TY - GEN
T1 - Classifying Motor Preparation and Execution of the Left and the Right Lower Limb Using Brain Network Features
AU - Su, Kai Hsiang
AU - Phang, Chun Ren
AU - Ko, Li Wei
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Upper limb motor-related cortical activity (MRCA) has been widely developed and applied in the BCI technology, but the lower limb MRCA is difficult to classify or transfer to control command due to the close anatomical distance of the motor area in human cortex. In previous studies, the classification system based on the cortical functional connectivity features has been proven to be able to classify the lower limb motor imagery recorded from clinical-grade EEG system. The aim of this study is to use the brain network features to distinguish lower limb MRCA that can be potentially applied to reality in real time. The movement-related cortex areas of the lower limbs are very close, making it difficult to classify using spatial features. By using the Pearson correlation to calculate the connectivity strength between EEG signals as brain network features, and in combination with linear SVM for classification, three subjects can be trained for less than twenty minutes and achieve an average accuracy of 77.92%. In offline testing, the average accuracy of cross-validation also reached 75.31%. Our research proves that the classification system constructed by the cortical functional connectivity features can operate in a real environment and has promising classification accuracy. This shows the potential application of our system for lower limb rehabilitation neural feedback system or lower limb BCI-based robotic control device.
AB - Upper limb motor-related cortical activity (MRCA) has been widely developed and applied in the BCI technology, but the lower limb MRCA is difficult to classify or transfer to control command due to the close anatomical distance of the motor area in human cortex. In previous studies, the classification system based on the cortical functional connectivity features has been proven to be able to classify the lower limb motor imagery recorded from clinical-grade EEG system. The aim of this study is to use the brain network features to distinguish lower limb MRCA that can be potentially applied to reality in real time. The movement-related cortex areas of the lower limbs are very close, making it difficult to classify using spatial features. By using the Pearson correlation to calculate the connectivity strength between EEG signals as brain network features, and in combination with linear SVM for classification, three subjects can be trained for less than twenty minutes and achieve an average accuracy of 77.92%. In offline testing, the average accuracy of cross-validation also reached 75.31%. Our research proves that the classification system constructed by the cortical functional connectivity features can operate in a real environment and has promising classification accuracy. This shows the potential application of our system for lower limb rehabilitation neural feedback system or lower limb BCI-based robotic control device.
UR - http://www.scopus.com/inward/record.url?scp=85123915398&partnerID=8YFLogxK
U2 - 10.1109/CACS52606.2021.9638707
DO - 10.1109/CACS52606.2021.9638707
M3 - Conference contribution
AN - SCOPUS:85123915398
T3 - 2021 International Automatic Control Conference, CACS 2021
BT - 2021 International Automatic Control Conference, CACS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Automatic Control Conference, CACS 2021
Y2 - 3 November 2021 through 6 November 2021
ER -