Electroencephalographic Classification Reveals Atypical Speech Motor Planning in Stuttering Adults.
Purpose: This study explores speech motor planning in adults who stutter (AWS) and adults who do not stutter (ANS) by applying machine learning algorithms to electroencephalographic (EEG) signals. In this study, we developed a technique to holistically examine neural activity differences in speaking...
| Published in: | Journal of Speech, Language & Hearing Research Vol. 67; no. 7; pp. 2053 - 2077 |
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| Main Authors: | , , , , |
| Format: | Article |
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American Speech-Language-Hearing Association
Jul2024
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=178362725&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 178362725 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: Jul2024 vid: 67 iid: 7 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 178362725 10.1044/2024_JSLHR-23-00635 ppf: 2053 ppct: 24 formats: fmt: @attributes: type: P size: 3.1MB tig: atl: Electroencephalographic Classification Reveals Atypical Speech Motor Planning in Stuttering Adults. aug: au: Kinahan, Sean P. Saidi, Pouria Daliri, Ayoub Liss, Julie Berisha, Visar affil: College of Health Solutions, Arizona State University, Tempe. School of Computing and Augmented Intelligence, Arizona State University, Tempe. School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe. Department of Speech and Hearing Science, Arizona State University, Tempe. su: Motor ability Reading Speech Assistive technology Adults T-test (Statistics) Electroencephalography Stuttering Magnetic resonance imaging Mann Whitney U Test Support vector machines Artificial neural networks Machine learning Neuroradiology Speech therapy sug: subj: Motor ability Reading Speech Assistive technology Adults Diagnostic Imaging Centers T-test (Statistics) Electroencephalography Stuttering Magnetic resonance imaging Mann Whitney U Test Support vector machines Artificial neural networks Machine learning Neuroradiology Speech therapy ab: Purpose: This study explores speech motor planning in adults who stutter (AWS) and adults who do not stutter (ANS) by applying machine learning algorithms to electroencephalographic (EEG) signals. In this study, we developed a technique to holistically examine neural activity differences in speaking and silent reading conditions across the entire cortical surface. This approach allows us to test the hypothesis that AWS will exhibit lower separability of the speech motor planning condition. Method: We used the silent reading condition as a control condition to isolate speech motor planning activity. We classified EEG signals from AWS and ANS individuals into speaking and silent reading categories using kernel support vector machines. We used relative complexities of the learned classifiers to compare speech motor planning discernibility for both classes. Results: AWS group classifiers require a more complex decision boundary to separate speech motor planning and silent reading classes. Conclusions: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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