Classification of Hand Motions within EEG Signals for Non-Invasive BCI-Based Robot Hand Control

Jeong Hyun Cho; Ji Hoon Jeong; Kyung Hwan Shim; Dong Ju Kim; Seong Whan Lee

doi:10.1109/SMC.2018.00097

Classification of Hand Motions within EEG Signals for Non-Invasive BCI-Based Robot Hand Control

Jeong Hyun Cho, Ji Hoon Jeong, Kyung Hwan Shim, Dong Ju Kim, Seong Whan Lee

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

9 Citations (Scopus)

Abstract

The development of brain-computer interface (BCI) systems that are based on electroencephalography (EEG), and driven by spontaneous movement intentions, is useful for rehabilitation and external device control. In this study, we analyzed the decoding of five different hand executions and imageries from EEG signals, for a robot hand control. Five healthy subjects participated in this experiment. They executed and imagined five sustained hand motions. In this motor execution (ME) and motor imagery (MI) experiment, we proposed a subject-specific time interval selection method, and we used common spatial patterns (CSP) and the regularized linear discriminant analysis (RLDA) for the data analysis. As a result, we classified the five different hand motions offline and obtained average classification accuracies of 56.83% for ME, and 51.01% for MI, respectively. Both results were higher than the obtained accuracies from a comparison method that used a standard fixed time interval method. This result is encouraging, and the proposed method could potentially be used in future applications, such as a BCI-driven robot hand control.

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	515-518
Number of pages	4
ISBN (Electronic)	9781538666500
DOIs	https://doi.org/10.1109/SMC.2018.00097
Publication status	Published - 2019 Jan 16
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	Japan
City	Miyazaki
Period	18/10/7 → 18/10/10

Keywords

(EEG)
a robot hand
brain-computer interface (BCI)
electroencephalography
motor execution (ME)
motor imagery (MI)

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 Sustainable Development Goal(s)

Access to Document

10.1109/SMC.2018.00097

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Cho, J. H., Jeong, J. H., Shim, K. H., Kim, D. J., & Lee, S. W. (2019). Classification of Hand Motions within EEG Signals for Non-Invasive BCI-Based Robot Hand Control. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 515-518). [8616092] (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.2018.00097

Classification of Hand Motions within EEG Signals for Non-Invasive BCI-Based Robot Hand Control. / Cho, Jeong Hyun; Jeong, Ji Hoon; Shim, Kyung Hwan; Kim, Dong Ju ; Lee, Seong Whan.

Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 515-518 8616092 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

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

Cho, JH, Jeong, JH, Shim, KH, Kim, DJ & Lee, SW 2019, Classification of Hand Motions within EEG Signals for Non-Invasive BCI-Based Robot Hand Control. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616092, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 515-518, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, 18/10/7. https://doi.org/10.1109/SMC.2018.00097

Cho JH, Jeong JH, Shim KH, Kim DJ , Lee SW. Classification of Hand Motions within EEG Signals for Non-Invasive BCI-Based Robot Hand Control. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 515-518. 8616092. (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). https://doi.org/10.1109/SMC.2018.00097

Cho, Jeong Hyun ; Jeong, Ji Hoon ; Shim, Kyung Hwan ; Kim, Dong Ju ; Lee, Seong Whan. / Classification of Hand Motions within EEG Signals for Non-Invasive BCI-Based Robot Hand Control. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 515-518 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

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abstract = "The development of brain-computer interface (BCI) systems that are based on electroencephalography (EEG), and driven by spontaneous movement intentions, is useful for rehabilitation and external device control. In this study, we analyzed the decoding of five different hand executions and imageries from EEG signals, for a robot hand control. Five healthy subjects participated in this experiment. They executed and imagined five sustained hand motions. In this motor execution (ME) and motor imagery (MI) experiment, we proposed a subject-specific time interval selection method, and we used common spatial patterns (CSP) and the regularized linear discriminant analysis (RLDA) for the data analysis. As a result, we classified the five different hand motions offline and obtained average classification accuracies of 56.83% for ME, and 51.01% for MI, respectively. Both results were higher than the obtained accuracies from a comparison method that used a standard fixed time interval method. This result is encouraging, and the proposed method could potentially be used in future applications, such as a BCI-driven robot hand control.",

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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 Agency for Defense Development (ADD) of Korea (06-201-305-001, A Study on Human-Computer Interaction Technology for the Pilot Status Recognition).; 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 ; Conference date: 07-10-2018 Through 10-10-2018",

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Classification of Hand Motions within EEG Signals for Non-Invasive BCI-Based Robot Hand Control

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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