TY - GEN
T1 - A Training System for Brain-Computer Interfaces Based on Motor Imagery Selection
AU - Koizumi, Yuki
AU - Shibanoki, Taro
AU - Tsuji, Toshio
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Number 17K12723. The experiment was conducted with the approval of the Bioethics Committee of the Faculty of Engineering, Ibaraki University (permit number: 18T0300).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/3
Y1 - 2020/3
N2 - This paper proposes a BCI training system based on motor imagery selection. The system selects discriminable motor imagery tasks based on the partial KL information theory and provides related feedback training. Results from training experiments showed a gradual increase in repeatability for all subjects in selected motor imagery tasks.
AB - This paper proposes a BCI training system based on motor imagery selection. The system selects discriminable motor imagery tasks based on the partial KL information theory and provides related feedback training. Results from training experiments showed a gradual increase in repeatability for all subjects in selected motor imagery tasks.
KW - brain-computer interface (BCI)
KW - class selection
KW - electroencephalogram (EEG)
KW - Kullback-Leibler divergence
KW - motor imagery
KW - training system
UR - http://www.scopus.com/inward/record.url?scp=85085162982&partnerID=8YFLogxK
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U2 - 10.1109/LifeTech48969.2020.1570620341
DO - 10.1109/LifeTech48969.2020.1570620341
M3 - Conference contribution
AN - SCOPUS:85085162982
T3 - LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
SP - 217
EP - 218
BT - LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2020
Y2 - 10 March 2020 through 12 March 2020
ER -