976 results about "Stimulation Parameter" patented technology

The invention relates to a closed-loop multi-guide multi-mode time domain interference electrical stimulation system and method. The method comprises the steps that a software part determines stimulation parameters; the automatic electrode position adjusting device adjusts the spatial position of the multi-conductive-electrode device, and the multi-conductive-electrode device stimulates the position of a target spot; the current output precision and impedance monitoring module monitors the precision and impedance information of electrode output current, and the core main control and functional circuit module adjusts a control signal according to the precision and impedance information; when the multi-conducting-electrode device conducts stimulation, the electroencephalogram device synchronously collects electroencephalogram signals and uploads the electroencephalogram signals to the electroencephalogram analysis algorithm module of the software part, the electroencephalogram analysis algorithm module evaluates and feeds back the effectiveness and the effect of stimulation, and the time domain interference electrical stimulation function module and the target area dynamic adjustment algorithm module optimize stimulation parameters according to the effectiveness and the effect of stimulation. Through the arrangement, a real-time closed-loop feedback control system is formed, continuous monitoring, real-time optimization and adjustment of electrical stimulation parameters and dynamic adjustment of a stimulated brain target region are realized, and the accuracy and safety of current output are ensured.

The invention discloses a multifunctional magnetic stimulation treatment system and an application method thereof, relates to the technical field of medical equipment, and solves the defects of unstable curative effect and limited application range caused by individual difference in the existing system. The precision of the laser beam is automatically adjusted through a self-adaptive calibration algorithm, and it is ensured that a treatment area is accurately positioned; optimal stimulation parameters are calculated through a self-adaptive magnetic field depth optimization algorithm, and pertinence and effectiveness of treatment parameters are ensured; the treatment effect is predicted through a feedback mapping algorithm, and the treatment parameters are dynamically adjusted to ensure the safety and effectiveness of the treatment process; physiological data, symptom description and medical history information of a patient are comprehensively analyzed and recognized through the complex disease pattern recognition module, a detailed disease pattern recognition report is output, the application range of the system is expanded, and personalized disease prediction and risk assessment are provided; the applicability and the curative effect of an existing system are greatly improved.

The invention relates to the technical field of medical data processing, in particular to an integrated rehabilitation training system based on motion capture and myoelectricity feedback, which comprises a motion data acquisition module, a motion evaluation and analysis module and a control module, and is specifically integrated on a training garment through inertial motion capture equipment and a surface myoelectricity sensor. The motion state and muscle activation condition of a patient are monitored in real time, actual motion data and actual electromyographic signal data of the patient are collected, and whether the motion state of the patient meets the standard or not is judged by comparing the data deviation condition between the actual motion of the patient and the standard motion. And according to the deviation analysis result, the electrical stimulation parameters can be dynamically adjusted through closed-loop control, so that personalized and precise spinal cord electrical stimulation rehabilitation training is realized.

The invention discloses a peripheral nerve stimulation system based on muscle fatigue prediction and compensation, which is used for acquiring electromyographic signals and predicting muscle fatigue states during nerve stimulation, and timely warning and adjusting stimulation to ensure the stability of muscle strength, and comprises a stimulation output module used for stimulating target nerves to activate target muscles according to stimulation parameters; the acquisition and analysis module is used for synchronously acquiring electromyographic signals of target muscles, preprocessing the electromyographic signals and extracting electromyographic signal features; the fatigue prediction module is used for predicting the fatigue state of the muscle according to the electromyographic signal characteristics; the compensation control module is used for adjusting stimulation parameters according to the fatigue state of the muscle and sending the stimulation parameters to the stimulation output module to ensure that the muscle strength is stable in the stimulation process; and the user interaction module is used for performing stimulation control and setting in real time and displaying the stimulation effect and the muscle fatigue state. Muscle fatigue and strength attenuation in the peripheral nerve stimulation process can be dynamically predicted and compensated in time, and the safety, accuracy and reliability of nerve stimulation are improved.

