689 results about "Stimulation Parameter" patented technology

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 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 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 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 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 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 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.

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.

An example of a system for delivering neurostimulation may include a programming control circuit and a user interface. The programming control circuit may be configured to generate stimulation parameters controlling delivery of the neurostimulation according to a pulse sequence. The pulse sequence may include a series of neurostimulation pulses and be defined by sequence parameters and one or more modulation functions each modulating an adjustable parameter selected from the sequence parameters. The user interface may be configured to set the pulse sequence to a tonic pulse sequence by determining an initial value for each adjustable parameter and set the pulse sequence to a modulated pulse sequence by selecting one or more adjustable parameters, determining a modulation function for each selected adjustable parameter, and applying the determined modulation function to that selected adjustable parameter to modulate the tonic pulse sequence.

Methods and systems for spinal cord stimulation (SCS) are disclosed. The methods and systems involve the use of electrode leads implanted within the spine of a patient to record nerve responses induced by a stimulus. The disclosed neural responses differ in various aspects from the electrical responses previously measured in the SCS context, such as stimulation artifacts and evoked compound action potentials (ECAPs). The disclosed neural responses typically occur at a later time after inducing the stimulation pulse. Another significant feature is that when the frequency of evoked stimulation is ultra-low, such as about 10 Hz or less, the disclosed neural response is most significantly observed at a consistent, relatively constant amplitude as a whole. The disclosed methods and systems may use these neural responses as indications of pain, treatment, and / or another clinically relevant dimension to guide / confirm stimulation placement, as well as feedback control for stimulation parameters.

The invention discloses a nerve regulation and control intervention system based on time domain interference electrical stimulation, and the system comprises a magnetic resonance image registration module which receives and registers an imported magnetic resonance image, and obtains a to-be-processed MRI image; the head model tissue segmentation module is used for performing head model tissue segmentation on the to-be-processed MRI image; the stimulation simulation module is used for performing stimulation simulation based on the tissue segmentation structure to obtain a front lead field matrix; the intervention coordinate selection module is used for determining a target brain region corresponding to a target spot brain region coordinate position needing stimulation intervention and putting the target brain region into a running sequence; the electrode position arrangement optimization module is used for verifying, optimizing and finely adjusting the electric field intensity of the target brain region under different electrode arrangement schemes to obtain an electrode configuration parameter result and displaying the electrode configuration parameter result; the parameter importing module is used for receiving the selected electrode configuration parameters and imported preset electrical stimulation parameters; and the electrical stimulation module is used for performing electrical stimulation on the target brain region of the target object. According to the application, the accuracy and the effectiveness of nerve regulation and control treatment can be realized.

The invention relates to the technical field of electric acupuncture apparatuses, and discloses a method and a system for automatically adjusting stimulation parameters of an electric acupuncture apparatus. According to the method, body surface electromyographic signals, skin impedance and capillary hemodynamic parameters of a user are collected in real time through a sensor set, feature extraction and analysis are conducted through a time sequence prediction model of a Transform architecture, and physiological feature data are generated. And then, constructing a user physiological state characterization model by adopting a multi-modal neural perception fusion algorithm, and carrying out collaborative optimization on the electrical stimulation parameters through an adaptive quantum particle swarm optimization algorithm to generate a stimulation parameter combination matched with the real-time neuromuscular response characteristics of the user. The system drives electrical stimulation output according to the parameter combination, monitors physiological data changes in real time, realizes closed-loop dynamic balance of stimulation parameters and biological feedback signals through the dynamic parameter compensation module, and improves the treatment effect and safety.

A neurostimulation system that has a programmer which has a power supply and processors with processing circuitry and a neurostimulation device which has power supply, processors and communication circuitry for providing communication with a neurostimulation device as well as communication circuitry for communication with the programmer. The neurostimulation system is configured to deliver treatment to a patient in accordance with a clinician prescribed protocol of a regimen. The neurostimulation system compares a delivered therapy treatment which has a treatment protocol to a defined treatment protocol of a prescribed treatment and adjusts the prescribed treatment stimulation parameters to compensate for a difference between the delivered neurostimulation therapy treatment and the prescribed treatment protocol for closely approximating the prescribed treatment protocol.

