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A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces
Studying the motor-control mechanisms of the brain is critical in academia and also has practical implications because techniques such as brain-computer interfaces (BCIs) can be developed based on brain mechanisms. Magnetoencephalography (MEG) signals have the highest spatial resolution (~3 mm) and...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10444808/ https://www.ncbi.nlm.nih.gov/pubmed/37607973 http://dx.doi.org/10.1038/s41597-023-02454-y |
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author | Yeom, Hong Gi Kim, June Sic Chung, Chun Kee |
author_facet | Yeom, Hong Gi Kim, June Sic Chung, Chun Kee |
author_sort | Yeom, Hong Gi |
collection | PubMed |
description | Studying the motor-control mechanisms of the brain is critical in academia and also has practical implications because techniques such as brain-computer interfaces (BCIs) can be developed based on brain mechanisms. Magnetoencephalography (MEG) signals have the highest spatial resolution (~3 mm) and temporal resolution (~1 ms) among the non-invasive methods. Therefore, the MEG is an excellent modality for investigating brain mechanisms. However, publicly available MEG data remains scarce due to expensive MEG equipment, requiring a magnetically shielded room, and high maintenance costs for the helium gas supply. In this study, we share the 306-channel MEG and 3-axis accelerometer signals acquired during three-dimensional reaching movements. Additionally, we provide analysis results and MATLAB codes for time-frequency analysis, F-value time-frequency analysis, and topography analysis. These shared MEG datasets offer valuable resources for investigating brain activities or evaluating the accuracy of prediction algorithms. To the best of our knowledge, this data is the only publicly available MEG data measured during reaching movements. |
format | Online Article Text |
id | pubmed-10444808 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104448082023-08-24 A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces Yeom, Hong Gi Kim, June Sic Chung, Chun Kee Sci Data Data Descriptor Studying the motor-control mechanisms of the brain is critical in academia and also has practical implications because techniques such as brain-computer interfaces (BCIs) can be developed based on brain mechanisms. Magnetoencephalography (MEG) signals have the highest spatial resolution (~3 mm) and temporal resolution (~1 ms) among the non-invasive methods. Therefore, the MEG is an excellent modality for investigating brain mechanisms. However, publicly available MEG data remains scarce due to expensive MEG equipment, requiring a magnetically shielded room, and high maintenance costs for the helium gas supply. In this study, we share the 306-channel MEG and 3-axis accelerometer signals acquired during three-dimensional reaching movements. Additionally, we provide analysis results and MATLAB codes for time-frequency analysis, F-value time-frequency analysis, and topography analysis. These shared MEG datasets offer valuable resources for investigating brain activities or evaluating the accuracy of prediction algorithms. To the best of our knowledge, this data is the only publicly available MEG data measured during reaching movements. Nature Publishing Group UK 2023-08-22 /pmc/articles/PMC10444808/ /pubmed/37607973 http://dx.doi.org/10.1038/s41597-023-02454-y Text en © The Author(s) 2023 https://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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Yeom, Hong Gi Kim, June Sic Chung, Chun Kee A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces |
title | A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces |
title_full | A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces |
title_fullStr | A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces |
title_full_unstemmed | A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces |
title_short | A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces |
title_sort | magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10444808/ https://www.ncbi.nlm.nih.gov/pubmed/37607973 http://dx.doi.org/10.1038/s41597-023-02454-y |
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