed between the Grade 6 groups in eye movement measures. Notably, the Grade 6 RD group outperformed the RL-matched Grade 3 group, yielding significantly fewer and shorter fixations. The discussion centers on the role of the eye-tracking method as a reliable means of deciphering the simultaneous cognitive processing involved in learning.

Objective: Naming speed, behaviorally measured via the serial Rapid automatized naming (RAN) test, is one of the most examined underlying cognitive factors of reading development and reading difficulties (RD). However, the unconstrained-reading format of serial RAN has made it challenging for traditional EEG analysis methods to extract neural components for studying the neural underpinnings of naming speed. The present study aims to explore a novel approach to isolate neural components during the serial RAN task that are (a) informative of group differences between children with dyslexia (DYS) and chronological age controls (CAC), (b) improve the power of analysis, and (c) are suitable for deciphering the neural underpinnings of naming speed.Methods: We propose a novel machine-learning-based algorithm that extracts spatiotemporal neural components during serial RAN, termed RAN-related neural-congruency components. We demonstrate our approach on EEG and eye-tracking recordings from 60 children (30 DYS and 30 CAC), under phonologically or visually similar, and dissimilar control tasks.Results: Results reveal significant differences in the RAN-related neural-congruency components between DYS and CAC groups in all four conditions.Conclusion: Rapid automatized naming-related neural-congruency components capture the neural activity of cognitive processes associated with naming speed and are informative of group differences between children with dyslexia and typically developing children.Significance: We propose the resulting RAN-related neural-components as a methodological framework to facilitate studying the neural underpinnings of naming speed and their association with reading performance and related difficulties.

Children's performance on the spoonerism task, a behavioral test that measures phonological processing skills, predicts reading abilities and related disorders. However, this relationship between phonological processing skills and dyslexia has been primarily examined based on behavioral responses to the spoonerism task. As a result, there is a growing interest in developmental neuroscience to explore the neural origins of this relationship and its relation to reading difficulties. Yet, traditional electroencephalography (EEG) analysis methods had little success identifying informative neural components that depict neural differences in children with reading disorders during spoonerism. The current study explores a novel computational approach to isolate informative neural signatures elicited during the spoonerism test. We apply our method to EEG data obtained from a group of children with dyslexia and controls during the execution of a spoonerism task. Our findings demonstrate that our method extracts components that characterize the neural origins of complex cognitive phonological processes, explains differences between children with dyslexia and controls, and generates novel insights into the neural underpinnings of dyslexia in children.