The invention discloses a patient improvement effect analysis method for controlling spinal cord electrical stimulation through an implantable brain-computer interface, and relates to the technical field of medical rehabilitation, and the method comprises the steps: multi-dimensional collaborative data collection: implanting electrodes in a target brain region and below a spinal cord injury segment, installing a detection element at an exoskeleton key part, and carrying out multi-dimensional collaborative data collection; a sensor is attached to a lower limb preset muscle group, electroencephalogram signals, SCS stimulation parameters, EXS motion data and neuromuscular response data are synchronously collected, and time correlation marks are embedded; according to the method, the reliability of motion intention decoding is remarkably improved by adopting a mode of combining multi-source signal preprocessing and a multi-mode intention recognition model, and in the signal preprocessing stage, the self-adaptive filtering algorithm combining Kalman filtering and wavelet threshold denoising is applied, so that the motion intention decoding efficiency is improved. SCS electrical stimulation interference, EXS motor noise and physiological noise in the electroencephalogram signals are effectively removed.

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 provides a nerve stimulation system which comprises a stimulation device, the stimulation device comprises a signal generation circuit, a control module, a signal acquisition circuit and a reuse electrode, and the stimulation device is arranged to be implantable in a body, generates a stimulation signal and applies the stimulation signal to a target nerve through the reuse electrode; the signal generation circuit applies a titration stimulation signal to a target nerve according to a preset mode, the signal intensity of the titration stimulation signal in the preset mode is gradually increased to a peak value from an initial threshold value, and then the titration stimulation signal is generated after the peak value is kept for a period of time or some stimulation periods; gradually reducing to a termination threshold value and ending; when a titration stimulation signal is applied to a target nerve, the signal acquisition circuit can correspondingly acquire an evoked potential; and selecting the stimulation parameter with the lowest stimulation signal intensity in the evoked potential time period meeting the target threshold value as a parameter of a treatment stimulation signal for subsequent treatment stimulation signal application.

A system for automated programming of stimulation devices uses machine learning with multimodal medical imaging data to determine optimal patient-specific parameters. The system stores voxel-level intensity and functional imaging data from patients who previously underwent stimulation therapy. Similarity metrics calculated directly between the raw imaging data are used to cluster patients into phenotypic groups. Known therapeutic outcomes for each patient are linked to the associated stimulation parameters. For a new patient, biomarkers are extracted from their medical imaging data and used to identify phenotypically similar groups. The stimulation parameters with beneficial outcomes in those groups are analyzed to determine target recommended settings or discard suboptimal settings for the new patient. This data-driven approach leverages machine learning on multimodal medical imaging to automate patient-specific programming of stimulation therapy devices for optimal therapeutic benefit without requiring spatial normalization or atlas registration.

The invention discloses a physiotherapy instrument control system and method based on artificial intelligence, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: collecting an electromyographic signal in real time, carrying out band-pass filtering and TK energy operator processing, and outputting a muscle activation feature vector; based on the muscle activation feature vectors, spatial-temporal features are extracted through a convolutional neural network, and a physiotherapy parameter combination is constructed; a PID controller is adopted to adjust the physical therapy parameter combination in real time, and optimized physical therapy parameters are output; performing electromagnetic thermal coupling simulation on the optimized physiotherapy parameters through finite element analysis to obtain a physiotherapy instrument control instruction; and updating the weight of the convolutional neural network by adopting a stochastic gradient descent algorithm according to the physiotherapy efficiency evaluation report, and outputting an optimization execution instruction. PID parameters are optimized through a quantum annealing algorithm, stimulation parameters are dynamically adjusted in combination with myoelectricity beta wave band phase locking, and treatment parameters and physiological response are synchronized.

