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