The invention discloses an intelligent orthopedic rehabilitation self-adaptive evaluation method and device based on multi-modal physiological feedback. The method comprises the steps that multi-modal physiological data including electromyographic signals, knee joint three-dimensional motion trails, plantar pressure distribution and cognitive input degree of a subject are synchronously collected; constructing a three-dimensional baseline vector according to the MMT muscle force test, knee joint ROM measurement and VAS pain score of the subject; inputting the multi-modal physiological data and the three-dimensional baseline vector into a personalized orthopedic rehabilitation model, and outputting a knee joint function index prediction value and personalized rehabilitation adjustment parameters; dynamically adjusting electrical stimulation parameters according to rehabilitation adjustment parameters output by the personalized orthopedic rehabilitation model; if the cognitive load index exceeds the early warning line, the electrical stimulation intensity is reduced; otherwise, activating the hippocampus body wave stimulation mode. Adjustment is carried out according to the real-time state of the subject, blindness and subjectivity in the rehabilitation process are avoided, and the rehabilitation efficiency and success rate are improved.

The invention discloses a cranial nerve double-path stimulation intelligent regulation and control method and system for Parkinson's disease, and relates to the technical field of medical treatment and neural engineering, and the method comprises the steps: obtaining an electroneurographic signal of a target patient based on a stimulation electrode, and determining neural activity features according to the electroneurographic signal; the stimulating electrodes comprise a first stimulating electrode and a second stimulating electrode; determining a nerve evaluation value according to the nerve activity characteristics, and generating a co-stimulation parameter based on the nerve evaluation value; initial stimulation parameters of the stimulation electrode are adjusted based on the co-stimulation parameters, the target patient is stimulated through the stimulation electrode, and cranial nerve two-way stimulation intelligent regulation and control for the Parkinson's disease are completed. According to the invention, by introducing the abnormal oscillation mode and evaluating the synchronism characteristics of the neural network in real time, the neural activities of the deep brain and the vagus nerve area are comprehensively quantified, and the accuracy of nerve regulation and control is improved; by generating collaborative stimulation parameters and comprehensively considering the mutual influence of the two stimulation pathways, intelligent collaborative regulation and control are realized.

The invention relates to the technical field of biomedical engineering and neuroscience, and discloses an in-ear vagus nerve stimulation control method, system and device based on scalp electroencephalography, and the method comprises the steps: collecting a scalp electroencephalogram signal, a body movement signal and a pulse signal of a to-be-detected user; performing preprocessing and frequency domain separation on the signal; calculating a sleep coupling score, and dividing a physiological state according to the sleep coupling score; dynamically adjusting the intensity and frequency of intra-ear vagus nerve stimulation of the user according to the physiological state, and generating feedback data; in-ear vagus nerve stimulation is performed and the dynamic adjustment parameters are updated based on the feedback data. According to the method, accurate sleep recognition is realized through multi-source signal parallel processing and SCS modeling, and the classification precision is remarkably improved; lSTM prediction and biphase modulation are combined to optimize stimulation parameters, sleep time is shortened, and side effects are reduced; a GPU acceleration and low-power-consumption framework is adopted, and efficient self-adaptive closed-loop regulation and control are achieved.

The invention discloses a nerve regulation system for cognitive impairment, and the system comprises a cognitive behavior evaluation module which is used for recognizing a cognitive defect field which needs to be improved by a target object; the image data acquisition module is used for acquiring and preprocessing task state nuclear magnetic data and T1 weighted structure images of a target object; the brain region activation analysis module is used for determining a neural regulation potential target spot according to the preprocessed task state nuclear magnetic data; the brain network analysis module is used for identifying key nodes related to cognitive defects; the stimulation target screening module is used for determining an individualized stimulation target of the target object according to the nerve regulation potential target and the key node of the target object; the time domain interference electrical stimulation simulation and implementation module is used for performing electrical stimulation on the individualized stimulation target spot; and the dynamic feedback adjustment module is used for dynamically adjusting the stimulation parameters and enhancing the personalized regulation and control effect. By adopting the method and the device, the specific brain region directly related to the cognitive impairment can be determined, so that the targeting and the accuracy of treatment are enhanced.

Electrical stimulation systems and methods for operation and management of the electrical stimulation system are described. One method for operation of an electrical stimulation system includes receiving a request to modify stimulation; displaying a graph for at least one adjustable stimulation parameter; indicating on the graph a current setting of each of the at least one adjustable stimulation parameter and a range around the current setting of at least one of the at least one adjustable stimulation parameter which represents at least one of a minor change in the stimulation based on a predefined minor change criteria or a moderate change in the stimulation based on a predefined moderate change criteria; receiving a selection of a new setting for at least one of the at least one adjustable stimulation parameter; and modifying the stimulation according to the selection.

