NAO Robot Limb Control Method Based on Motor Imagery EEG

Brain computer interface (BCI) technology refers to the technology that helps establish direct communication and control channels between the human brain and the computer. This technology can meet the control requirements of external equipment without passing through the patient's damaged nerves and declining muscle system. Electroencephalogram (EEG) contains a variety of information, including motor imagination information, steady-state visually evoked EEG information, emotional information, and fatigue information, etc. This paper designs a brain-computer interface system based on motor imagination and uses it to control the body movements of the NAO robot. The system is mainly divided into three modules: signal acquisition, signal processing and robot control. In terms of signal acquisition, electrode caps, conductive glue, EEG amplifiers and Curry7 software are used. In this paper, we design a frequency band feature selection algorithm based on an improved sub-band filter bank (FB)-Common Space Pattern (CSP). These features are classified using Bagging ensemble classification algorithm and converted to use Commands for controlling the NAO robot. In terms of communication control, the instruction codes are preset, and the classification results are corresponding to instructions for controlling different actions of the robot. It is transmitted to the robot by using wireless communication technology. The Bagging ensemble classification method proposed in this paper classifies EEG features. The accuracy of the four types of motor imaging EEG classification is as high as 78.29%, and the accuracy of NAO robot action execution experiments is up to 86.33%, which proves the correctness of the algorithm studied in this paper. The reliability and feasibility of the NAO robot brain control system designed in this paper.