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 proceedingConference contribution

2 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 languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages515-518
Number of pages4
ISBN (Electronic)9781538666500
DOIs
Publication statusPublished - 2019 Jan 16
Event2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan
Duration: 2018 Oct 72018 Oct 10

Publication series

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

Conference

Conference2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
CountryJapan
CityMiyazaki
Period18/10/718/10/10

Fingerprint

Brain-Computer Interfaces
Brain computer interface
End effectors
Electroencephalography
Hand
Robots
Imagery (Psychotherapy)
Discriminant analysis
Patient rehabilitation
Computer Systems
Decoding
Discriminant Analysis
Experiments
Healthy Volunteers
Rehabilitation
Robot
Equipment and Supplies
Imagery
Experiment

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

Cite this

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 proceedingConference 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).
@inproceedings{63442d17339d43bb8dba4c2460b17086,
title = "Classification of Hand Motions within EEG Signals for Non-Invasive BCI-Based Robot Hand Control",
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.",
keywords = "(EEG), a robot hand, brain-computer interface (BCI), electroencephalography, motor execution (ME), motor imagery (MI)",
author = "Cho, {Jeong Hyun} and Jeong, {Ji Hoon} and Shim, {Kyung Hwan} and Kim, {Dong Ju} and Lee, {Seong Whan}",
year = "2019",
month = "1",
day = "16",
doi = "10.1109/SMC.2018.00097",
language = "English",
series = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "515--518",
booktitle = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",

}

TY - GEN

T1 - Classification of Hand Motions within EEG Signals for Non-Invasive BCI-Based Robot Hand Control

AU - Cho, Jeong Hyun

AU - Jeong, Ji Hoon

AU - Shim, Kyung Hwan

AU - Kim, Dong Ju

AU - Lee, Seong Whan

PY - 2019/1/16

Y1 - 2019/1/16

N2 - 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.

AB - 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.

KW - (EEG)

KW - a robot hand

KW - brain-computer interface (BCI)

KW - electroencephalography

KW - motor execution (ME)

KW - motor imagery (MI)

UR - http://www.scopus.com/inward/record.url?scp=85062224023&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062224023&partnerID=8YFLogxK

U2 - 10.1109/SMC.2018.00097

DO - 10.1109/SMC.2018.00097

M3 - Conference contribution

AN - SCOPUS:85062224023

T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

SP - 515

EP - 518

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

PB - Institute of Electrical and Electronics Engineers Inc.

ER -