A system for restoring muscle function to the lumbar spine to treat low back pain is provided. The system may include electrodes coupled to an implantable pulse generator (IPG), a handheld activator configured to transfer a stimulation command to the IPG, and an external programmer configured to transfer programming data to the IPG. The stimulation command directs the programmable controller to stimulate the tissue in accordance with the programming data. The system may include a software-based programming system run on a computer such that the treating physician may program and adjust stimulation parameters.

Embodiments of the present disclosure may include a neuromodulation system. The neuromodulation system can include an implantable pulse generator configured to provide neuromodulation to a patient based on a sequence of neuromodulation programs. The implantable pulse generator can include alternately accessible memories. The alternately accessible memories can enable the implantable pulse generator to store a second neuromodulation program while providing neuromodulation according to a first neuromodulation program. The IPG can then transition from providing stimulation according to the first neuromodulation program to providing stimulation according to the second neuromodulation program without stopping stimulation. The neuromodulation system can include a parameter memory. Stimulation parameter updates can be written to the parameter memory. The value of the parameter memory can be used to determine the next stimulation pulse. The IPG can accordingly support pulse-to-pulse changes in stimulation parameters.

The invention relates to the technical field of biomedicine, and discloses a female private rehabilitation training system integrating a breathing method and neural motor control, and the system comprises a data collection module which is used for collecting physiological data of a user; the personalized training scheme generation module is used for generating a personalized nerve-muscle-fascia training scheme; the nerve-muscle synchronous resonance training module is used for constructing a time sequence matching model and generating an adaptive training dynamic control instruction; the space-time adaptability training module is used for adjusting training duration, intensity and stimulation frequency; the multi-dimensional nerve-muscle feedback fusion module is used for constructing a feedback response model; and the fascia synchronous activation and relaxation module is used for coordinating the tension relationship between the fascia and the muscle system. According to the method, feature matching is carried out on an electric activation heat map matrix through the convolutional neural network, analysis of a myoelectricity response state transition probability is carried out in combination with a hidden Markov model, and stimulation parameters and respiratory rhythm are optimized in real time according to a training process.

The invention provides a nervous system closed-loop regulation and control system based on transcranial opto-acoustic cooperation. The nervous system closed-loop regulation and control system is composed of EEG monitoring equipment, personal information data, an opto-acoustic cooperation system, a multi-mode large model and a psychological disease database. Aiming at a transcranial infrared light and ultrasonic co-stimulation technology of a patient, through optimization of stimulation parameters, photoacoustic bidirectional regulation and control are realized, a multi-modal large model is used as a base, multi-modal data fusion and reinforcement learning are used as technical means, closed-loop photoacoustic co-stimulation system equipment is designed, and intervention parameters are automatically optimized; an effective regulation and control method for psychological diseases is realized through a non-intrusive brain stimulation technology, and adaptive adjustment and closed-loop feedback of parameters are realized. Brain waves of a patient are detected in real time, personal information of the patient is collected, the information is integrated and transmitted into the system after evaluation, intervention is adjusted in real time according to different brain states, and feedback is utilized to adjust parameters of next intervention and stimulate the precise regulation and control process.

The invention provides a muscle stimulation adjusting method and system based on gait detection. The method comprises the following steps: acquiring lower limb biomechanical data; performing phase recognition according to a dynamic programming algorithm, and constructing a pressure center track to perform feature extraction to obtain gait features; carrying out feature analysis on the gait features through a reverse dynamics model and a machine learning model, and carrying out output judgment according to a double-model collaborative decision-making mechanism to obtain a stimulation parameter decision-making result; generating an electrical stimulation waveform according to the decision result, and applying differential electrical stimulation to the target muscle in the gait cycle to obtain a stimulation scheme; and monitoring stimulated myoelectricity feedback signals and gait changes, and carrying out parameter updating by calculating a signal change rate and analyzing a motion track error to obtain an updating scheme. According to the method, through a double decision-making mechanism of the reverse dynamics model and the machine learning model and a real-time feedback regulation mechanism, the gait phase recognition precision is improved, and precise regulation and control of stimulation parameters are realized.

The invention belongs to the technical field of nerve regulation and control, and discloses a portable noninvasive brain deep electrical stimulation system based on time interference, and the system comprises an electroencephalogram signal collection unit which is used for obtaining an electroencephalogram signal in a high-quality resting state, and carrying out the signal preprocessing and feature extraction, thereby obtaining an optimal stimulation envelope frequency; the personalized finite element modeling unit is used for constructing a head personalized model and outputting optimal electrode configuration and stimulation parameters for a target brain region target spot; the electrode positioning and stimulation execution unit is used for realizing accurate positioning of an electrode position and implementing time interference electrical stimulation; the MCU unit is used for realizing data management, task scheduling and remote communication; the human-computer interaction unit is used for realizing visual treatment flow and parameter setting; and the power supply management unit provides stable working voltage for the system. According to the invention, the portable, intelligent and personalized modeling of the electrical stimulation treatment equipment is realized, and the precision and efficiency of nerve regulation and control are remarkably improved.

