Abstract
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.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant No. 51675389) and the Excellent Dissertation Cultivation Funds of Wuhan University of Technology (2018-YS-053). It is also supported by the Fundamental Research Funds for the Central Universities (WUT: 203109001).
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Zhao, X. et al. (2021). Multiple Action Movement Control Scheme for Assistive Robot Based on Binary Motor Imagery EEG. In: Liang, Q., Wang, W., Liu, X., Na, Z., Li, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2020. Lecture Notes in Electrical Engineering, vol 654. Springer, Singapore. https://doi.org/10.1007/978-981-15-8411-4_101
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