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Source Localization and Spectrum Analyzing of EEG in Stuttering State upon Dysfluent Utterances.

Masoumeh Bayat1, Reza Boostani2, Malihe Sabeti3

  • 1Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran.

Clinical EEG and Neuroscience
|January 11, 2023
PubMed
Summary

Quantitative electroencephalography revealed distinct brain activity patterns in adults who stutter (AWS). Differences in alpha, beta, and theta brainwaves were observed during fluent and dysfluent speech states.

Keywords:
imagined speechpower spectraspeech preparationstuttering state

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Area of Science:

  • Neuroscience
  • Speech and Language Sciences
  • Biomedical Engineering

Background:

  • Stuttering is a complex speech disorder affecting adults.
  • Understanding the neural underpinnings of stuttering is crucial for developing effective interventions.
  • Quantitative electroencephalography (qEEG) offers a non-invasive method to study brain activity during speech production.

Purpose of the Study:

  • To investigate power spectral dynamics in adults who stutter (AWS) during fluent and dysfluent speech states.
  • To identify specific electroencephalography (EEG) patterns associated with stuttering.
  • To explore the neural correlates of stuttering using quantitative EEG.

Main Methods:

  • Utilized a 64-channel electroencephalography (EEG) setup for data acquisition in 20 AWS.
  • Employed speech preparation (SP) and imagined speech (IS) conditions to minimize speech-related noise.
  • Decomposed EEG signals into 6 frequency bands and localized sources using standard low-resolution electromagnetic tomography (sLORETA).

Main Results:

  • Significant differences in time-locked EEG signals were observed between fluent and dysfluent speech.
  • Poor alpha and beta suppression in the left frontotemporal areas (and partly right frontal) characterized dysfluent states.
  • Increased theta band activation in motor areas and delta/beta2 power in motor/parietal regions were associated with fluent speech.

Conclusions:

  • Neural circuitries involved in stuttering likely require examination across the entire speech frequency spectrum.
  • Specific EEG frequency bands and their spatial localization provide insights into the neural basis of stuttering.
  • Findings contribute to a more comprehensive understanding of the neurophysiology of stuttering.