Decoding of Multi-directional Reaching Movements for EEG-Based Robot Arm Control

Ji Hoon Jeong; Keun Tae Kim; Dong Ju Kim; Seong Whan Lee

doi:10.1109/SMC.2018.00096

Decoding of Multi-directional Reaching Movements for EEG-Based Robot Arm Control

Ji Hoon Jeong, Keun Tae Kim, Dong Ju Kim, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

20 Citations (Scopus)

Abstract

This paper presents the feasibility of an electroencephalography (EEG)-based robot arm control system using a decoding of multi-directional arm reaching movement imagery. To do that, we have designed and implemented an experimental environment that can acquire non-invasive brain signals about multi-directional arm reaching movement. Five subjects participated in our experiments and the subjects performed four directional reaching tasks (Left, right, forward, and backward) with actual movement and movement imagery. The filter-bank common spatial pattern (FBCSP) was applied to extract spatio-frequency features from the acquired EEG signals. The regularized linear discriminant analysis (RLDA) was also applied as a classifier. As a result, the averaged classification accuracies of the actual movement and movement imagery were represented 67.04% and 59.19%, respectively. These results showed a feasibility of the EEG-based robot arm control system based on multi-directional arm reaching movement imagery.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	511-514
Number of pages	4
ISBN (Electronic)	9781538666500
DOIs	https://doi.org/10.1109/SMC.2018.00096
Publication status	Published - 2018 Jul 2
Event	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan Duration: 2018 Oct 7 → 2018 Oct 10

Publication series

Name	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Country/Territory	Japan
City	Miyazaki
Period	18/10/7 → 18/10/10

Bibliographical note

Funding Information:
This work was partly supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No. 2017-0-00432, Development of non-invasive integrated BCI SW platform to control home appliances and external devices by user's thought via AR/VR interface) and partly funded by Microsoft Research Asia.

Publisher Copyright:
© 2018 IEEE.

Keywords

a robot arm control
brain-machine interface
electroencephalography
muti-directional arm reaching movement

ASJC Scopus subject areas

Information Systems
Information Systems and Management
Health Informatics
Artificial Intelligence
Computer Networks and Communications
Human-Computer Interaction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/SMC.2018.00096

Cite this

Jeong, J. H., Kim, K. T., Kim, D. J., & Lee, S. W. (2018). Decoding of Multi-directional Reaching Movements for EEG-Based Robot Arm Control. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 511-514). Article 8616091 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2018.00096

Decoding of Multi-directional Reaching Movements for EEG-Based Robot Arm Control. / Jeong, Ji Hoon; Kim, Keun Tae; Kim, Dong Ju et al.
Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 511-514 8616091 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jeong, JH, Kim, KT, Kim, DJ & Lee, SW 2018, Decoding of Multi-directional Reaching Movements for EEG-Based Robot Arm Control. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616091, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 511-514, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, 18/10/7. https://doi.org/10.1109/SMC.2018.00096

Jeong JH, Kim KT, Kim DJ , Lee SW. Decoding of Multi-directional Reaching Movements for EEG-Based Robot Arm Control. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 511-514. 8616091. (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). doi: 10.1109/SMC.2018.00096

Jeong, Ji Hoon ; Kim, Keun Tae ; Kim, Dong Ju et al. / Decoding of Multi-directional Reaching Movements for EEG-Based Robot Arm Control. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 511-514 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

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abstract = "This paper presents the feasibility of an electroencephalography (EEG)-based robot arm control system using a decoding of multi-directional arm reaching movement imagery. To do that, we have designed and implemented an experimental environment that can acquire non-invasive brain signals about multi-directional arm reaching movement. Five subjects participated in our experiments and the subjects performed four directional reaching tasks (Left, right, forward, and backward) with actual movement and movement imagery. The filter-bank common spatial pattern (FBCSP) was applied to extract spatio-frequency features from the acquired EEG signals. The regularized linear discriminant analysis (RLDA) was also applied as a classifier. As a result, the averaged classification accuracies of the actual movement and movement imagery were represented 67.04% and 59.19%, respectively. These results showed a feasibility of the EEG-based robot arm control system based on multi-directional arm reaching movement imagery.",

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Decoding of Multi-directional Reaching Movements for EEG-Based Robot Arm Control

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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