Electrophysiological Biomarkers of Developmental Stuttering: A Review of Auditory Evoked Potentials and Artificial-Intelligence-Supported Analyses
Abstract
Stuttering is a complex neurodevelopmental fluency disorder characterized by involuntary repetitions, prolongations, and blocks during speech production. Converging evidence from neuroimaging and electrophysiology suggests that stuttering reflects atypical interactions among motor, sensory, and cognitive systems rather than an isolated motor impairment. This review synthesizes findings from studies using event-related potentials (ERPs) and long-latency auditory evoked potentials (LLAEPs) particularly P1, N1, P2, N2, P300, and mismatch negativity (MMN) to characterize auditory–motor integration, pre-speech auditory modulation, and attentional processing in individuals who stutter. We further consider how machine-learning and deep-learning techniques could be integrated with these electrophysiological markers to potentially support objective diagnosis, prognosis, and individualized therapeutic monitoring. Together, the evidence suggests that LLAEP components may constitute promising biomarkers of neural adaptation in stuttering and could provide a neuroscientific framework for data-driven, personalized speech therapy.
Keywords:
stuttering auditory evoked potentials P300 neural plasticity machine learningReferences
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