52 results about "Steady state visually evoked potential" patented technology

The invention discloses a brain-controlled rehabilitation equipment-oriented control strategy generation method, device and equipment and a storage medium, and relates to the technical field of signal processing, and the method comprises the following steps: acquiring a multi-channel electroencephalogram signal, and preprocessing the multi-channel electroencephalogram signal to obtain a target electroencephalogram signal comprising a steady-state visual evoked potential signal and a motor imagery signal, the target electroencephalogram signal corresponds to a preset action category; decoding the steady-state visual evoked potential signal by adopting filter group task related component analysis to obtain a correlation score vector; decoding the motor imagery signal by adopting a Mangban dynamic routing space-time network model to obtain a classification score vector; performing posterior probability distribution conversion and weighted fusion on the correlation score vector and the classification score vector to obtain fusion probability distribution; and generating a target control strategy according to the action category corresponding to the highest probability value in the fusion probability distribution. The control strategy obtained by the invention can consider both intention recognition precision and rehabilitation nerve activation effect.

The invention discloses an electroencephalogram signal analysis method, and relates to the technical field of electroencephalogram signal processing. The method comprises the following steps: collecting and preprocessing a multi-channel electroencephalogram signal; establishing a spatial filter, and separating and storing task-related signals and task-independent signals from the signals; constructing an auxiliary classifier to generate an adversarial network, training an auxiliary classifier by using the task-independent signal to judge whether task-independent noise is mixed in an artificial signal generated by a generator, and applying punishment to the generator, so as to generate a high-quality and high-fidelity task-related artificial electroencephalogram signal; and jointly inputting the generated signal and the original signal into a CNN-LSTM-Attention hybrid model for training to obtain a final spatial filtering and intention recognition model. According to the method, the problem of model performance bottleneck caused by insufficient individual training data in a steady-state visual evoked potential brain-computer interface system is effectively solved, the accuracy and robustness of patient intention recognition are remarkably improved, and meanwhile, the calibration burden of a user is relieved.

Systems and methods are disclosed for assessing visual function of a user or patient. The systems and methods may contain steps, including: extracting data from an electrode site of a sensor; obtaining an average of the data across successive video presentations; applying a fast-Fourier transform to the average; determining a signal-to-noise ratio (SNR) of results of the fast-Fourier transform at tagged frequencies; determining a steady-state visual evoked potential (SSVEP) amplitude based on the SNR of the results of the fast-Fourier transform at the tagged frequencies; and determining a ratio of lower to higher spatial frequency SSVEPs to assess the vision function of the user.

The invention relates to the technical field of visual interaction systems, in particular to a visual interaction system for young children based on head-mounted equipment, which comprises a display module, a signal processing module and a signal acquisition module, and is characterized in that the display module presents a stimulation normal form for auxiliary diagnosis and a stimulation scene for auxiliary treatment by utilizing an augmented reality technology; the signal acquisition module is used for acquiring electroencephalogram information of the user and sending the electroencephalogram information to the signal processing module; according to the visual interaction system, visual function defect detection can be achieved through the display module in cooperation with electroencephalogram information collection and analysis without depending on language expression feedback of the examined person, and therefore the visual interaction system is more suitable for visual function examination of young children.

The present invention relates to the fields of brain science and computer technology, and in particular to a visual brain-computer interface signal decoding method and system thereof. The method comprises constructing a visual brain-computer filter bank, acquiring EEG data, and transforming it into multiple sub-band signals; constructing a convolutional neural network to extract the signal features corresponding to each sub-band signal, and splicing the signal features along the channel to form an aggregated feature map; constructing a temporal kernel selection network to calculate the weight values ​​of the spliced ​​feature map to obtain a weighted feature map; flattening the weighted feature map to obtain a one-dimensional feature vector; and constructing a classification module to map and classify the one-dimensional feature vector and output the stimulation frequency of the EEG data. The present invention optimizes feature extraction by selecting a temporal kernel, accurately capturing the characteristic pattern of steady-state visual evoked potential signals. By expanding the receptive field of the convolution kernel, task-related feature patterns are emphasized, significantly improving the classification performance and generalization ability of the model.

