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EEG signal analysis of passage reading and rapid automatized naming between adults with dyslexia and normal controls

Technology plays a major role in discovering and improving accuracies of disorders and difficulties through identification of patterns. This paper attempts to discover such unique brainwave signal patterns found in adults with dyslexia using EEG while performing tasks that are more challenging for individuals with dyslexia. The EEG signals are collected from adults with dyslexia and normal controls during passage reading and rapid automatized naming. EEG signals provide valuable insights into the behaviour of the brain; however, identifying these patterns is not always quite straightforward due to its complexity. We identify these unique patterns and optimal brain regions for classification using machine learning. This study revealed that the greater level of difficulties seen in individuals with dyslexia while performing these tasks compared to normal controls are reflected in the brainwave signal patterns.