A Novel Approach to Classify Natural Grasp Actions by Estimating Muscle Activity Patterns from EEG Signals

Jeong Hyun Cho; Ji Roon Jeong; Dong Joo Kim; Seong Whan Lee

doi:10.1109/BCI48061.2020.9061627

A Novel Approach to Classify Natural Grasp Actions by Estimating Muscle Activity Patterns from EEG Signals

Jeong Hyun Cho, Ji Roon Jeong, Dong Joo Kim, Seong Whan Lee

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

14 Citations (Scopus)

Abstract

Developing electroencephalogram (EEG) based brain-computer interface (BCI) systems is challenging. In this study, we analyzed natural grasp actions from EEG. Ten healthy subjects participated in this experiment. They executed and imagined three sustained grasp actions. We proposed a novel approach which estimates muscle activity patterns from EEG signals to improve the overall classification accuracy. For implementing, we have recorded EEG and electromyogram (EMG) simultaneously. Using the similarity of the estimated pattern from EEG signals compare to the activity pattern from EMG signals showed higher classification accuracy than competitive methods. As a result, we obtained the average classification accuracy of 63.89±7.54% for actual movement and 46.96±15.30% for motor imagery. These are 21.59% and 5.66% higher than the result of the competitive model, respectively. This result is encouraging, and the proposed method could potentially be used in future applications, such as a BCI-driven robot control for handling various daily use objects.

Original language	English
Title of host publication	8th International Winter Conference on Brain-Computer Interface, BCI 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728147079
ISBN (Print)	9781728147079
DOIs	https://doi.org/10.1109/BCI48061.2020.9061627
Publication status	Published - 2020 Feb 1
Event	8th International Winter Conference on Brain-Computer Interface, BCI 2020 - Gangwon, Korea, Republic of Duration: 2020 Feb 26 → 2020 Feb 28

Publication series

Name	8th International Winter Conference on Brain-Computer Interface, BCI 2020

Conference

Conference	8th International Winter Conference on Brain-Computer Interface, BCI 2020
Country/Territory	Korea, Republic of
City	Gangwon
Period	20/2/26 → 20/2/28

Bibliographical note

Funding Information:
This research was partly supported by an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant, funded by the Korean government (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 an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning).

Funding Information:
This research was partly supported by an Institute of Information and Communications Technology Planning & Evaluation (IITP) grant, funded by the Korean government (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 an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User's Intentions using Deep Learning).

Publisher Copyright:
© 2020 IEEE.

Keywords

brain-computer interface
electroencephalogram
electromyogram
hand grasping motion
motor imagery
robotic arm

ASJC Scopus subject areas

Behavioral Neuroscience
Cognitive Neuroscience
Artificial Intelligence
Human-Computer Interaction

Access to Document

10.1109/BCI48061.2020.9061627

Cite this

Cho, J. H., Jeong, J. R., Kim, D. J., & Lee, S. W. (2020). A Novel Approach to Classify Natural Grasp Actions by Estimating Muscle Activity Patterns from EEG Signals. In 8th International Winter Conference on Brain-Computer Interface, BCI 2020 Article 9061627 (8th International Winter Conference on Brain-Computer Interface, BCI 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BCI48061.2020.9061627

A Novel Approach to Classify Natural Grasp Actions by Estimating Muscle Activity Patterns from EEG Signals. / Cho, Jeong Hyun; Jeong, Ji Roon; Kim, Dong Joo et al.
8th International Winter Conference on Brain-Computer Interface, BCI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9061627 (8th International Winter Conference on Brain-Computer Interface, BCI 2020).

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

Cho, JH, Jeong, JR, Kim, DJ & Lee, SW 2020, A Novel Approach to Classify Natural Grasp Actions by Estimating Muscle Activity Patterns from EEG Signals. in 8th International Winter Conference on Brain-Computer Interface, BCI 2020., 9061627, 8th International Winter Conference on Brain-Computer Interface, BCI 2020, Institute of Electrical and Electronics Engineers Inc., 8th International Winter Conference on Brain-Computer Interface, BCI 2020, Gangwon, Korea, Republic of, 20/2/26. https://doi.org/10.1109/BCI48061.2020.9061627

Cho JH, Jeong JR, Kim DJ , Lee SW. A Novel Approach to Classify Natural Grasp Actions by Estimating Muscle Activity Patterns from EEG Signals. In 8th International Winter Conference on Brain-Computer Interface, BCI 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9061627. (8th International Winter Conference on Brain-Computer Interface, BCI 2020). doi: 10.1109/BCI48061.2020.9061627

Cho, Jeong Hyun ; Jeong, Ji Roon ; Kim, Dong Joo et al. / A Novel Approach to Classify Natural Grasp Actions by Estimating Muscle Activity Patterns from EEG Signals. 8th International Winter Conference on Brain-Computer Interface, BCI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (8th International Winter Conference on Brain-Computer Interface, BCI 2020).

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abstract = "Developing electroencephalogram (EEG) based brain-computer interface (BCI) systems is challenging. In this study, we analyzed natural grasp actions from EEG. Ten healthy subjects participated in this experiment. They executed and imagined three sustained grasp actions. We proposed a novel approach which estimates muscle activity patterns from EEG signals to improve the overall classification accuracy. For implementing, we have recorded EEG and electromyogram (EMG) simultaneously. Using the similarity of the estimated pattern from EEG signals compare to the activity pattern from EMG signals showed higher classification accuracy than competitive methods. As a result, we obtained the average classification accuracy of 63.89±7.54% for actual movement and 46.96±15.30% for motor imagery. These are 21.59% and 5.66% higher than the result of the competitive model, respectively. This result is encouraging, and the proposed method could potentially be used in future applications, such as a BCI-driven robot control for handling various daily use objects.",

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note = "Funding Information: This research was partly supported by an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant, funded by the Korean government (No. 2017-0-00432, Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User{\textquoteright}s Thought via AR/VR Interface) and partly funded by an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User{\textquoteright}s Intentions using Deep Learning). Funding Information: This research was partly supported by an Institute of Information and Communications Technology Planning & Evaluation (IITP) grant, funded by the Korean government (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 an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User's Intentions using Deep Learning). Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th International Winter Conference on Brain-Computer Interface, BCI 2020 ; Conference date: 26-02-2020 Through 28-02-2020",

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N1 - Funding Information: This research was partly supported by an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant, funded by the Korean government (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 an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning). Funding Information: This research was partly supported by an Institute of Information and Communications Technology Planning & Evaluation (IITP) grant, funded by the Korean government (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 an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User's Intentions using Deep Learning). Publisher Copyright: © 2020 IEEE.

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N2 - Developing electroencephalogram (EEG) based brain-computer interface (BCI) systems is challenging. In this study, we analyzed natural grasp actions from EEG. Ten healthy subjects participated in this experiment. They executed and imagined three sustained grasp actions. We proposed a novel approach which estimates muscle activity patterns from EEG signals to improve the overall classification accuracy. For implementing, we have recorded EEG and electromyogram (EMG) simultaneously. Using the similarity of the estimated pattern from EEG signals compare to the activity pattern from EMG signals showed higher classification accuracy than competitive methods. As a result, we obtained the average classification accuracy of 63.89±7.54% for actual movement and 46.96±15.30% for motor imagery. These are 21.59% and 5.66% higher than the result of the competitive model, respectively. This result is encouraging, and the proposed method could potentially be used in future applications, such as a BCI-driven robot control for handling various daily use objects.

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A Novel Approach to Classify Natural Grasp Actions by Estimating Muscle Activity Patterns from EEG Signals

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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