The present invention discloses a square wave attack method and device for a steady-state visual evoked potential paradigm. The method includes: obtaining the set flicker frequencies of all output categories in the system to be attacked; setting the frequency of the square wave perturbation signal for the target attack category according to the set flicker frequencies of all output categories; setting the amplitude of the square wave perturbation signal according to the EEG amplitude range of the subject; setting any one channel within a predetermined range centered on the occipital region as the attacked channel; superimposing the square wave perturbation signal with the frequency and amplitude on the original signal of the attacked channel to attack the system to be attacked, so that the output of the system to be attacked is changed to the target attack category. The present invention solves the technical problem in the prior art that the perturbation signal is complex and difficult to implement.

The present application relates to the technical field of brain-computer interface, and provides a steady-state visual evoked potential brain-computer interface instruction classification method and device, wherein the method comprises: acquiring electroencephalogram data generated by a user under a frequency-semantic joint stimulation; inputting the electroencephalogram data into an electroencephalogram decoding model to obtain an instruction classification result; a frequency perception feature extractor and a semantic decoding feature extractor respectively extract frequency features and semantic features from the electroencephalogram data in parallel; and a joint decision module obtains the instruction classification result based on the frequency features and the semantic features. The present application expands the number of encodable instructions to the product of the frequency domain dimension and the semantic dimension by designing a frequency-semantic joint stimulation; the frequency perception feature extractor and the semantic decoding feature extractor can simultaneously extract frequency features and semantic features from the electroencephalogram signal, and then the joint decision module is used for fusion decision, so that the accuracy of instruction classification and the interaction efficiency are significantly improved under the premise of ensuring high comfort.

The invention discloses a universal brain-computer interface decoding method, and relates to the technical field of neural network algorithms. In order to solve the technical problems that an existing brain-computer interface decoding algorithm is poor in universality, performance is reduced under a complex normal form, and light weight and high classification performance are difficult to balance, the method comprises the steps that original electroencephalogram signals are preprocessed and enhanced through TRCA (Task Related Component Analysis); and decoding is realized in combination with an enhanced deep neural network containing causal convolution, expansion convolution and a multi-layer full-connection layer. The method is verified by experiments under three normal forms of steady-state visual evoked potential SSVEP, high-frequency SSVEP and SSPVEP, not only maintains the light weight of the network to reduce the over-fitting risk, but also shows the classification performance superior to that of a traditional algorithm and an existing deep learning algorithm when the normal form difficulty is improved and the data length is increased, has strong universality and practicability, and is suitable for popularization and application. The method can be widely applied to brain-computer interface related fields such as medical rehabilitation and virtual reality.

The application provides a steady-state visual evoked potential classification method and device and electronic equipment. The method comprises the following steps: obtaining a steady-state visual evoked potential signal; based on a plurality of different frequency band bandpass filters, extracting short time domain window data corresponding to each frequency band in the plurality of different frequency bands from the steady-state visual evoked potential signal; inputting the short time domain window data corresponding to the plurality of frequency bands into a pre-trained hybrid network model to obtain a classification result of the steady-state visual evoked potential signal; wherein the hybrid network model comprises a convolutional neural network and a bidirectional gated recurrent unit, the convolutional neural network is used to obtain a fusion feature map based on the short time domain window data corresponding to the plurality of frequency bands, and compress the fusion feature map into one-dimensional data; the bidirectional gated recurrent unit is used to obtain the classification result of the steady-state visual evoked potential signal based on the one-dimensional data. The application can accurately classify the short time domain window potential signal data and improve the classification performance.

The present invention relates to a hybrid brain-computer interface method that combines visual and auditory weak stimulation. Its technical characteristics are: determining the video stimulation interface, the number of visual stimulation targets and the visual stimulation attributes, and realizing the visual weak stimulation function by means of high-frequency flickering of the peripheral visual field; realizing the auditory weak stimulation function by applying binaural frequency-divided low-sound-pressure-level auditory stimulation signals to the left and right ears; performing visual weak stimulation and auditory weak stimulation at the same time, and selectively paying attention to the auditory stimulation of the left or right ear to complete the task of the hybrid paradigm; while performing visual weak stimulation and auditory weak stimulation, extracting the steady-state visual evoked potential characteristics and auditory steady-state response characteristics of the user's electroencephalogram signal and performing identification and classification to generate coding instructions. The present invention combines the visual weak stimulation method with the auditory weak stimulation method, and uses two electroencephalogram signals for encoding at the same time, thereby improving the naturalness and friendliness of human-computer interaction, and can encode more targets using fewer frequencies.

