Analyzing Speech Motor Movement using Surface Electromyography in Minimally Verbal Adults with Autism Spectrum Disorder
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916320350568448 |
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| author | Zulfikar, Wazeer Protyasha, Nishat Canales, Camila Patel, Heli Williamson, James Sarnie, Laura Nowinski, Lisa Kosmyna, Nataliya Townsend, Paige Yuditskaya, Sophia Talkar, Tanya Sarawgi, Utkarsh Oggy McDougle, Christopher Quatieri, Thomas Maes, Pattie Mody, Maria |
| author_facet | Zulfikar, Wazeer Protyasha, Nishat Canales, Camila Patel, Heli Williamson, James Sarnie, Laura Nowinski, Lisa Kosmyna, Nataliya Townsend, Paige Yuditskaya, Sophia Talkar, Tanya Sarawgi, Utkarsh Oggy McDougle, Christopher Quatieri, Thomas Maes, Pattie Mody, Maria |
| contents | Adults who are minimally verbal with autism spectrum disorder (mvASD) have pronounced speech difficulties linked to impaired motor skills. Existing research and clinical assessments primarily use indirect methods such as standardized tests, video-based facial features, and handwriting tasks, which may not directly target speech-related motor skills. In this study, we measure activity from eight facial muscles associated with speech using surface electromyography (sEMG), during carefully designed tasks. The findings reveal a higher power in the sEMG signals and a significantly greater correlation between the sEMG channels in mvASD adults (N=12) compared to age and gender-matched neurotypical controls (N=14). This suggests stronger muscle activation and greater synchrony in the discharge patterns of motor units. Further, eigenvalues derived from correlation matrices indicate lower complexity in muscle coordination in mvASD, implying fewer degrees of freedom in motor control. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08877 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analyzing Speech Motor Movement using Surface Electromyography in Minimally Verbal Adults with Autism Spectrum Disorder Zulfikar, Wazeer Protyasha, Nishat Canales, Camila Patel, Heli Williamson, James Sarnie, Laura Nowinski, Lisa Kosmyna, Nataliya Townsend, Paige Yuditskaya, Sophia Talkar, Tanya Sarawgi, Utkarsh Oggy McDougle, Christopher Quatieri, Thomas Maes, Pattie Mody, Maria Neurons and Cognition Human-Computer Interaction Adults who are minimally verbal with autism spectrum disorder (mvASD) have pronounced speech difficulties linked to impaired motor skills. Existing research and clinical assessments primarily use indirect methods such as standardized tests, video-based facial features, and handwriting tasks, which may not directly target speech-related motor skills. In this study, we measure activity from eight facial muscles associated with speech using surface electromyography (sEMG), during carefully designed tasks. The findings reveal a higher power in the sEMG signals and a significantly greater correlation between the sEMG channels in mvASD adults (N=12) compared to age and gender-matched neurotypical controls (N=14). This suggests stronger muscle activation and greater synchrony in the discharge patterns of motor units. Further, eigenvalues derived from correlation matrices indicate lower complexity in muscle coordination in mvASD, implying fewer degrees of freedom in motor control. |
| title | Analyzing Speech Motor Movement using Surface Electromyography in Minimally Verbal Adults with Autism Spectrum Disorder |
| topic | Neurons and Cognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2407.08877 |