Does EMG control lead to distinct motor adaptation?
Powered prostheses are controlled using electromyographic (EMG) signals, which may introduce high levels of uncertainty even for simple tasks. According to Bayesian theories, higher uncertainty should influence how the brain adapts motor commands in response to perceived errors. Such adaptation may...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-09-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00302/full |
| _version_ | 1819129053372743680 |
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| author | Reva E Johnson Reva E Johnson Konrad P. Kording Konrad P. Kording Levi eHargrove Levi eHargrove Jonathon W. Sensinger Jonathon W. Sensinger |
| author_facet | Reva E Johnson Reva E Johnson Konrad P. Kording Konrad P. Kording Levi eHargrove Levi eHargrove Jonathon W. Sensinger Jonathon W. Sensinger |
| author_sort | Reva E Johnson |
| collection | DOAJ |
| description | Powered prostheses are controlled using electromyographic (EMG) signals, which may introduce high levels of uncertainty even for simple tasks. According to Bayesian theories, higher uncertainty should influence how the brain adapts motor commands in response to perceived errors. Such adaptation may critically influence how patients interact with their prosthetic devices; however, we do not yet understand adaptation behavior with EMG control. Models of adaptation can offer insights on movement planning and feedback correction, but we first need to establish their validity for EMG control interfaces. Here we created a simplified comparison of prosthesis and able-bodied control by studying adaptation with three control interfaces: joint angle, joint torque, and EMG. Subjects used each of the control interfaces to perform a target-directed task with random visual perturbations. We investigated how control interface and visual uncertainty affected trial-by-trial adaptation. As predicted by Bayesian models, increased errors and decreased visual uncertainty led to faster adaptation. The control interface had no significant effect beyond influencing error sizes. This result suggests that Bayesian models are useful for describing prosthesis control and could facilitate further investigation to characterize the uncertainty faced by prosthesis users. A better understanding of factors affecting movement uncertainty will guide sensory feedback strategies for powered prostheses and clarify what feedback information best improves control. |
| first_indexed | 2024-12-22T08:37:36Z |
| format | Article |
| id | doaj.art-61ba3a47817d4aceb2ce9a6b7e4f246d |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-22T08:37:36Z |
| publishDate | 2014-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-61ba3a47817d4aceb2ce9a6b7e4f246d2022-12-21T18:32:19ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2014-09-01810.3389/fnins.2014.0030291144Does EMG control lead to distinct motor adaptation?Reva E Johnson0Reva E Johnson1Konrad P. Kording2Konrad P. Kording3Levi eHargrove4Levi eHargrove5Jonathon W. Sensinger6Jonathon W. Sensinger7Rehabilitation Institute of ChicagoNorthwestern UniversityRehabilitation Institute of ChicagoNorthwestern UniversityRehabilitation Institute of ChicagoNorthwestern UniversityNorthwestern UniversityUniversity of New BrunswickPowered prostheses are controlled using electromyographic (EMG) signals, which may introduce high levels of uncertainty even for simple tasks. According to Bayesian theories, higher uncertainty should influence how the brain adapts motor commands in response to perceived errors. Such adaptation may critically influence how patients interact with their prosthetic devices; however, we do not yet understand adaptation behavior with EMG control. Models of adaptation can offer insights on movement planning and feedback correction, but we first need to establish their validity for EMG control interfaces. Here we created a simplified comparison of prosthesis and able-bodied control by studying adaptation with three control interfaces: joint angle, joint torque, and EMG. Subjects used each of the control interfaces to perform a target-directed task with random visual perturbations. We investigated how control interface and visual uncertainty affected trial-by-trial adaptation. As predicted by Bayesian models, increased errors and decreased visual uncertainty led to faster adaptation. The control interface had no significant effect beyond influencing error sizes. This result suggests that Bayesian models are useful for describing prosthesis control and could facilitate further investigation to characterize the uncertainty faced by prosthesis users. A better understanding of factors affecting movement uncertainty will guide sensory feedback strategies for powered prostheses and clarify what feedback information best improves control.http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00302/fulluncertaintyprostheticsBayesian ModelsEMGSensorimotor adaptation |
| spellingShingle | Reva E Johnson Reva E Johnson Konrad P. Kording Konrad P. Kording Levi eHargrove Levi eHargrove Jonathon W. Sensinger Jonathon W. Sensinger Does EMG control lead to distinct motor adaptation? Frontiers in Neuroscience uncertainty prosthetics Bayesian Models EMG Sensorimotor adaptation |
| title | Does EMG control lead to distinct motor adaptation? |
| title_full | Does EMG control lead to distinct motor adaptation? |
| title_fullStr | Does EMG control lead to distinct motor adaptation? |
| title_full_unstemmed | Does EMG control lead to distinct motor adaptation? |
| title_short | Does EMG control lead to distinct motor adaptation? |
| title_sort | does emg control lead to distinct motor adaptation |
| topic | uncertainty prosthetics Bayesian Models EMG Sensorimotor adaptation |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00302/full |
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