Semi-autonomous Navigation Control of Intelligent Wheelchair Based on Steady State Visual Evoked Potential
ZHANG Yahui1, WANG Fei2, LI Jinghong2, LIU Yuqiang2, WU Shichao2
1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;
2. Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China
张亚徽, 王斐, 李景宏, 刘玉强, 吴仕超. 基于稳态视觉诱发电位的智能轮椅半自主导航控制[J]. 机器人, 2019, 41(5): 620-627,636.DOI: 10.13973/j.cnki.robot.180771.
ZHANG Yahui, WANG Fei, LI Jinghong, LIU Yuqiang, WU Shichao. Semi-autonomous Navigation Control of Intelligent Wheelchair Based on Steady State Visual Evoked Potential. ROBOT, 2019, 41(5): 620-627,636. DOI: 10.13973/j.cnki.robot.180771.
Abstract:Current brain-computer interface (BCI) control based intelligent wheelchairs are facing the problem of users' fatigue caused by uncoordinated interaction, low recognition accuracy, low execution efficiency. A method of human-machine collaborative intelligent control is proposed to solve the problem, and a semi-autonomous navigation control system based on BCI and hierarchical map is designed and implemented for intelligent wheelchair. Firstly, a three-level raster-topology-intention map is constructed according to actual needs. Then, a one dimensional convolutional neural network (1D-CNN) based on canonical correlation analysis (CCA) is used to identify and classify the human's intention, which is sent to the navigation control section through BCI system. Finally, the control command is given to control the navigation of the intelligent wheelchair by fusion decision. The experimental results show that the proposed method achieves high recognition accuracies on electroencephalography (EEG) signals with an average of 91.576%, and the control system demonstrates a good stability. The method can flexibly control the motion direction of intelligent wheelchair and reach the target location without collision according to the human's control intention.
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