Study on Brain Plasticity of EEG Signals Based on Motor Imagination Training

Yu Rui; Yin Kuiying

doi:doi:10.11648/j.si.20200802.16

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Study on Brain Plasticity of EEG Signals Based on Motor Imagination Training

Yu Rui, Yin Kuiying

Published in Science Innovation (Volume 8, Issue 2)

Received: 24 March 2020 Published: 19 May 2020

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Abstract

To explore the effects of hand motion imagery on brain plasticity for use in guiding disabled people to use robotic arms, this paper designed a comparison experiment between the disabled and normal people, and collected the EEG data of 4 subjects (2 disabled men who lost their right hands and 2 normal men) for five weeks of training. What’s more, this paper compared and analyzed the collected EEG rhythm through the brain topographic map. The results showed that after training, the disabled people could produce ERD in both the μ and β frequency bands, and the longer the training time, the more obvious the ERD phenomenon was. This paper also uses the common space pattern algorithm and support vector machine to extract and classify the features of EEG signals. The results show that the classification accuracy of the disabled can reach more than 85%, and that of the normal can reach more than 90%. Based on the results of brain topographical map and classification, this paper concludes that motor imagination training can have a positive effect on the brain of people with impaired motor area, which provides a neurophysiological basis for the extensive application of motor imagination training in the field of rehabilitation.

Published in	Science Innovation (Volume 8, Issue 2)
DOI	10.11648/j.si.20200802.16
Page(s)	48-53
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Motion Imagination, Brain Plasticity, Brain Topographic Map, Feature Extraction

References

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[11]	王娜. 基于脑电信号的运动想象分类研究[D].南京邮电大学,2018.
[12]	赵凯.多类运动想象脑电信号识别及其在BCI中的应用研究[D].东北电力大学,2019.
[13]	Vapnik V N. Statistical learning theory [M]. New York: Jone Wiley&Sons, 1998.
[14]	庄玮,段锁林,徐亭婷.基于SVM的4类运动想象的脑电信号分类方法[J].常州大学学报(自然科学版),2014,26(01):42-46.
[15]	杨淑莹. 模式识别与智能计算--Matlab技术实现[M]. 北京：电子工业出版社，2008.
[16]	王毅军. 结合能量和相位特征的多分类想象运动脑-机接口[C]. 中国电子学会生物医学电子学分会、中国生物医学工程学会生物医学测量分会、中国生物医学工程学会生物信息与控制分会、中国生物医学工程学会生物医学传感器技术分会.中国生物医学工程进展——2007中国生物医学工程联合学术年会论文集（下册）.中国电子学会生物医学电子学分会、中国生物医学工程学会生物医学测量分会、中国生物医学工程学会生物信息与控制分会、中国生物医学工程学会生物医学传感器技术分会:中国生物医学工程学会,2007:691-694.
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Yu Rui, Yin Kuiying. (2020). Study on Brain Plasticity of EEG Signals Based on Motor Imagination Training. Science Innovation, 8(2), 48-53. https://doi.org/10.11648/j.si.20200802.16

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ACS Style

Yu Rui; Yin Kuiying. Study on Brain Plasticity of EEG Signals Based on Motor Imagination Training. Sci. Innov. 2020, 8(2), 48-53. doi: 10.11648/j.si.20200802.16

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AMA Style

Yu Rui, Yin Kuiying. Study on Brain Plasticity of EEG Signals Based on Motor Imagination Training. Sci Innov. 2020;8(2):48-53. doi: 10.11648/j.si.20200802.16

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@article{10.11648/j.si.20200802.16,
  author = {Yu Rui and Yin Kuiying},
  title = {Study on Brain Plasticity of EEG Signals Based on Motor Imagination Training},
  journal = {Science Innovation},
  volume = {8},
  number = {2},
  pages = {48-53},
  doi = {10.11648/j.si.20200802.16},
  url = {https://doi.org/10.11648/j.si.20200802.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.si.20200802.16},
  abstract = {To explore the effects of hand motion imagery on brain plasticity for use in guiding disabled people to use robotic arms, this paper designed a comparison experiment between the disabled and normal people, and collected the EEG data of 4 subjects (2 disabled men who lost their right hands and 2 normal men) for five weeks of training. What’s more, this paper compared and analyzed the collected EEG rhythm through the brain topographic map. The results showed that after training, the disabled people could produce ERD in both the μ and β frequency bands, and the longer the training time, the more obvious the ERD phenomenon was. This paper also uses the common space pattern algorithm and support vector machine to extract and classify the features of EEG signals. The results show that the classification accuracy of the disabled can reach more than 85%, and that of the normal can reach more than 90%. Based on the results of brain topographical map and classification, this paper concludes that motor imagination training can have a positive effect on the brain of people with impaired motor area, which provides a neurophysiological basis for the extensive application of motor imagination training in the field of rehabilitation.},
 year = {2020}
}