The invention relates to the technical field of self-adaptive electrical stimulation pelvic floor treatment systems, in particular to a self-adaptive electrical stimulation pelvic floor treatment system combined with reinforcement learning and a control method of the self-adaptive electrical stimulation pelvic floor treatment system combined with reinforcement learning, and the self-adaptive electrical stimulation pelvic floor treatment system comprises a micro electrical stimulation device which is used for applying electrical stimulation to pelvic floor muscles; the multi-mode biological feedback sensor array is used for collecting physiological signals of a patient; the data processing module is connected with the multi-mode biological feedback sensor array and used for processing the collected physiological signals; the reinforcement learning controller is connected with the data processing module and the micro electrical stimulation device and is used for dynamically adjusting electrical stimulation parameters based on the processed physiological signals; the human-computer interaction interface is connected with the reinforcement learning controller and is used for receiving doctor input and displaying treatment information; through real-time self-adaptive control and personalized parameter adjustment, a target muscle group can be stimulated more accurately, so that muscles are stimulated in an optimal state all the time, and the pelvic floor muscle strength improvement degree of a patient reaches 68.5% and is far higher than that of a traditional method.

A system for restoring muscle function to the lumbar spine to treat low back pain is provided. The system may include electrodes coupled to an implantable pulse generator (IPG), a handheld activator configured to transfer a stimulation command to the IPG, and an external programmer configured to transfer programming data to the IPG. The stimulation command directs the programmable controller to stimulate the tissue in accordance with the programming data. The system may include a software-based programming system run on a computer such that the treating physician may program and adjust stimulation parameters.

The invention discloses a closed-loop noninvasive spinal cord electrical stimulation regulation and control method and system based on motion data, and the system is provided with a flexible electrode array patch which is attached to the skin surface of a to-be-tested human body; the sensing module is used for collecting electromyographic signals or acceleration signals of a user; the signals are processed, and characteristic values related to actions and motions are extracted; the closed-loop feedback module compares the characteristic value with a model set characteristic / threshold value, and judges whether to start stimulation site and stimulation parameter adjustment or not; automatically selecting an optimal electrode unit and a corresponding stimulation parameter based on a judgment result; the dynamic regulation and control module controls the optimal electrode unit to output stimulation current, and regulates the activity of corresponding spinal cord neurons; and continuously performing closed-loop adjustment on the electrode unit and the stimulation parameters according to the motion feedback data. According to the method and system, precise stimulation and closed-loop regulation and control based on the motion response of the patient are achieved, and the problems that stimulation target positioning depends on artificial experience, parameter adjustment is not timely, and individualized and closed-loop feedback capacity is lacked are solved.

The present disclosure provides systems and methods for providing neurostimulation therapy according to patient features. The patient features may be analyzed to develop a patient model between physiological and / or patient reported features and optimal settings for a neurostimulation therapy using machine learning operations. The model is trained using data corresponding to time intervals in which electrical pulses are applied to the patient, including video kinematic data, patient reported pain levels, implanted or worn sensor patient data, and stimulation parameters. The model is used to control ongoing neurostimulation therapy for the patient.

The invention relates to the technical field of rehabilitation medicine, in particular to a body carving slimming method for fat metabolism based on bioelectricity feedback and acupoint stimulation. The invention provides a body carving slimming method based on bioelectricity feedback and acupoint stimulation. The body carving slimming method comprises the four steps of obtaining, processing, acupoint stimulation and outputting. Bioelectricity signals and weight data of body parts of a user are collected through a bioelectricity and weight detection device. The muscle activity state and fat distribution are analyzed, a posture improvement value is calculated, and a personalized body carving training scheme is generated. In an acupoint stimulation link, electrical stimulation is performed on a specific acupoint according to the scheme, bioelectricity feedback is monitored in real time, and stimulation parameters are dynamically adjusted. And generating a personalized report containing the posture change and the training effect evaluation, and providing suggestions to the user. According to the method, the effect, safety and individuation are remarkably improved, and a new direction is opened up for development of the body carving and slimming field.