The invention provides a transcranial deep electrical stimulation closed-loop regulation and control system and method based on fNIRS dynamic guidance. The method comprises the steps that a transcranial alternating current electrical stimulation module applies an electrical signal to a target deep brain region according to preset parameters to carry out electrical stimulation regulation and control; the synchronous triggering module is used for realizing millisecond-level synchronization of a stimulation task and near-infrared brain function data acquisition; the brain function monitoring module analyzes the concentration change of cortex oxyhemoglobin in real time through the multi-channel head cap; the electrical stimulation-response coupling mapping module positions a significant activation channel by adopting statistical test, and establishes an oxyhemoglobin concentration variable and electrical stimulation current amplitude quantitative model; and the dynamic parameter adjusting module optimizes the output current in real time. Personalized closed-loop regulation and control of non-invasive deep brain stimulation can be achieved, cerebral cortex blood oxygen signals are monitored in real time through near-infrared brain function imaging, stimulation parameters are dynamically adjusted, and the accuracy and individualization level of deep brain stimulation are remarkably improved.

The invention relates to the technical field of intelligent medical instruments, and discloses a closed-loop electro-acupuncture therapeutic apparatus based on heart and brain coupling information feedback. The device comprises a forehead electroencephalogram signal acquisition module, a single-lead electrocardio acquisition module, a Bluetooth transmission module, a signal preprocessing module, an ECG and EEG feature extraction module, a dynamic coupling analysis module, an embedded XGBoost classifier and an electroacupuncture control module. According to the system, collected EEG and ECG signals are wirelessly transmitted through Bluetooth, HRV indexes and EEG frequency band power spectral density are extracted after preprocessing, and frequency domain coherence analysis is carried out to obtain heart and brain bidirectional coupling characteristics. The embedded XGBoost classifier outputs optimal electroacupuncture stimulation parameters based on the characteristics, and the electroacupuncture control module generates corresponding bidirectional pulse waves for stimulation. According to the therapeutic apparatus, closed-loop feedback control is achieved, therapeutic parameters can be dynamically optimized, the individuation and precision level is improved, and meanwhile safety is ensured through impedance monitoring and electrical isolation.

The invention provides an electrical stimulation thumbtack needle system and method, and the method comprises the steps: determining the change gradient of the muscle contraction intensity of a target user through the median frequency feature of an electromyographic signal on the skin surface of the target user, and further determining the muscle fatigue feature of the target user; determining the change trend of the skin resistance of the target user according to the fluctuation characteristics of the skin resistance in the skin resistance data of the target user and the environmental temperature and humidity of the target user, and determining the tolerance index of the target user to electrical stimulation according to the muscle fatigue characteristics and the change trend of the skin resistance; the parameter deviation of each thumbtack needle of the electrical stimulation thumbtack needle equipment in the electrical stimulation process is determined; and based on all the parameter deviations, adjusting electrical stimulation parameter information of the electrical stimulation thumbtack needle equipment, and controlling the electrical stimulation thumbtack needle equipment to continuously perform electrical stimulation on the target user. By adopting the scheme, the stimulation parameters of the electrical stimulation thumbtack needle equipment can be dynamically adjusted according to the real-time physiological data of the user.

The invention relates to the technical field of data analysis and processing, and discloses a sleep-awakening circadian rhythm disorder adjusting method and device based on ultrasonic stimulation. The method comprises the following steps: collecting sleep physiological data of a user to construct a feature matrix; analyzing a sleep stage to obtain a disorder type; generating an ultrasonic stimulation parameter scheme; determining brain region stimulation coordinates to generate spatial positioning data; predicting user feedback through the convolutional neural network; and comparing the prediction and actual feedback adjustment schemes to obtain an optimized target stimulation scheme. According to the method and the device, accurate recognition, personalized stimulation parameter generation, accurate brain region positioning, predictive evaluation and dynamic scheme optimization of sleep-awakening circadian rhythm disorders are realized, so that the regulation effect of ultrasonic stimulation on various sleep-awakening circadian rhythm disorders is improved.

