| This paper conducts a study on the recognition method of Motor imagination(MI)Electroencephalogram(EEG)and the control of bionic manipulator,aiming at realizing the interaction between brain and bionic manipulator.The main results of the study are as follows:(1)Study on feature extraction method of motor imagination EEG signal.To solve the problem that EEG amplitude is weak and susceptible to interference,thus affecting the performance of brain-computer interface system,Butterworth bandpass filtering and common average reference pair are used to reduce the noise of EEG signal,and complementary ensemble empirical mode decomposition and Pearson correlation coefficient are used to filter the optimal modal components.(2)Research on pattern recognition methods.Firstly,the T-random proximity embedding method is used to reduce dimension,eliminate redundant irrelevant features,and obtain low-dimensional spatial features.Then,a fast distributed gradient classifier model based on decision tree is constructed and the hyperparameters and parameters selected range are determined.Harris Eagle optimization algorithm is used to optimize the parameters of the classifier model to construct the optimal classifier.The experimental data show that the classifier optimized by Harris Eagle has better classification results than the original classifier in pattern recognition.Finally,the improved algorithm is compared with other classification methods to verify the recognition ability of the algorithm.(3)Bionic robotic arm control research.For the composite control system of the motion imagination robot arm integrated with augmented reality,the overall control frame and the software and hardware system of the robot arm were designed,and the test platform of the robot arm was built for experimental analysis.The results proved that the designed composite control method could complete tasks such as object movement,obstacle avoidance and grasping relatively accurately and quickly during the use of the robot arm. |
PDF Full Text Request