Objective: We explored neural components in Electroencephalography (EEG) signals during a phonological processing task to assess (a) the neural origins of Baddeley's working-memory components contributing to phonological processing, (b) the unitary structure of phonological processing and (c) the neural differences between children with dyslexia (DYS) and controls (CAC).
Methods: EEG data were collected from sixty children (half with dyslexia) while performing the initial- and final- phoneme elision task. We explored a novel machine-learning-based approach to identify the neural components in EEG elicited in response to the two conditions and capture differences between DYS and CAC.
Results: Our method identifies two sets of phoneme-related neural congruency components capturing neural activations distinguishing DYS and CAC across conditions.
Conclusions: Neural congruency components capture the underlying neural mechanisms that drive the relationship between phonological deficits and dyslexia and provide insights into the phonological loop and visual-sketchpad dimensions in Baddeley's model at the neural level. They also confirm the unitary structure of phonological awareness with EEG data.
Significance: Our findings provide novel insights into the neural origins of the phonological processing differences in children with dyslexia, the unitary structure of phonological awareness, and further verify Baddeley's model as a theoretical framework for phonological processing and dyslexia.
Keywords: Baddeley’s working memory model; EEG; Machine Learning; Neural congruency; Phoneme Elision; Phonological Awareness Unitary structure; Phonological awareness.
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