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Full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground
Human locomotion is a complex process that requires the integration of central and peripheral nervous signalling. Understanding the brain’s involvement in locomotion is challenging and is traditionally investigated during locomotor imagination or observation. However, stationary imaging methods lack...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6038848/ https://www.ncbi.nlm.nih.gov/pubmed/29989591 http://dx.doi.org/10.1038/sdata.2018.133 |
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author | Brantley, Justin A. Luu, Trieu Phat Nakagome, Sho Zhu, Fangshi Contreras-Vidal, Jose L. |
author_facet | Brantley, Justin A. Luu, Trieu Phat Nakagome, Sho Zhu, Fangshi Contreras-Vidal, Jose L. |
author_sort | Brantley, Justin A. |
collection | PubMed |
description | Human locomotion is a complex process that requires the integration of central and peripheral nervous signalling. Understanding the brain’s involvement in locomotion is challenging and is traditionally investigated during locomotor imagination or observation. However, stationary imaging methods lack the ability to infer information about the peripheral and central signalling during actual task execution. In this report, we present a dataset containing simultaneously recorded electroencephalography (EEG), lower-limb electromyography (EMG), and full body motion capture recorded from ten able-bodied individuals. The subjects completed an average of twenty trials on an experimental gait course containing level-ground, ramps, and stairs. We recorded 60-channel EEG from the scalp and 4-channel EOG from the face and temples. Surface EMG was recorded from six muscle sites bilaterally on the thigh and shank. The motion capture system consisted of seventeen wireless IMUs, allowing for unconstrained ambulation in the experimental space. In this report, we present the rationale for collecting these data, a detailed explanation of the experimental setup, and a brief validation of the data quality. |
format | Online Article Text |
id | pubmed-6038848 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-60388482018-07-12 Full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground Brantley, Justin A. Luu, Trieu Phat Nakagome, Sho Zhu, Fangshi Contreras-Vidal, Jose L. Sci Data Data Descriptor Human locomotion is a complex process that requires the integration of central and peripheral nervous signalling. Understanding the brain’s involvement in locomotion is challenging and is traditionally investigated during locomotor imagination or observation. However, stationary imaging methods lack the ability to infer information about the peripheral and central signalling during actual task execution. In this report, we present a dataset containing simultaneously recorded electroencephalography (EEG), lower-limb electromyography (EMG), and full body motion capture recorded from ten able-bodied individuals. The subjects completed an average of twenty trials on an experimental gait course containing level-ground, ramps, and stairs. We recorded 60-channel EEG from the scalp and 4-channel EOG from the face and temples. Surface EMG was recorded from six muscle sites bilaterally on the thigh and shank. The motion capture system consisted of seventeen wireless IMUs, allowing for unconstrained ambulation in the experimental space. In this report, we present the rationale for collecting these data, a detailed explanation of the experimental setup, and a brief validation of the data quality. Nature Publishing Group 2018-07-10 /pmc/articles/PMC6038848/ /pubmed/29989591 http://dx.doi.org/10.1038/sdata.2018.133 Text en Copyright © 2018, The Author(s) http://creativecommons.org/licenses/by/4.0/ 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/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article. |
spellingShingle | Data Descriptor Brantley, Justin A. Luu, Trieu Phat Nakagome, Sho Zhu, Fangshi Contreras-Vidal, Jose L. Full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground |
title | Full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground |
title_full | Full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground |
title_fullStr | Full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground |
title_full_unstemmed | Full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground |
title_short | Full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground |
title_sort | full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6038848/ https://www.ncbi.nlm.nih.gov/pubmed/29989591 http://dx.doi.org/10.1038/sdata.2018.133 |
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