TY - GEN
T1 - Multiple Action Movement Control Scheme for Assistive Robot Based on Binary Motor Imagery EEG
AU - Zhao, Xuefei
AU - Liu, Dong
AU - Xie, Shengquan
AU - Liu, Quan
AU - Chen, Kun
AU - Ma, Li
AU - Ai, Qingsong
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - In this paper, a weighted voting system combined with basic signal processing methods is used to classify multi-category motor imagery (MI) scenarios (foot, left-hand, right-hand, tongue) to improve the classification accuracy of MI electroencephalogram (EEG) signal. Meanwhile, a feasible binary coding framework is proposed to control the KUKA robotic arm for grasping to improve online performance of applications on brain–computer interfaces (BCIs). Firstly, two-movement MI with the high classification accuracy is selected from four-action types, i.e., foot as 0, left-hand as 1, and their combination representing the four directions of motion direction of the robotic arm (e.g., 00-front, 01-back, 10-left, 11-right) is generated by two-bit binary coding. Next, the motion of the robotic arm in each direction is achieved by two successive movements of MI. Finally, the accuracy of our integrated classifier reaches 74.6% in four-movement MI data and 92.6% in two-movement MI data. Compared to four-movement MI to control the robotic arm, the binary coding method reduces the time by 6.8% and increases the accuracy more than two times.
AB - In this paper, a weighted voting system combined with basic signal processing methods is used to classify multi-category motor imagery (MI) scenarios (foot, left-hand, right-hand, tongue) to improve the classification accuracy of MI electroencephalogram (EEG) signal. Meanwhile, a feasible binary coding framework is proposed to control the KUKA robotic arm for grasping to improve online performance of applications on brain–computer interfaces (BCIs). Firstly, two-movement MI with the high classification accuracy is selected from four-action types, i.e., foot as 0, left-hand as 1, and their combination representing the four directions of motion direction of the robotic arm (e.g., 00-front, 01-back, 10-left, 11-right) is generated by two-bit binary coding. Next, the motion of the robotic arm in each direction is achieved by two successive movements of MI. Finally, the accuracy of our integrated classifier reaches 74.6% in four-movement MI data and 92.6% in two-movement MI data. Compared to four-movement MI to control the robotic arm, the binary coding method reduces the time by 6.8% and increases the accuracy more than two times.
KW - Binary coding
KW - Brain–computer interface (BCI)
KW - Electroencephalogram (EEG) signal
KW - KUKA robotic arm
KW - Motor imagery (MI)
UR - https://www.scopus.com/pages/publications/85111351557
U2 - 10.1007/978-981-15-8411-4_101
DO - 10.1007/978-981-15-8411-4_101
M3 - 会议稿件
AN - SCOPUS:85111351557
SN - 9789811584107
T3 - Lecture Notes in Electrical Engineering
SP - 760
EP - 768
BT - Communications, Signal Processing, and Systems - Proceedings of the 9th International Conference on Communications, Signal Processing, and Systems
A2 - Liang, Qilian
A2 - Wang, Wei
A2 - Liu, Xin
A2 - Na, Zhenyu
A2 - Li, Xiaoxia
A2 - Zhang, Baoju
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020
Y2 - 4 July 2020 through 5 July 2020
ER -