Version 1
: Received: 28 March 2020 / Approved: 1 April 2020 / Online: 1 April 2020 (07:52:09 CEST)
How to cite:
Bayat, M.; Boostani, R.; Sabeti, M.; Yadegari, F.; Pirmoradi, M.; KS, R.; Nami, M. Power Spectral Analysis upon Disfluent Utterance in Adults Who Stutter; a qEEG-based Investigation. Preprints2020, 2020040001. https://doi.org/10.20944/preprints202004.0001.v1
Bayat, M.; Boostani, R.; Sabeti, M.; Yadegari, F.; Pirmoradi, M.; KS, R.; Nami, M. Power Spectral Analysis upon Disfluent Utterance in Adults Who Stutter; a qEEG-based Investigation. Preprints 2020, 2020040001. https://doi.org/10.20944/preprints202004.0001.v1
Bayat, M.; Boostani, R.; Sabeti, M.; Yadegari, F.; Pirmoradi, M.; KS, R.; Nami, M. Power Spectral Analysis upon Disfluent Utterance in Adults Who Stutter; a qEEG-based Investigation. Preprints2020, 2020040001. https://doi.org/10.20944/preprints202004.0001.v1
APA Style
Bayat, M., Boostani, R., Sabeti, M., Yadegari, F., Pirmoradi, M., KS, R., & Nami, M. (2020). <strong>Power Spectral Analysis upon Disfluent Utterance in Adults Who Stutter; a qEEG-based Investigation</strong>. Preprints. https://doi.org/10.20944/preprints202004.0001.v1
Chicago/Turabian Style
Bayat, M., Rao KS and Mohammad Nami. 2020 "<strong>Power Spectral Analysis upon Disfluent Utterance in Adults Who Stutter; a qEEG-based Investigation</strong>" Preprints. https://doi.org/10.20944/preprints202004.0001.v1
Abstract
Purpose: The present study which addressed adults who stutter (AWS), has been an attempt to investigate power spectral dynamics in stuttering state through answering the written questions using quantitative electroencephalography (qEEG).Materials and Methods: A 64-channel EEG setup was used for data acquisition in 9 AWS. Since speech, and especially stuttering, causes significant noise in the EEG, the three conditions of speech preparation (SP), imagined speech (IS), and simulated speech (SS) in a 7-band format were chosen to source localize the signals using the standard low-resolution electromagnetic tomography (sLORETA) tool in fluent and disfluent states. Results: Having extracted enough fluent and disfluent utterances, significant differences were noted. Consistent with previous studies, the lack of beta suppression in SP, especially in beta2 and beta3 and somewhat in gamma band, was localized in supplementary motor area (SMA) and pre motor area in disfluent state. Delta band frequency was the best marker of stuttering shared in all 3 experimental conditions. Decreased delta power in SMA of both hemispheres and right premotor area through SP, in fronto-central and right angular gyrus through IS, and in SMA of both hemispheres through SS were a notable qEEG features of disfluent speech. Conclusion: The dynamics of beta and delta frequency bands may potentially explain the neural networks involved in stuttering. Based on the above, neurorehabilitation may better be formulated in the treatment of speech disfluency, namely stuttering.
Keywords
stuttering; power spectra; speech preparation; imagined speech; simulated speech
Subject
Medicine and Pharmacology, Neuroscience and Neurology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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