The invention provides a steady-state visual evoked potential brain-computer interface system for snowflake point weak flicker coding, and the system comprises a stimulation presentation module which is used for presenting a stimulation interface of snowflake point weak flicker; each stimulation target comprises a corresponding frequency and initial phase combination, and the frequencies and initial phase combinations corresponding to different stimulation targets are different; the stimulation interface is used for guiding the sight line of the user to be transferred to a current stimulation target when each trial starts; the electroencephalogram collection module is used for collecting electroencephalogram signals of the occipital area of the user through a dry electrode head ring, and the dry electrode head ring comprises a plurality of movable electrodes, a reference electrode and a grounding electrode; the signal processing module is used for processing the electroencephalogram signal to identify a steady-state visual evoked potential and acquiring a frequency and phase combination of a stimulation target corresponding to the electroencephalogram signal; and the interaction control module is used for outputting a corresponding control instruction according to the frequency and phase combination of the stimulation target identified by the signal processing module to realize brain-computer interaction.

This invention discloses a brain-controlled cotton candy machine control method based on attention level and SSVEP (Steady State Visual Evoked Potential), belonging to the field of brain-computer interface and food processing equipment integration technology. This invention employs a NeuroSci wireless EEG acquisition system, using SSVEP steady-state visual evoked potentials to achieve flavor selection (original, strawberry, pineapple), and utilizes frontal electrodes to collect EEG signals to calculate attention level. An STM32F407ZGT6 is used as the main controller to establish a precise mapping relationship between attention level grading and the cotton candy machine's motor speed. This is combined with servo motors for automatic quantitative feeding, infrared monitoring for candy card reversal and obstacle clearance, and touch display, voice commands, and multimodal audio-visual feedback. A state machine logic is used to complete the entire process of automatic control, from initialization and preheating to interaction, production, and completion. This invention achieves closed-loop control of "EEG acquisition—signal analysis—device execution," solving the problems of cumbersome operation, poor interactivity, and limited functionality in traditional cotton candy machines. It features intelligent operation, high stability, and high safety, and can be used in parent-child interaction, brain science popularization, and attention training scenarios, possessing high application and promotion value.

A neurofeedback system includes an EEG apparatus, a presentation apparatus and a controller. The controller is configured to: (i) cause the presentation apparatus to display an overlaid image to the user that comprises a first image flickering at a first frequency and a second image flickering at a second frequency different than the first frequency, the first image being an affective distractor stimulus image and the second image being a task-relevant stimulus image, (ii) receive from the EEG apparatus a number of first steady-state visual evoked potential (SSVEP) signals generated in response the first image of the overlaid image and a number of second SSVEP signals generated in response the second image of the overlaid image, and (iii) calculate feedback indicative of how much attention of the was user allocated to the task-relevant stimulus image versus how much attention of the user was allocated to the affective distractor stimulus image.

The invention discloses a manipulator situation awareness space target recognition pre-judgment method, system and device based on a brain-computer interface and a storage medium, and the method comprises the following steps: S1, forming a plurality of grabbing modes based on different types of grabbing electroencephalogram signals of a user; s2, on the basis of the captured image of the captured target, predicting a moving path of the captured target and confirming a capturing mode; s3, based on the predicted moving path and grabbing mode of the grabbed target, grabbing the grabbed target; compared with the prior art, the brain-computer interface normal form of the steady-state visual evoked potential is adopted, the user gazes the instructions corresponding to various different grabbing modes on the display screen, and the instructions induce the user to generate the steady-state visual evoked potential through different flicker frequencies; the electroencephalogram signals are collected through an electroencephalogram signal amplifier and an electroencephalogram interface electrode, and typicality correlation analysis processing is carried out.

The invention provides a neurosurgery ward quick response method based on a double-normal-form layered brain-computer interface system, and relates to the technical field of medical information. The system consists of a motor imagery subsystem, a steady-state visual evoked potential subsystem, a protocol management module, an event response module and a state machine management module, and is interconnected with a ward terminal. According to the method, emergency triggering is achieved through motor imagery normally-open monitoring, stable visual evoked potential high-precision interaction is switched to when the illness state is stable or medical care confirms, and the emergency state can be returned by preemptive interruption; and the state machine module is switched among three states and records operation indexes to realize log auditing and parameter setting. According to the method, quick starting, low false triggering and accurate transmission are considered, and the emergency response efficiency and the communication safety of the ward are improved.

The invention discloses an integrated non-intrusive, safe and user-friendly electroencephalography (EEG) system capable of classifying signals generated from both Event Related Potential (ERP) based steady-state visually evoked potential (SSVEP) and pure cognition, leveraging Riemannian Geometry-based signal classification algorithms for precise command generation. The system seamlessly combines SSVEP-based visual stimuli with cognition-based EEG signals to provide a comprehensive interface for brain-computer interaction (BCI) applications. Riemannian Geometry techniques are employed for robust signal classification and efficient command generation, enhancing the system's accuracy and reliability.

The present invention discloses an EEG signal recognition method based on PLSR and extended FBCCA. The method comprises the following steps: regressing a sinusoidal reference template signal onto a multi-channel sampled EEG signal to obtain an EEG estimation signal; performing correlation analysis on the decomposed sub-band components of the multi-channel sampled EEG signal and the estimation signal to obtain a set of extended correlation coefficients of the sub-band components; and obtaining a set of extended correlation coefficients of the EEG signal for different targets from a set of extended correlation coefficients of different sub-band components of the EEG signal for the estimation signal. The fundamental frequency of the reference template signal corresponding to the maximum value is the target frequency of the EEG signal to be identified. The accuracy and applicability of steady-state visual evoked potential signal recognition are improved by extracting the spatial distribution relationship in the EEG signal. At the same time, the influence of multiple groups of typical variables is taken into account by expanding the main characteristic components, thereby improving the accuracy of EEG signal classification and recognition in the steady-state visual evoked potential paradigm.

This invention discloses a method for analyzing electroencephalogram (EEG) signals, relating to the field of EEG signal processing technology. The method includes: acquiring and preprocessing multi-channel EEG signals; establishing a spatial filter to separate and store task-related and task-independent signals from the signals; constructing a generative adversarial network (GAN) with an auxiliary classifier, training an auxiliary classifier using task-independent signals to determine whether the artificial signals generated by the generator contain task-independent noise, and penalizing the generator to generate high-quality, high-fidelity task-related artificial EEG signals; and inputting the generated signals and the original signals into a CNN-LSTM-Attention hybrid model for training to obtain the final spatial filtering and intent recognition model. This invention effectively solves the model performance bottleneck problem caused by insufficient individual training data in steady-state visual evoked potential brain-computer interface systems, significantly improving the accuracy and robustness of patient intent recognition while reducing the calibration burden on users.

The invention discloses a target identification method based on a steady-state visual evoked potential brain-computer interface and a related device, and relates to the technical field of brain-computer interfaces, and the method comprises the steps: designing a trained identification model which comprises a segment coding module, a plurality of feature extraction modules and a classification module which are connected in sequence, the feature extraction module comprises a first normalization layer, a self-adaptive frequency spectrum module, an enhanced time delay neural network module, an inverse Fourier transform layer, a second normalization layer, a splicing layer, an interactive convolution module and a first addition layer, the trained recognition model is used for determining the recognition frequency corresponding to the electroencephalogram signals, the recognition frequency serves as the target frequency, and the recognition frequency is used as the target frequency. According to the method and the device, the three core application requirements of high-precision identification, cross-subject strong generalization ability, light weight and low delay of the SSVEP-BCI system under an ultra-short time window can be met at the same time.

The embodiment of the invention discloses a control method based on an electroencephalogram signal, a medium, equipment and a product. The method comprises the steps of obtaining the electroencephalogram signal; the electroencephalogram signal comprises a steady-state visual evoked potential signal and a motor imagery signal; identifying the steady-state visual evoked potential signal to obtain a to-be-controlled object and an operation intention for the to-be-controlled object; and performing motor imagery classification on the motor imagery signal to obtain a target imagery mode; the operation intention corresponds to a preset imagination mode; if the preset imagination mode corresponding to the operation intention is consistent with the target imagination mode, generating a control instruction based on the operation intention; and controlling the to-be-controlled object to execute the control instruction. According to the embodiment of the invention, the control efficiency can be improved on the premise that the driving safety is guaranteed, the efficient human-vehicle interaction of'wanted control 'is realized, the control experience of a user is improved, and hidden potential safety hazards are avoided.

The invention discloses a visual stimulation pattern display normal form, equipment, a medium, a product and an adaptive system, and belongs to the field of brain-computer interfaces. The normal form comprises the steps that at least two stimulation areas are displayed in a display interface, each stimulation area in the at least two stimulation areas at least comprises a peripheral view pattern displayed at a preset frequency, the peripheral view patterns are used for inducing steady-state visual evoked potentials, and different stimulation areas correspond to different stimulation time windows; a silence time window is arranged between every two adjacent stimulation time windows; for each stimulation time window under the current stimulation round, determining a display time period distributed in the current stimulation time window, when the display time period arrives, displaying a central view pattern in the center of the corresponding stimulation area, the display time period being randomly distributed in the current stimulation time window, and displaying the central view pattern in the center of the corresponding stimulation area; the duration of the display period is determined based on the response time of the predetermined component of the subject event-related potential. According to the embodiment of the invention, the number of targets corresponding to the classifiable central view pattern can be increased.

The invention relates to the technical field of electroencephalogram control, and discloses an intelligent nursing bed self-adaptive control system and method based on multi-mode electroencephalogram intention recognition, and the system comprises a signal separation module, a state judgment module, an intention feature analysis module, a lateral feature extraction module, a hierarchical intention decision module and a control instruction generation module. Acquiring an occipital area steady-state visual evoked potential signal and a motion-related cortex potential signal based on the original electroencephalogram signal of the patient; analyzing the phase locking stability of the occipital area steady state visual evoked potential signal to judge an attention effective state; analyzing the motion-related cortex potential signal to obtain an energy change sequence; analyzing a contralateral dominating mode of the patient; performing hierarchical intention decision on the patient to obtain a preliminary motion intention of the patient; generating a final control instruction of the target equipment in combination with the physiological feedback signal of the patient; according to the invention, the efficiency of adaptive control of the intelligent nursing bed based on multi-mode electroencephalogram intention recognition can be improved.

This invention discloses a method for decoding EEG features for steady-state visual evoked potentials, including: the subject's fixation frequency being f n The original steady-state visual evoked potentials collected during visual stimulation are X. n Using N fb A filter bank for X n Preprocessing is performed to obtain N fb After preprocessing, the steady-state visual evoked potential pairs are filtered to obtain useful signal components and noise signal components, and then a spatial filter is obtained to obtain the steady-state visual evoked potential template signal. This is used to acquire the unknown steady-state visual evoked potential K and obtain N. fb K is a preprocessed unknown steady-state visual evoked potential. (m) By improving and K (m) The signal-to-noise ratio, calculate N fb The filtered sum K (m) The correlation coefficient of N fb The weighted summation yields ρ n ; We obtained K and steady-state visual evoked potentials [X1,…X i ,…,X n ,...,X Nf The correlation coefficient between ], ρ i If the maximum value is reached, then the visual stimulus frequency of K is f. i The technical solution in this embodiment achieves the effect of improving the accuracy of recognizing unknown visual stimuli using a small amount of training data.

The invention discloses a learning system and method based on steady-state visual evoked potential and transcranial magnetic regulation. The learning system comprises a display module which comprises a display interface and a flicker grid, the display interface is used for displaying a learned question, the flicker grid comprises a plurality of option boxes, each option box corresponds to an answer option, and the plurality of option boxes have different flicker frequencies; the electroencephalogram acquisition module is used for acquiring real-time electroencephalogram signals of a user and sending the real-time electroencephalogram signals to the data processing module; the data processing module is connected with the display module and the electroencephalogram acquisition module, and is used for analyzing the electroencephalogram signals acquired by the electroencephalogram acquisition module, obtaining SSVEP characteristic frequency, comparing the SSVEP characteristic frequency with flicker frequency of an option box, selecting answer options watched by a user, and displaying the options on the display module; a control instruction is output to the magnetic stimulation module according to the answering condition of the user; and the magnetic stimulation module is used for applying magnetic stimulation to the user.

A method for presenting visual feedback includes receiving a steady-state visual evoked potential (SSVEP) signal extracted through an electroencephalogram (EEG) analysis of a user gazing at a visual stimulus of a specific frequency. The method also includes classifying the visual stimulus and generate a classification result based on the SSVEP signal. The method additionally includes disposing, on the visual stimulus, a visual feedback having a same frequency as the visual stimulus. The method further includes reflecting the classification result in the visual feedback in real time.

