Abstract
A brain-machine interface (BMI) based on electroencephalography (EEG) can overcome the movement deficits for patients and real-world applications for healthy people. Ideally, the BMI system detects user movement intentions transforms them into a control signal for a robotic arm movement. In this study, we made progress toward user intention decoding and successfully classified six different reaching movements of the right arm in the movement execution (ME). Notably, we designed an experimental environment using robotic arm movement and proposed a convolutional neural network architecture (CNN) with inception block for robust classify executed movements of the same limb. As a result, we confirmed the classification accuracies of six different directions show 0.45 for the executed session. The results proved that the proposed architecture has approximately 613% performance increase compared to its conventional classification models. Hence, we demonstrate the 3D inception CNN architecture to contribute to the continuous decoding of ME.
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
Publisher Copyright:© 2020 IEEE.
Keywords
- brain-machine interface
- deep learning
- electroencephalogram
- movement execution
- robotic arm
ASJC Scopus subject areas
- Behavioral Neuroscience
- Cognitive Neuroscience
- Artificial Intelligence
- Human-Computer Interaction