TY - GEN
T1 - Complex Motor Imagery-based Brain-Computer Interface System
T2 - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
AU - Lee, Seung Bo
AU - Jung, Min Kyung
AU - Kim, Hakseung
AU - Lee, Seong Whan
AU - Kim, Dong Joo
N1 - Funding Information:
and ICT, MSIT) under Grant 2019R1A2C1003399, and in part by Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT, MSIT) under Grant NRF-2020R1C1C1006773.
Funding Information:
This research was supported in part 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), in part by Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/11
Y1 - 2020/10/11
N2 - Motor imagery (MI) classification is important as the emerging research interest of brain computer interface (BCI) due to its potential about real-world application. Advancing manipulation and control technology of external devices such as robotics, the need of MI for complex and human-like movements is growing. The two most important procedures that influence the performance of MI-BCI are feature extraction and classification. Although there have been recent studies on feature extraction for complex, there is no consensus on the classifier suitable for complex MI. This study aimed to identify the best classifier for complex MI decoding.Electroencephalography (EEG) recordings measured during complex MI, which are hand grasping, spreading, pronation and supination, were used for binary (grasp vs. twist) and quaternary classification. Time domain parameter, which have shown suitability for complex movement decoding in previous works, was used as the EEG feature. Four types of ten machine learning classifiers, which have been applied to MI-BCI, were compared.Shrinkage regularized linear discriminant analysis (SRLDA) exhibited the best classification accuracy in both binary (92.8%) and quaternary (55.2%). In the case of training and testing time, a small amount of time for real-time analysis were needed, except random forest and logistic regression.This study showed that SRLDA is an appropriate classifier for complex MI classification, due to its ability to handle stationary and high dimensionality feature, TDP. The findings suggest that complex MI-BCI could gain more benefit from applying linear and shrinkage regularized model (i.e., SRLDA).
AB - Motor imagery (MI) classification is important as the emerging research interest of brain computer interface (BCI) due to its potential about real-world application. Advancing manipulation and control technology of external devices such as robotics, the need of MI for complex and human-like movements is growing. The two most important procedures that influence the performance of MI-BCI are feature extraction and classification. Although there have been recent studies on feature extraction for complex, there is no consensus on the classifier suitable for complex MI. This study aimed to identify the best classifier for complex MI decoding.Electroencephalography (EEG) recordings measured during complex MI, which are hand grasping, spreading, pronation and supination, were used for binary (grasp vs. twist) and quaternary classification. Time domain parameter, which have shown suitability for complex movement decoding in previous works, was used as the EEG feature. Four types of ten machine learning classifiers, which have been applied to MI-BCI, were compared.Shrinkage regularized linear discriminant analysis (SRLDA) exhibited the best classification accuracy in both binary (92.8%) and quaternary (55.2%). In the case of training and testing time, a small amount of time for real-time analysis were needed, except random forest and logistic regression.This study showed that SRLDA is an appropriate classifier for complex MI classification, due to its ability to handle stationary and high dimensionality feature, TDP. The findings suggest that complex MI-BCI could gain more benefit from applying linear and shrinkage regularized model (i.e., SRLDA).
UR - http://www.scopus.com/inward/record.url?scp=85098889798&partnerID=8YFLogxK
U2 - 10.1109/SMC42975.2020.9282984
DO - 10.1109/SMC42975.2020.9282984
M3 - Conference contribution
AN - SCOPUS:85098889798
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 2496
EP - 2501
BT - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 October 2020 through 14 October 2020
ER -