Classification of movement-related cortical potentials for multi-command control based on brain-machine interface

Ji Yong Kim, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Decoding of various motor intentions for generating command is one of the important factors in brain-based wheelchair system. The goal of this study focuses on classifying four types of trunk-related motor execution and imagery intentions. By brain components which are related to the trunk-related movements (waist, shoulder, and trunk) are generated in the very small and very close brain areas; therefore, decoding of trunk-related motor intentions are not easy for providing reliable system commands. To the best of our knowledge, the problems mentioned above have not been explored in the literature. In this study, we first validated the decoding accuracy of trunk-related motor intention based movement-related cortical potential. A set of binary classification performance which are shoulder extension (SE), waist rotation (WR), trunk flexion (TF), and rest (RE) have validated in respect to execution movement as well as imagery movement across six subjects. All binary classification results showed performance that is higher than the chance level. The best decoding accuracy shows 68.5% in the motor imagery task of shoulder extension vs. waist rotation.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1506-1509
Number of pages4
ISBN (Electronic)9781509018970
DOIs
Publication statusPublished - 2017 Feb 6
Event2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary
Duration: 2016 Oct 92016 Oct 12

Other

Other2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
CountryHungary
CityBudapest
Period16/10/916/10/12

Fingerprint

Brain
Decoding
Binary Classification
Wheelchairs
Movement
Imagery

Keywords

  • Brain-machine interface (BMI)
  • Motor imagery (MI)
  • Movement-related cortical potential (MRCP)

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Control and Optimization
  • Human-Computer Interaction

Cite this

Kim, J. Y., & Lee, S. W. (2017). Classification of movement-related cortical potentials for multi-command control based on brain-machine interface. In 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings (pp. 1506-1509). [7844451] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2016.7844451

Classification of movement-related cortical potentials for multi-command control based on brain-machine interface. / Kim, Ji Yong; Lee, Seong Whan.

2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1506-1509 7844451.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kim, JY & Lee, SW 2017, Classification of movement-related cortical potentials for multi-command control based on brain-machine interface. in 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings., 7844451, Institute of Electrical and Electronics Engineers Inc., pp. 1506-1509, 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016, Budapest, Hungary, 16/10/9. https://doi.org/10.1109/SMC.2016.7844451
Kim JY, Lee SW. Classification of movement-related cortical potentials for multi-command control based on brain-machine interface. In 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1506-1509. 7844451 https://doi.org/10.1109/SMC.2016.7844451
Kim, Ji Yong ; Lee, Seong Whan. / Classification of movement-related cortical potentials for multi-command control based on brain-machine interface. 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1506-1509
@inproceedings{205bc403eeff4722afec0049e8461b0e,
title = "Classification of movement-related cortical potentials for multi-command control based on brain-machine interface",
abstract = "Decoding of various motor intentions for generating command is one of the important factors in brain-based wheelchair system. The goal of this study focuses on classifying four types of trunk-related motor execution and imagery intentions. By brain components which are related to the trunk-related movements (waist, shoulder, and trunk) are generated in the very small and very close brain areas; therefore, decoding of trunk-related motor intentions are not easy for providing reliable system commands. To the best of our knowledge, the problems mentioned above have not been explored in the literature. In this study, we first validated the decoding accuracy of trunk-related motor intention based movement-related cortical potential. A set of binary classification performance which are shoulder extension (SE), waist rotation (WR), trunk flexion (TF), and rest (RE) have validated in respect to execution movement as well as imagery movement across six subjects. All binary classification results showed performance that is higher than the chance level. The best decoding accuracy shows 68.5{\%} in the motor imagery task of shoulder extension vs. waist rotation.",
keywords = "Brain-machine interface (BMI), Motor imagery (MI), Movement-related cortical potential (MRCP)",
author = "Kim, {Ji Yong} and Lee, {Seong Whan}",
year = "2017",
month = "2",
day = "6",
doi = "10.1109/SMC.2016.7844451",
language = "English",
pages = "1506--1509",
booktitle = "2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Classification of movement-related cortical potentials for multi-command control based on brain-machine interface

AU - Kim, Ji Yong

AU - Lee, Seong Whan

PY - 2017/2/6

Y1 - 2017/2/6

N2 - Decoding of various motor intentions for generating command is one of the important factors in brain-based wheelchair system. The goal of this study focuses on classifying four types of trunk-related motor execution and imagery intentions. By brain components which are related to the trunk-related movements (waist, shoulder, and trunk) are generated in the very small and very close brain areas; therefore, decoding of trunk-related motor intentions are not easy for providing reliable system commands. To the best of our knowledge, the problems mentioned above have not been explored in the literature. In this study, we first validated the decoding accuracy of trunk-related motor intention based movement-related cortical potential. A set of binary classification performance which are shoulder extension (SE), waist rotation (WR), trunk flexion (TF), and rest (RE) have validated in respect to execution movement as well as imagery movement across six subjects. All binary classification results showed performance that is higher than the chance level. The best decoding accuracy shows 68.5% in the motor imagery task of shoulder extension vs. waist rotation.

AB - Decoding of various motor intentions for generating command is one of the important factors in brain-based wheelchair system. The goal of this study focuses on classifying four types of trunk-related motor execution and imagery intentions. By brain components which are related to the trunk-related movements (waist, shoulder, and trunk) are generated in the very small and very close brain areas; therefore, decoding of trunk-related motor intentions are not easy for providing reliable system commands. To the best of our knowledge, the problems mentioned above have not been explored in the literature. In this study, we first validated the decoding accuracy of trunk-related motor intention based movement-related cortical potential. A set of binary classification performance which are shoulder extension (SE), waist rotation (WR), trunk flexion (TF), and rest (RE) have validated in respect to execution movement as well as imagery movement across six subjects. All binary classification results showed performance that is higher than the chance level. The best decoding accuracy shows 68.5% in the motor imagery task of shoulder extension vs. waist rotation.

KW - Brain-machine interface (BMI)

KW - Motor imagery (MI)

KW - Movement-related cortical potential (MRCP)

UR - http://www.scopus.com/inward/record.url?scp=85015793534&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85015793534&partnerID=8YFLogxK

U2 - 10.1109/SMC.2016.7844451

DO - 10.1109/SMC.2016.7844451

M3 - Conference contribution

AN - SCOPUS:85015793534

SP - 1506

EP - 1509

BT - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -