Please select the most similar patent number from A, B, C and D. Which number is?
A robot natural control method based on an electromyographic signal and an error electroencephalographic potential. The method mainly comprises the following steps: an experimenter executing four natural actions according to a prompt; collecting an electromyographic signal and IMU data, performing pre-processing, feature extraction and classifier classification on the data, and controlling, according to a classification result, a UR5 mechanical arm such that same executes a corresponding action on a target; and triggering and starting the acquisition of an electroencephalographic signal while controlling the UR5 mechanical arm, performing pre-processing, feature extraction and classifier classification on data, detecting whether an error potential is generated, if an error potential is generated, stopping the current action of the UR5 mechanical arm, otherwise, no interference being carried out. Compared with a traditional electromyographic signal control method, in the present method, electromyographic identification of a natural action is provided, an electromyographic signal and an IMU signal are also fused for data processing and classification identification, and an error correction mechanism during a robot control process is then improved by means of the identification of an error electroencephalographic potential, thereby improving the control efficiency and accuracy of a robot.
A: The present application provides a wearable device-based mobile robot control system and a control method. The flow of a first control method comprises: a pose of an operator&#39;s gesture — a pose of a virtual gesture model — an end pose of a virtual robotic arm ― an end pose of a multi-degree-of-freedom robotic arm; by establishing the driving relation between the pose of an operator&#39;s gesture and the end pose of a multi-degree-of-freedom robotic arm, continuous control of the end pose of the multi-degree-of-freedom robotic arm is achieved. In a second method, during control, different hands of an operator are used for controlling different action parts of a slave robot, and separate control by the left and right hands enables the slave robot to more accurately execute a command action. In a third method, the configuration of a connection hand form enables an operator not to depend on a physical controller. Compared with discrete control of robot actions implemented only by using a gesture type, the present application implements incremental continuous accurate control of the end position of a detection system.
B: A CNN-LSTM deep learning model-based driver fatigue identification method comprising the following steps: acquiring EEG signals from a subject under test during a driving simulation; issuing an operation instruction randomly during the driving simulation, and dividing, according to a response time of the test subject for completing the operation instruction, the EEG signals into fatigue data and non-fatigue data; performing band-pass filtering and mean removal preprocessing on the EEG signals, and extracting N minutes of the required fatigue EEG signal data and non-fatigue EEG signal data ; performing independent component analysis on the EEG signal data so as to remove interference signals therefrom; establishing a CNN-LSTM model, and configuring a network parameter of the CNN-LSTM model; inputting the interference-free EEG signal data into a CNN network and performing feature extraction; and reconstructing feature extraction data, and inputting the same into an LSTM network and performing classification. Experiment results indicate an improved accuracy of 96.3 ± 3.1% (grand mean ± population standard deviation).
C: A natural action electroencephalographic recognition method based on source location and a brain network. The method comprises: (1) performing multi-channel electroencephalographic measurement on a natural action; (2) pre-processing a collected EEG signal, and extracting MRCP, a θ wave, an α wave, a β wave and a γ wave; (3) determining a lead field matrix of the signal, solving an initial value solution of a source by using an L1 regularized constraint, and performing iterative solving by using a successive overrelaxation method, so as to obtain a source location result; (4) taking sources as nodes, and calculating a PLV between each pair of sources time point by time point by using a short-time sliding window, so as to construct a brain network; and (5) calculating a network adjacency matrix and five brain network indexes time point by time point, sending these features to a classifier for training and testing, and performing a statistical test on the brain network indexes. By means of the method, a traditional source location method is improved by means of combining a T-wMNE algorithm with a successive overrelaxation method, and a brain network is constructed by means of taking sources as nodes, thereby facilitating the improvement of the electroencephalographic decoding precision of a natural action and the revealing of a nerve operation mechanism of a human body.
D: A motor imagery electroencephalogram signal processing method, a device, and a storage medium. The method comprises: inputting a source MI electroencephalogram signal of a source domain and a target MI electroencephalogram signal of a target domain into an initial feature extraction model separately, and obtaining a first source MI feature used to represent the source MI electroencephalogram signal and a first target MI feature used to represent the target MI electroencephalogram signal (S202); inputting the first source MI feature into an initial classification model, and obtaining a first classification result output by the initial classification model, wherein the first classification result is used to represent a desired execution action of the source MI electroencephalogram signal (S204); and if a first known action corresponding to the source MI electroencephalogram signal is inconsistent with the desired execution action of the source MI electroencephalogram signal as represented by the first classification result, or if a similarity level between a feature distribution of the first source MI feature and a feature distribution of the first target MI feature is less than a first pre-determined threshold, adjusting a model parameter of the initial feature extraction model and/or a model parameter of the initial classification model, and obtaining a target feature extraction model and a target classification model (S206).
Answer: A