Semi-autonomous Navigation Control of Intelligent Wheelchair Based on Steady State Visual Evoked Potential

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.