The invention discloses an SSVEP (Steady-State Visual Evoked Potential) source separation system and method based on multivariate autoregressive cross power spectrum analysis. The method comprises the following steps: firstly, carrying out band-pass filtering, downsampling and principal component analysis on original multichannel electroencephalogram signals, and calculating a whitened data matrix; establishing a multiple autoregression MVAR model, and estimating a model parameter matrix; then calculating a cross power spectral density (CPSD) matrix among the channels; then manifold optimization decomposition is carried out, under a Stiefel manifold constraint condition, a Riemannian trust region optimization algorithm is adopted to maximize a CPSD objective function, a spatial filtering matrix is iteratively solved, and neural source components synchronized with stimulation are extracted; and finally, carrying out reverse reconstruction on the decomposed component signals, and outputting spatial and temporal distribution of the signals. According to the method, the multiple autoregression (MVAR) model and the cross power spectral density (CPSD) analysis are combined, the time delay dependency relationship and the frequency domain coherent structure of the neural signal can be captured at the same time, the neural source component of the steady state visual evoked potential (SSVEP) is efficiently extracted, and higher physiological rationality and higher signal-to-noise ratio are achieved.

The invention relates to a method and a device for presenting visual feedback. The method includes receiving a steady-state visual evoked potential signal extracted by an electroencephalogram analysis of a user gazing a visual stimulus at a particular frequency. The method further includes classifying the visual stimulus based on the SSVEP signal and generating a classification result. In addition, the method includes setting visual feedback on the visual stimulus having the same frequency as the visual stimulus. The method also includes reflecting the classification result in the visual feedback in real time.

This invention relates to a brain-computer interface (BCI) control method and system for a rehabilitation robot, comprising the following steps: S1: Using a target image stimulation paradigm, visual evoked signals are induced in the operator, and the operator's electroencephalogram (EEG) signals are simultaneously acquired. The EEG signals include two types: a first type is steady-state visual evoked potential (VEP) EEG signals, and a second type is hand fine motor intention EEG signals; S2: The steady-state VEP EEG signals are decoded to identify the target object and its spatial position in the image being viewed by the operator, generating a target selection command. By employing the above technical solution, this invention simultaneously acquires steady-state VEP EEG signals and hand fine motor intention EEG signals, which are used for target position selection and robot hand movement type determination, respectively. This achieves decision fusion of dual-modal EEG signals, overcoming the problems of low dimensionality and poor flexibility in single-mode control.

The invention discloses a stroke patient language analysis evaluation method and system and electronic equipment, and relates to the technical field of data processing.The method comprises the steps that electroencephalogram signals of a stroke patient are collected through non-invasive electroencephalogram signal collecting equipment, and the electroencephalogram signals comprise steady-state visual evoked potential signals; performing preliminary analysis on the steady-state visual evoked potential signal through a filter bank canonical correlation analysis algorithm, and outputting first analysis data; acquiring facial expression sensing data and head motion sensing data of the stroke patient through a multi-modal sensing device, optimizing the first analysis data according to the facial expression sensing data and the head motion sensing data, and outputting second analysis data; and inputting the second analysis data into a voice output module for voice conversion output, thereby achieving the technical effects of improving the evaluation accuracy of the expression intention of the patient and improving the communication ability and participation intention of the patient.

The invention discloses an FPV control method and system based on real-time image recognition and a brain-computer interface, and belongs to the technical field of unmanned equipment control. Secondly, detecting a plurality of alternative target objects according to an instance segmentation result, and dynamically projecting a steady-state visual evoked potential stimulation signal on a real-time image transmission picture to form a plurality of groups of flickering visual marks; then acquiring a real-time electroencephalogram signal of an operator, and identifying a target object selected by the operator; and finally, obtaining and generating a motion path of the FPV equipment, and constructing an FPV equipment control instruction to drive the FPV equipment to move. When FPV control is carried out, the alternative target object is determined through real-time image recognition, and the target object selected by an operator is directly acquired through a brain-computer interface, so that the control mode of the operator on the FPV equipment is greatly simplified; and meanwhile, the FPV equipment automatically generates the motion path, so that the problem of misoperation is avoided, and the control accuracy of the FPV equipment is improved.