The invention provides a brain organism closed-loop rehabilitation system and a control method thereof, belongs to the field of brain-computer interfaces and rehabilitation medical treatment, and is used for solving the problems of insufficient brain state perception, poor adaptability and low robustness of a brain organism rehabilitation system in related technologies. The method comprises the following steps: synchronously acquiring multi-modal physiological signals by cooperating with physiological signal monitoring, task induction and regulation and control equipment modules, removing artifacts through preprocessing and redundancy check, extracting neuroplasticity characteristics, dynamically adjusting weight decoding brain activity intentions, regulating and controlling stimulation parameters in a closed loop, and training an optimization model in combination with a multi-center federation; accurate and highly-adaptive rehabilitation regulation and control are realized, and the rehabilitation effect and the model generalization are improved.

The invention discloses a time interference electrical stimulation system for suppressing, controlling and adjusting, which comprises an image acquisition module for acquiring functional magnetic resonance images to acquire functional connection data; the stimulation module is used for respectively generating time interference electrical stimulation waveforms in the plurality of stimulation areas so as to stimulate the corresponding stimulation areas and adjusting stimulation parameters; the evaluation module is electrically connected with the image acquisition module and the stimulation module, acquires cognitive emotion evaluation data, and obtains an emotion and cognitive neural circuit regulation result by combining the emotion evaluation data and the function connection data as an inhibition control capability evaluation index; and the control module is electrically connected with the stimulation module and the evaluation module, and controls the stimulation module to adjust the stimulation parameters based on the emotion and cognitive neural circuit adjustment result. According to the system, effective time interference electrical stimulation is carried out on a plurality of inhibition control intervention brain regions, stimulation parameters are improved by implementing stimulation effect evaluation, and a favorable adjustment means is provided for inhibition control of multi-region and complex network modulation.

The invention discloses a method and a related device for closed-loop optimization of a lower limb motor imagery experiment normal form through dynamic mind training, and relates to the field of rehabilitation medicines.The method comprises the steps that electroencephalogram signals of a testee when the testee executes a mind fusion lower limb motor imagery task are collected and preprocessed, and preprocessed electroencephalogram signal data are obtained; based on a dynamic feature extraction technology of a sliding window, calculating electrophysiological indexes in real time; performing weighted fusion on the motor imagery definition, the concentration degree and the correctness degree through predefined weights to generate a comprehensive state score; on the basis of the comprehensive state score, dynamically adjusting a normal feeling and task stimulation parameter applied to the testee; based on the collected electroencephalogram signals, extracting optimal perception motion rhythm and cross-frequency coupling characteristics; and inputting into the trained two-dimensional time convolution network decoding model to obtain the lower limb motor imagery intention of the testee. According to the method, the evoked rate and the stability of the electroencephalogram characteristics of the stroke patient in the lower limb motor imagery task can be improved.

The invention relates to an electrical stimulation detection system and method for evaluating rectum sensation and anal sphincter reflex functions. The system comprises a stimulation unit which comprises a program-controlled electrical stimulator and an intra-rectum stimulation electrode used for applying monopulse electrical stimulation to the rectum wall; a recording unit including an anal sphincter response recording device for capturing anal sphincter reflex activity; and the central control and data processing unit is used for coordinating electrical stimulation, reaction recording and data analysis. The method comprises the following steps: arranging a stimulating electrode and recording equipment; applying monopulse electrical stimulation to the rectum wall; synchronously recording the reflective reaction of the anal sphincter; and performing quantitative analysis on the stimulation parameters and the reaction signals to determine a reflection threshold value, an incubation period, an amplitude and duration time. According to the system and the method, objective evaluation on the integrity of the reflex arc of the anorectum and the afferent sensitivity of the rectum feeling is realized through accurately controlled electrical stimulation and objective reaction quantification, and a reference basis is provided for diagnosis and rehabilitation evaluation of related dysfunctions.