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Electroencephalographic Classification Reveals Atypical Speech Motor Planning in Stuttering Adults.

Sean P KinahanPouria SaidiAyoub DaliriJulie M LissVisar Berisha
Published in: Journal of speech, language, and hearing research : JSLHR (2024)
These findings indicate that the EEG signals associated with speech motor planning are less discernible in AWS, which may result from altered neuronal dynamics in AWS. Our results support the hypothesis that AWS exhibit lower inherent separability of the silent reading and speech motor planning conditions. Further investigation may identify and compare the features leveraged for speech motor classification in AWS and ANS. These observations may have clinical value for developing novel speech therapies or assistive devices for AWS.
Keyphrases
  • machine learning
  • hearing loss
  • deep learning
  • working memory
  • subarachnoid hemorrhage