TY - JOUR AU - Christoforou, Christoforos AU - Papadopoulos, Timothy C. AU - Theodorou, Maria PY - 2021/04/18 Y2 - 2024/03/29 TI - Single-trial FRPs: A Machine Learning Approach Towards the Study of the Neural Underpinning of Reading Disorders JF - The International FLAIRS Conference Proceedings JA - FLAIRS VL - 34 IS - 0 SE - Main Track Proceedings DO - 10.32473/flairs.v34i1.128446 UR - https://journals.flvc.org/FLAIRS/article/view/128446 SP - AB - <p class="AbstractText">Understanding the neural underpinning of reading disorders, such as dyslexia, is a fundamental question in developmental neuroscience. However, identifying and isolating informative neural components elicited during free-naming paradigms (i.e. unprompted and unconstrained naming tasks) has proven a challenging methodological task. These methodological barriers have hindered the study of the neural underpinnings of reading disorders. In this paper, we proposed a machine learning approach for detecting neural components during free-naming, overcoming much of the current methodological challenges. We propose a new neural-based metric to differentiate groups of children with dyslexia (DYS) and their chronological age controls (CAC) in a free-naming task. Our approach combines electroencephalography (EEG) and eye-tracking measures to generate single-trial fixation-related potentials (sFRPs) and formulate an optimization problem to extract naming-related neural components, informative of group differences. Our approach is validated on a real dataset involving children with dyslexia and CAC performing a Rapid-Automatized Naming (RAN) task. Our results demonstrate the validity of the proposed metric as an indicator of the neural-based markers of reading disorders. Importantly, our proposed framework provides a novel approach that can facilitate the study of neural correlates of reading disorders under paradigms current methods are unable to.</p> ER -