Abstract
Motor imagery-based brain-computer interface (BCI) has been widely used to translate user's motor intentions in BCI applications. In general, experiment trial of motor imagery task is repeated to improve the accuracy of the motor imagery-based BCI application, but it is not well known whether the accuracy would converge from a certain number of trial repetition. This study identified that how many trials are required in the classification model for motor imagery task with hand-grasping to show reliable classification performance. Five participants equipped with an electroencephalography device were enrolled, and they were requested to perform the motor imagery tasks with hand-grasping and unfolding. Trials were classified into hand-grasping, unfolding and resting. We observed that the classification performance is converged when more than 40 trials are used in the model. This finding could be utilized to develop reliable motor imagery-based BCI application with increasing the efficiency of the experiment.
| Original language | English |
|---|---|
| Title of host publication | 7th International Winter Conference on Brain-Computer Interface, BCI 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781538681169 |
| DOIs | |
| Publication status | Published - 2019 Feb 1 |
| Event | 7th International Winter Conference on Brain-Computer Interface, BCI 2019 - Gangwon, Korea, Republic of Duration: 2019 Feb 18 → 2019 Feb 20 |
Publication series
| Name | 7th International Winter Conference on Brain-Computer Interface, BCI 2019 |
|---|
Conference
| Conference | 7th International Winter Conference on Brain-Computer Interface, BCI 2019 |
|---|---|
| Country | Korea, Republic of |
| City | Gangwon |
| Period | 19/2/18 → 19/2/20 |
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Keywords
- brain computer interface
- hand-grasping
- motor imagery task
- performance convergence
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Signal Processing
- Neuroscience (miscellaneous)
Cite this
Exploring the Number of Repetitions in Trials for the Performance Convergence of Classification in Motor Imagery Task with Hand-Grasping. / Kim, Young Tak; Lee, Seung Bo; Kim, Hakseung; Jeong, Ji Hoon; Lee, Seong Whan; Kim, Dong-Joo.
7th International Winter Conference on Brain-Computer Interface, BCI 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8737308 (7th International Winter Conference on Brain-Computer Interface, BCI 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Exploring the Number of Repetitions in Trials for the Performance Convergence of Classification in Motor Imagery Task with Hand-Grasping
AU - Kim, Young Tak
AU - Lee, Seung Bo
AU - Kim, Hakseung
AU - Jeong, Ji Hoon
AU - Lee, Seong Whan
AU - Kim, Dong-Joo
PY - 2019/2/1
Y1 - 2019/2/1
N2 - Motor imagery-based brain-computer interface (BCI) has been widely used to translate user's motor intentions in BCI applications. In general, experiment trial of motor imagery task is repeated to improve the accuracy of the motor imagery-based BCI application, but it is not well known whether the accuracy would converge from a certain number of trial repetition. This study identified that how many trials are required in the classification model for motor imagery task with hand-grasping to show reliable classification performance. Five participants equipped with an electroencephalography device were enrolled, and they were requested to perform the motor imagery tasks with hand-grasping and unfolding. Trials were classified into hand-grasping, unfolding and resting. We observed that the classification performance is converged when more than 40 trials are used in the model. This finding could be utilized to develop reliable motor imagery-based BCI application with increasing the efficiency of the experiment.
AB - Motor imagery-based brain-computer interface (BCI) has been widely used to translate user's motor intentions in BCI applications. In general, experiment trial of motor imagery task is repeated to improve the accuracy of the motor imagery-based BCI application, but it is not well known whether the accuracy would converge from a certain number of trial repetition. This study identified that how many trials are required in the classification model for motor imagery task with hand-grasping to show reliable classification performance. Five participants equipped with an electroencephalography device were enrolled, and they were requested to perform the motor imagery tasks with hand-grasping and unfolding. Trials were classified into hand-grasping, unfolding and resting. We observed that the classification performance is converged when more than 40 trials are used in the model. This finding could be utilized to develop reliable motor imagery-based BCI application with increasing the efficiency of the experiment.
KW - brain computer interface
KW - hand-grasping
KW - motor imagery task
KW - performance convergence
UR - http://www.scopus.com/inward/record.url?scp=85068330347&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068330347&partnerID=8YFLogxK
U2 - 10.1109/IWW-BCI.2019.8737308
DO - 10.1109/IWW-BCI.2019.8737308
M3 - Conference contribution
T3 - 7th International Winter Conference on Brain-Computer Interface, BCI 2019
BT - 7th International Winter Conference on Brain-Computer Interface, BCI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
ER -