TY - GEN
T1 - Decoding of Multi-directional Reaching Movements for EEG-Based Robot Arm Control
AU - Jeong, Ji Hoon
AU - Kim, Keun Tae
AU - Kim, Dong Ju
AU - Lee, Seong Whan
N1 - Funding Information:
This work was partly supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (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 Microsoft Research Asia.
PY - 2019/1/16
Y1 - 2019/1/16
N2 - This paper presents the feasibility of an electroencephalography (EEG)-based robot arm control system using a decoding of multi-directional arm reaching movement imagery. To do that, we have designed and implemented an experimental environment that can acquire non-invasive brain signals about multi-directional arm reaching movement. Five subjects participated in our experiments and the subjects performed four directional reaching tasks (Left, right, forward, and backward) with actual movement and movement imagery. The filter-bank common spatial pattern (FBCSP) was applied to extract spatio-frequency features from the acquired EEG signals. The regularized linear discriminant analysis (RLDA) was also applied as a classifier. As a result, the averaged classification accuracies of the actual movement and movement imagery were represented 67.04% and 59.19%, respectively. These results showed a feasibility of the EEG-based robot arm control system based on multi-directional arm reaching movement imagery.
AB - This paper presents the feasibility of an electroencephalography (EEG)-based robot arm control system using a decoding of multi-directional arm reaching movement imagery. To do that, we have designed and implemented an experimental environment that can acquire non-invasive brain signals about multi-directional arm reaching movement. Five subjects participated in our experiments and the subjects performed four directional reaching tasks (Left, right, forward, and backward) with actual movement and movement imagery. The filter-bank common spatial pattern (FBCSP) was applied to extract spatio-frequency features from the acquired EEG signals. The regularized linear discriminant analysis (RLDA) was also applied as a classifier. As a result, the averaged classification accuracies of the actual movement and movement imagery were represented 67.04% and 59.19%, respectively. These results showed a feasibility of the EEG-based robot arm control system based on multi-directional arm reaching movement imagery.
KW - a robot arm control
KW - brain-machine interface
KW - electroencephalography
KW - muti-directional arm reaching movement
UR - http://www.scopus.com/inward/record.url?scp=85062213086&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062213086&partnerID=8YFLogxK
U2 - 10.1109/SMC.2018.00096
DO - 10.1109/SMC.2018.00096
M3 - Conference contribution
AN - SCOPUS:85062213086
T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
SP - 511
EP - 514
BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Y2 - 7 October 2018 through 10 October 2018
ER -
