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Decoding unconstrained arm movements in primates using high-density electrocorticography signals for brain-machine interface use

Motor deficit is among the most debilitating aspects of injury to the central nervous system. Despite ongoing progress in brain-machine interface (BMI) development and in the functional electrical stimulation of muscles and nerves, little is understood about how neural signals in the brain may be us...

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Autores principales: Hu, Kejia, Jamali, Mohsen, Moses, Ziev B., Ortega, Carlos A., Friedman, Gabriel N., Xu, Wendong, Williams, Ziv M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6043557/
https://www.ncbi.nlm.nih.gov/pubmed/30002452
http://dx.doi.org/10.1038/s41598-018-28940-7
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author Hu, Kejia
Jamali, Mohsen
Moses, Ziev B.
Ortega, Carlos A.
Friedman, Gabriel N.
Xu, Wendong
Williams, Ziv M.
author_facet Hu, Kejia
Jamali, Mohsen
Moses, Ziev B.
Ortega, Carlos A.
Friedman, Gabriel N.
Xu, Wendong
Williams, Ziv M.
author_sort Hu, Kejia
collection PubMed
description Motor deficit is among the most debilitating aspects of injury to the central nervous system. Despite ongoing progress in brain-machine interface (BMI) development and in the functional electrical stimulation of muscles and nerves, little is understood about how neural signals in the brain may be used to potentially control movement in one’s own unconstrained paralyzed limb. We recorded from high-density electrocorticography (ECoG) electrode arrays in the ventral premotor cortex (PMv) of a rhesus macaque and used real-time motion tracking techniques to correlate spatial-temporal changes in neural activity with arm movements made towards objects in three-dimensional space at millisecond precision. We found that neural activity from a small number of electrodes within the PMv can be used to accurately predict reach-return movement onset and directionality. Also, whereas higher gamma frequency field activity was more predictive about movement direction during performance, mid-band (beta and low gamma) activity was more predictive of movement prior to onset. We speculate these dual spatiotemporal signals may be used to optimize both planning and execution of movement during natural reaching, with prospective relevance to the future development of neural prosthetics aimed at restoring motor control over one’s own paralyzed limb.
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spelling pubmed-60435572018-07-15 Decoding unconstrained arm movements in primates using high-density electrocorticography signals for brain-machine interface use Hu, Kejia Jamali, Mohsen Moses, Ziev B. Ortega, Carlos A. Friedman, Gabriel N. Xu, Wendong Williams, Ziv M. Sci Rep Article Motor deficit is among the most debilitating aspects of injury to the central nervous system. Despite ongoing progress in brain-machine interface (BMI) development and in the functional electrical stimulation of muscles and nerves, little is understood about how neural signals in the brain may be used to potentially control movement in one’s own unconstrained paralyzed limb. We recorded from high-density electrocorticography (ECoG) electrode arrays in the ventral premotor cortex (PMv) of a rhesus macaque and used real-time motion tracking techniques to correlate spatial-temporal changes in neural activity with arm movements made towards objects in three-dimensional space at millisecond precision. We found that neural activity from a small number of electrodes within the PMv can be used to accurately predict reach-return movement onset and directionality. Also, whereas higher gamma frequency field activity was more predictive about movement direction during performance, mid-band (beta and low gamma) activity was more predictive of movement prior to onset. We speculate these dual spatiotemporal signals may be used to optimize both planning and execution of movement during natural reaching, with prospective relevance to the future development of neural prosthetics aimed at restoring motor control over one’s own paralyzed limb. Nature Publishing Group UK 2018-07-12 /pmc/articles/PMC6043557/ /pubmed/30002452 http://dx.doi.org/10.1038/s41598-018-28940-7 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Hu, Kejia
Jamali, Mohsen
Moses, Ziev B.
Ortega, Carlos A.
Friedman, Gabriel N.
Xu, Wendong
Williams, Ziv M.
Decoding unconstrained arm movements in primates using high-density electrocorticography signals for brain-machine interface use
title Decoding unconstrained arm movements in primates using high-density electrocorticography signals for brain-machine interface use
title_full Decoding unconstrained arm movements in primates using high-density electrocorticography signals for brain-machine interface use
title_fullStr Decoding unconstrained arm movements in primates using high-density electrocorticography signals for brain-machine interface use
title_full_unstemmed Decoding unconstrained arm movements in primates using high-density electrocorticography signals for brain-machine interface use
title_short Decoding unconstrained arm movements in primates using high-density electrocorticography signals for brain-machine interface use
title_sort decoding unconstrained arm movements in primates using high-density electrocorticography signals for brain-machine interface use
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6043557/
https://www.ncbi.nlm.nih.gov/pubmed/30002452
http://dx.doi.org/10.1038/s41598-018-28940-7
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