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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5783365/ https://www.ncbi.nlm.nih.gov/pubmed/29364932 http://dx.doi.org/10.1371/journal.pone.0191480 |
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author | Kang, Byeong Keun Kim, June Sic Ryun, Seokyun Chung, Chun Kee |
author_facet | Kang, Byeong Keun Kim, June Sic Ryun, Seokyun Chung, Chun Kee |
author_sort | Kang, Byeong Keun |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-5783365 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-57833652018-02-08 Prediction of movement intention using connectivity within motor-related network: An electrocorticography study Kang, Byeong Keun Kim, June Sic Ryun, Seokyun Chung, Chun Kee PLoS One Research Article 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. Public Library of Science 2018-01-24 /pmc/articles/PMC5783365/ /pubmed/29364932 http://dx.doi.org/10.1371/journal.pone.0191480 Text en © 2018 Kang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Kang, Byeong Keun Kim, June Sic Ryun, Seokyun Chung, Chun Kee Prediction of movement intention using connectivity within motor-related network: An electrocorticography study |
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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5783365/ https://www.ncbi.nlm.nih.gov/pubmed/29364932 http://dx.doi.org/10.1371/journal.pone.0191480 |
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