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 proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publication8th International Winter Conference on Brain-Computer Interface, BCI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728147079
ISBN (Print)9781728147079
DOIs
Publication statusPublished - 2020 Feb 1
Event8th International Winter Conference on Brain-Computer Interface, BCI 2020 - Gangwon, Korea, Republic of
Duration: 2020 Feb 262020 Feb 28

Publication series

Name8th International Winter Conference on Brain-Computer Interface, BCI 2020

Conference

Conference8th International Winter Conference on Brain-Computer Interface, BCI 2020
CountryKorea, Republic of
CityGangwon
Period20/2/2620/2/28

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

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  • 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 [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