TY - GEN
T1 - A Novel Approach to Classify Natural Grasp Actions by Estimating Muscle Activity Patterns from EEG Signals
AU - Cho, Jeong Hyun
AU - Jeong, Ji Roon
AU - Kim, Dong Joo
AU - Lee, Seong Whan
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.
PY - 2020/2/1
Y1 - 2020/2/1
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.
AB - 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.
KW - brain-computer interface
KW - electroencephalogram
KW - electromyogram
KW - hand grasping motion
KW - motor imagery
KW - robotic arm
UR - http://www.scopus.com/inward/record.url?scp=85084034002&partnerID=8YFLogxK
U2 - 10.1109/BCI48061.2020.9061627
DO - 10.1109/BCI48061.2020.9061627
M3 - Conference contribution
AN - SCOPUS:85084034002
SN - 9781728147079
T3 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
BT - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
Y2 - 26 February 2020 through 28 February 2020
ER -