Prediction of movement intention using connectivity within motor-related network: An electrocorticography study.
Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2018-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5783365?pdf=render |
| _version_ | 1828357924226859008 |
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| author | Byeong Keun Kang June Sic Kim Seokyun Ryun Chun Kee Chung |
| author_facet | Byeong Keun Kang June Sic Kim Seokyun Ryun Chun Kee Chung |
| author_sort | Byeong Keun Kang |
| collection | DOAJ |
| description | Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention. |
| first_indexed | 2024-04-14T03:21:42Z |
| format | Article |
| id | doaj.art-41b65318ad6746a4965cc99172a3d419 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-14T03:21:42Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-41b65318ad6746a4965cc99172a3d4192022-12-22T02:15:17ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01131e019148010.1371/journal.pone.0191480Prediction of movement intention using connectivity within motor-related network: An electrocorticography study.Byeong Keun KangJune Sic KimSeokyun RyunChun Kee ChungMost brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.http://europepmc.org/articles/PMC5783365?pdf=render |
| spellingShingle | Byeong Keun Kang June Sic Kim Seokyun Ryun Chun Kee Chung Prediction of movement intention using connectivity within motor-related network: An electrocorticography study. PLoS ONE |
| title | Prediction of movement intention using connectivity within motor-related network: An electrocorticography study. |
| title_full | Prediction of movement intention using connectivity within motor-related network: An electrocorticography study. |
| title_fullStr | Prediction of movement intention using connectivity within motor-related network: An electrocorticography study. |
| title_full_unstemmed | Prediction of movement intention using connectivity within motor-related network: An electrocorticography study. |
| title_short | Prediction of movement intention using connectivity within motor-related network: An electrocorticography study. |
| title_sort | prediction of movement intention using connectivity within motor related network an electrocorticography study |
| url | http://europepmc.org/articles/PMC5783365?pdf=render |
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