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TY  - JOUR
T1  - Study on Brain Plasticity of EEG Signals Based on Motor Imagination Training
AU  - Yu Rui
AU  - Yin Kuiying
Y1  - 2020/05/19
PY  - 2020
N1  - https://doi.org/10.11648/j.si.20200802.16
DO  - 10.11648/j.si.20200802.16
T2  - Science Innovation
JF  - Science Innovation
JO  - Science Innovation
SP  - 48
EP  - 53
PB  - Science Publishing Group
SN  - 2328-787X
UR  - https://doi.org/10.11648/j.si.20200802.16
AB  - To explore the effects of hand motion imagery on brain plasticity for use in guiding disabled people to use robotic arms, this paper designed a comparison experiment between the disabled and normal people, and collected the EEG data of 4 subjects (2 disabled men who lost their right hands and 2 normal men) for five weeks of training. What’s more, this paper compared and analyzed the collected EEG rhythm through the brain topographic map. The results showed that after training, the disabled people could produce ERD in both the μ and β frequency bands, and the longer the training time, the more obvious the ERD phenomenon was. This paper also uses the common space pattern algorithm and support vector machine to extract and classify the features of EEG signals. The results show that the classification accuracy of the disabled can reach more than 85%, and that of the normal can reach more than 90%. Based on the results of brain topographical map and classification, this paper concludes that motor imagination training can have a positive effect on the brain of people with impaired motor area, which provides a neurophysiological basis for the extensive application of motor imagination training in the field of rehabilitation.
VL  - 8
IS  - 2
ER  -

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Author Information

Yu Rui

Nanjing Research Institute of Electronic Technology, Nanjing, China
Yin Kuiying

Nanjing Research Institute of Electronic Technology, Nanjing, China

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Plain Text BibTeX RIS

APA Style

Yu Rui, Yin Kuiying. (2020). Study on Brain Plasticity of EEG Signals Based on Motor Imagination Training. Science Innovation, 8(2), 48-53. https://doi.org/10.11648/j.si.20200802.16

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ACS Style

Yu Rui; Yin Kuiying. Study on Brain Plasticity of EEG Signals Based on Motor Imagination Training. Sci. Innov. 2020, 8(2), 48-53. doi: 10.11648/j.si.20200802.16

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AMA Style

Yu Rui, Yin Kuiying. Study on Brain Plasticity of EEG Signals Based on Motor Imagination Training. Sci Innov. 2020;8(2):48-53. doi: 10.11648/j.si.20200802.16

Copy | Download

@article{10.11648/j.si.20200802.16,
  author = {Yu Rui and Yin Kuiying},
  title = {Study on Brain Plasticity of EEG Signals Based on Motor Imagination Training},
  journal = {Science Innovation},
  volume = {8},
  number = {2},
  pages = {48-53},
  doi = {10.11648/j.si.20200802.16},
  url = {https://doi.org/10.11648/j.si.20200802.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.si.20200802.16},
  abstract = {To explore the effects of hand motion imagery on brain plasticity for use in guiding disabled people to use robotic arms, this paper designed a comparison experiment between the disabled and normal people, and collected the EEG data of 4 subjects (2 disabled men who lost their right hands and 2 normal men) for five weeks of training. What’s more, this paper compared and analyzed the collected EEG rhythm through the brain topographic map. The results showed that after training, the disabled people could produce ERD in both the μ and β frequency bands, and the longer the training time, the more obvious the ERD phenomenon was. This paper also uses the common space pattern algorithm and support vector machine to extract and classify the features of EEG signals. The results show that the classification accuracy of the disabled can reach more than 85%, and that of the normal can reach more than 90%. Based on the results of brain topographical map and classification, this paper concludes that motor imagination training can have a positive effect on the brain of people with impaired motor area, which provides a neurophysiological basis for the extensive application of motor imagination training in the field of rehabilitation.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Study on Brain Plasticity of EEG Signals Based on Motor Imagination Training
AU  - Yu Rui
AU  - Yin Kuiying
Y1  - 2020/05/19
PY  - 2020
N1  - https://doi.org/10.11648/j.si.20200802.16
DO  - 10.11648/j.si.20200802.16
T2  - Science Innovation
JF  - Science Innovation
JO  - Science Innovation
SP  - 48
EP  - 53
PB  - Science Publishing Group
SN  - 2328-787X
UR  - https://doi.org/10.11648/j.si.20200802.16
AB  - To explore the effects of hand motion imagery on brain plasticity for use in guiding disabled people to use robotic arms, this paper designed a comparison experiment between the disabled and normal people, and collected the EEG data of 4 subjects (2 disabled men who lost their right hands and 2 normal men) for five weeks of training. What’s more, this paper compared and analyzed the collected EEG rhythm through the brain topographic map. The results showed that after training, the disabled people could produce ERD in both the μ and β frequency bands, and the longer the training time, the more obvious the ERD phenomenon was. This paper also uses the common space pattern algorithm and support vector machine to extract and classify the features of EEG signals. The results show that the classification accuracy of the disabled can reach more than 85%, and that of the normal can reach more than 90%. Based on the results of brain topographical map and classification, this paper concludes that motor imagination training can have a positive effect on the brain of people with impaired motor area, which provides a neurophysiological basis for the extensive application of motor imagination training in the field of rehabilitation.
VL  - 8
IS  - 2
ER  -

Copy | Download