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 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.