The invention relates to the technical field of medical rehabilitation, and particularly discloses a finger rehabilitation electrical stimulation parameter control method and system, and the method comprises the steps: obtaining a multi-channel surface electromyogram signal and finger joint kinematics data of a patient; according to the multi-channel surface electromyogram signals, target fine actions of the patient are recognized; according to the target fine motion and the finger joint kinematics data, a multi-muscle group cooperative activation mode and an expected motion trail are obtained through analysis; generating an electrical stimulation parameter combination for different muscle groups according to a multi-muscle group collaborative activation mode; outputting multi-channel electrical stimulation to the patient based on the electrical stimulation parameter combination, and continuously adjusting the electrical stimulation parameter combination on line according to the deviation between the kinematics data of the finger joints and the expected motion trail; the method solves the problems that an existing method is difficult to effectively simulate complex multi-muscle group coordination, complex in parameter adjustment and difficult to adapt to dynamic changes of a patient, and the effect of assisting the patient in complex and fine finger action training is achieved.

The invention relates to the technical field of neural engineering and brain-computer interfaces, in particular to a closed-loop multi-modal nerve stimulation system and method based on time interference, and the system comprises a multi-modal stimulation module which is used for integrating electrical stimulation, magnetic stimulation and optical genetic stimulation, and generating a time interference field domain; the neural state sensing module is used for collecting real-time electroencephalogram signals, blood oxygen concentration and neural metabolite level data; the neural control center is used for fusing neural state data and stimulation parameters, and dynamically adjusting time interference frequency and stimulation intensity through an adaptive algorithm; the time sequence cooperation engine predicts a neural response time phase based on a deep learning model, and optimizes a stimulation time sequence and a mode switching strategy; and the visual interaction platform is used for rendering the nerve activation thermodynamic diagram and the stimulation parameter adjustment curve in real time. Therefore, the problems of adjustment strategy solidification, low adjustment precision, insufficient energy conversion efficiency and the like in the prior art are solved.

The invention discloses a transcutaneous electrical stimulation device based on a large language model, and relates to the technical field of wearable devices. The transcutaneous electrical stimulation equipment based on the large language model comprises a data acquisition and preprocessing module, a feature extraction and fusion module, a model training and prediction module and a real-time information processing and response module, real-time data processing and intervention capabilities are integrated, human body data can be analyzed in real time, electrical stimulation parameters can be automatically adjusted, an instant treatment or adjustment scheme is provided for the problems of arrhythmia, chronic pain and the like, and the timeliness and effectiveness of treatment are greatly improved. And secondly, by fusing the physiological signal features and the symptom text features, the equipment can more comprehensively understand the health condition of the user, so that a more accurate treatment scheme is provided. Besides, the equipment also has the functions of anomaly detection and trend analysis, can predict potential health risks, and triggers an alarm in time when a serious anomaly is detected, thereby effectively guaranteeing the life safety of the user.

The invention discloses a brain deep nerve stimulation method and system based on adaptive adjustment, and relates to the technical field of brain deep nerve regulation, and the method comprises the steps: collecting a local field potential signal of a brain deep target region of a target patient, and extracting a beta frequency band power spectrum density and a gamma frequency band phase synchronization index as neural activity characteristic parameters; determining an individual baseline value and a preset threshold value based on historical data, and outputting a stimulation adjustment trigger signal when the beta frequency band power spectral density exceeds the individual baseline value and the gamma frequency band phase synchronization index is lower than the preset threshold value; in response to the trigger signal, calculating an optimal stimulation parameter combination through a gradient descent optimization algorithm and executing nerve regulation; and monitoring the signal change after regulation and control, calculating a relative change rate and updating a threshold value. Through a two-parameter joint judgment mechanism and a threshold updating strategy, individualized adaptive adjustment of stimulation parameters is realized, and the stimulation accuracy and the treatment effect are improved.

The invention provides a control method and system for electronic acupuncture equipment, and the method comprises the steps: obtaining the symptoms of a user, and recommending a matched acupoint for the user based on a symptom-acupoint database; the electronic acupuncture device scans a target area where the matched acupoint is located, skin resistance distribution and an infrared thermal image of the target area are obtained, the acupoint matching degree is calculated through a probability model, and the acupoint is positioned based on the acupoint matching degree; and monitoring whether the scanning area of the electronic acupuncture device is aligned with the recommended acupuncture point or not in real time, outputting current stimulation by the electronic acupuncture device when the scanning area is monitored to be stopped, and adjusting the output current according to the symptom of the user after the user is stimulated. By means of the method and the corresponding system, the user can be assisted in accurately positioning the acupuncture points, the stimulation parameters can be dynamically adjusted according to feedback information obtained after acupuncture and moxibustion are conducted on the user, and the optimal curative effect is kept.

The invention discloses an ear and neck percutaneous vagus nerve stimulation device based on an internal feeling signal, which belongs to the related technical field of percutaneous vagus nerve stimulation and is composed of a physiological signal acquisition module, a data transmission module, a signal processing and analyzing module, a control module, ear and neck integrated stimulation equipment, an interaction module and a power supply. The device supports neck or ear vagus nerve stimulation and a stimulation mode of combined use of the neck and the ear vagus nerve. And the same or different stimulation parameters can be used for the vagus nerves of the ears and the neck. According to the non-invasive design, the electrode does not need to be placed in a human body through an operation, nerves are stimulated through an external stimulator, and the risk of injury to the human body is reduced; and meanwhile, the output of two stimulation parameters taVNS and tcVNS can be adjusted adaptively through physiological signals and internal feeling indexes, and the fusion effect is improved.

Methods and systems are provided for treating chronic pain using spinal cord stimulation in combination with a neural recording device that records electrical signals from neural activity associated with chronic pain. Machine learning computational models are used to classify patterns of neural activity associated with various pain states and pain relief. Neural signatures of “chronic pain” and “pain relief” are used to assist with spinal cord stimulation programming to determine therapeutic stimulation parameters that achieve analgesia without paresthesia, the minimum stimulation amplitude needed for effective therapy, and to predict whether spinal cord stimulation is likely to be effective in relieving pain. A closed-loop therapy system is provided comprising a spinal cord stimulator that records brain electrical signals from neural activity associated with pain and automatically adjusts spinal cord stimulator settings to deliver electrical stimulation to the spinal cord when pre-specified patterns of neural activity associated with pain are detected.

One aspect of the present disclosure relates to a system for treating obstructive sleep apnea in a subject. The system can include a power source and a neuromuscular stimulator in electrical communications with the power source. The neuromuscular stimulator can include a controller and at least one electrode. The controller can be configured to receive certain power and stimulation parameters associated with a therapy signal from the power source. The at least one electrode can be configured to deliver the therapy signal to a target tissue associated with control of a posterior base of the tongue of the subject.

The invention is suitable for the field of health care treatment, and provides a forest health care activity system and an evaluation method thereof. The multi-sensory collaboration module comprises a cross-modal sensory calibration unit; a physiological sensing module; the intelligent evaluation module is used for calculating immersion degree and healing effect indexes based on a multi-dimensional quantitative model, and dynamically optimizing sensory stimulation parameters according to corresponding calculation results; the multi-dimensional quantitative model comprises a mapping relation between user physiological features and sensory stimulation parameters; the safety interaction module is used for monitoring abnormal fluctuation of the physiological indexes of the user in real time and triggering a sensory stimulation intensity down-regulation mechanism when the abnormal fluctuation exceeds a safety threshold value; regional and body restrictions of traditional forest health care are broken through, and immersive experience is provided for groups such as disabled people through multi-module cooperation. The immersion is enhanced through multi-sensory cooperation, personalized optimization is achieved through intelligent evaluation, user safety is guaranteed through safe interaction, and the universality and effectiveness of forest rehabilitation are further improved.

The invention provides an electronic acupuncture closed-loop control method and system based on multi-modal information feedback. The method comprises the following steps: acquiring long-term, short-term and real-time representation information of a testee under an initial stimulation scheme; the long-term and short-term characterization information is input into a collaborative generative network model for learning, a target stimulation scheme is output, the collaborative generative network model comprises traditional Chinese and western medicine agents, the traditional Chinese and western medicine agents learn the long-term and short-term characterization information, and a corresponding traditional Chinese medicine treatment scheme and an electrical stimulation scheme are output. The collaborative generation network model carries out collaborative training on traditional Chinese and western medicine agents; calculating an index deviation between the real-time characterization information and the standard characterization information to generate a real-time information deviation matrix; inputting the target electrical stimulation scheme and the real-time information deviation matrix into a parameter deviation prediction agent for learning and prediction; and performing parameter adjustment on the target stimulation scheme based on the predicted stimulation parameter deviation matrix to realize long / short time and real-time double-layer closed-loop control of the electronic acupuncture.