51 results about "Neural regulation" patented technology

The invention relates to the technical field of brain-computer interfaces, and provides a brain-computer interface instruction issuing method, device and equipment based on regulation enhancement simulation, and the method comprises the steps that an electroencephalogram decoding model comprises an encoder, a feature enhancer and a task classifier, the encoder encodes a real-time electroencephalogram signal to obtain compression representation before nerve regulation, and the feature enhancer is used for classifying the compression representation before nerve regulation; the feature enhancer performs feature enhancement on the compression representation to obtain enhanced representation, and the task classifier classifies the enhanced representation to obtain an electroencephalogram decoding result. According to the method, a feature enhancer is obtained by combining training of a state discriminator based on a sample electroencephalogram signal collected before nerve regulation and a real state label after nerve regulation, and the feature enhancer is driven to learn a feature migration relation between a compression feature before nerve regulation and a feature after nerve regulation; the feature characterization capability of an electroencephalogram decoding model on electroencephalogram signals is remarkably improved, so that the decoding robustness on weak stimulation signals is enhanced on the premise of not depending on high-intensity external stimulation.

The invention discloses a cognitive enhancement training method and system based on brain signal pattern recognition. The method comprises the following steps: acquiring offline brain signal data of a user, preprocessing the offline brain signal data, and inputting the preprocessed offline brain signal data into a pre-selected decoding model for model training so as to construct a decoding model for the user; based on multiple preset cognitive states, constructing task adjustment logic to perform bidirectional adjustment on the cognitive training task for each cognitive state; in the cognitive training process of the user, on-line brain signal data of the user is decoded based on the decoding model so as to obtain the current cognitive state of the user, and the next cognitive training test is adjusted in real time according to task adjustment logic so as to perform EEG neural regulation and fNIRS neural regulation on the user and further perform cognitive enhancement on the user. According to the method, the current cognitive state of a user is recognized in real time by pre-constructing a decoding model, and bidirectional adjustment is performed on a task according to preset task adjustment logic, so that cognitive enhancement is performed on the user.

The invention discloses a convolutional neural network information processing method for heart function dynamic monitoring, and belongs to the technical field of medical detection and monitoring. Comprising the following steps that multi-mode electrocardiosignal data and current physiological state parameters of a patient are obtained, and initial configuration of the intelligent heart function monitoring device is obtained; determining the signal quality grade of each signal channel, and generating a dynamic filtering adjustment strategy of the multi-modal signal acquisition module; configuring a feature extraction strategy of a double-branch convolutional neural network module based on the signal features of the target analysis signal segment and the physiological state parameters; performing multi-scale time sequence feature extraction and frequency domain autonomic nerve regulation feature extraction on the target analysis signal segment by using a double-branch convolutional neural network module according to a feature extraction strategy; executing arrhythmia classification, heart rate variability parameter quantification, heart function evaluation grade and graded early warning tasks, and outputting multi-stage intelligent early warning information from normal monitoring, potential risk and abnormal early warning to an emergency state.

The invention relates to the technical field of nervous system dysfunction, in particular to a nerve regulation and control system for degenerative disease treatment based on multi-modal physiological feedback, which comprises a central processing unit, an optical radiation applicator, a mechanical vibration applicator, an integrated biological signal sensing array and a man-machine interaction module, cooperative stimulation is applied by using a multispectral light source and a broadband vibrator, and multi-dimensional physiological signals such as heart rate variability, myoelectricity, galvanic skin and the like are monitored in real time through a sensor array. A multi-parameter adaptive control algorithm built in the central processing unit can dynamically and intelligently adjust stimulation parameters based on the feedback signals to form an accurate personalized treatment closed loop. Meanwhile, the safety monitoring module based on the biological thermal model ensures the safety boundary of the treatment process. According to the invention, intelligent, self-adaptive and non-invasive treatment of nerve dysfunction is realized.

The invention provides a transcranial magnetoacoustic stimulation closed-loop feedback method and system based on electroencephalogram guidance. The method comprises the steps that original electroencephalogram signals of a target brain area are collected and preprocessed; target brain region activity features are extracted based on the preprocessed electroencephalogram signals, and the neural activity state of the target brain region is judged according to the target brain region activity features; according to the state judgment result, initial stimulation parameters are generated and optimized in combination with a preset stimulation response model; the generated initial stimulation parameters are converted into actual stimulation signals, the transcranial magnetoacoustic stimulation device is controlled to execute ultrasonic and magnetic field stimulation on the target brain area, and closed-loop feedback is conducted according to the activity state of the target brain area. According to the method, the limitation of a traditional transcranial stimulation method in the aspects of real-time feedback, individualized adaptation, artifact interference and safety control is effectively solved, accurate, controllable and individualized intervention of the neural activity of the target brain region is achieved, and a repeatable, safe and efficient technical means is provided for nerve regulation and control research and clinical application.

The invention discloses a self-adaptive closed-loop ultrasonic stimulation system and method based on Actor-Critic reinforcement learning, and belongs to the field of neural regulation and control technologies and biomedical engineering, and the system comprises a signal collection module, a preprocessing module, an Actor strategy module, a Critic value module, an ultrasonic stimulation module, a safety monitoring module and a storage module. The Actor strategy module generates safe and controllable ultrasonic stimulation parameters according to the preprocessed electroencephalogram data; the safety monitoring module is used for checking whether the ultrasonic stimulation parameters generated by the Actor module meet safety standards or not; the ultrasonic stimulation module sends out an ultrasonic stimulation signal according to the ultrasonic stimulation parameter; and the Critic value module evaluates the electroencephalogram feedback effect after ultrasonic stimulation and provides feedback to the Actor module for optimizing the experimental strategy of the next step. The invention aims to intelligently adjust the ultrasonic stimulation parameters through the biofeedback data monitored in real time in combination with the reinforcement learning algorithm.

The invention relates to the technical field of biomedical engineering, in particular to a waking-up system based on multi-modal electroencephalogram characteristic dynamic evaluation and closed-loop regulation, which comprises a multi-modal electroencephalogram acquisition module for acquiring multi-modal original electroencephalogram signals; the multi-dimensional awakening related electroencephalogram feature fusion extraction module is used for extracting a multi-dimensional fusion feature vector; the consciousness level and waking-up response dynamic evaluation module is used for loading an off-line constructed and completed deep learning model, carrying out real-time dynamic evaluation and generating a waking-up response dynamic evaluation result; the personalized closed-loop awakening regulation and control decision module is used for generating and dynamically updating a closed-loop awakening regulation and control scheme by adopting an awakening personalized self-adaptive regulation and control algorithm; and the multi-modal awakening regulation and control execution and man-machine interaction module is used for executing multi-modal awakening nerve regulation and control output and providing a visual dynamic evaluation result, awakening regulation and control parameters and a man-machine interaction interface for medical personnel and family members of the patient. Therefore, the problems of single electroencephalogram acquisition mode, one-sided electroencephalogram feature extraction and the like in the prior art are solved.

The application belongs to the technical field of biomedical engineering and neural regulation technology. A brain-heart linkage transcranial strong alternating current stimulation feedback control method and system are provided. Heart rate variability characteristics are obtained according to electroencephalogram signals, electroencephalogram signal characteristics are obtained according to the electroencephalogram signals, and a dynamic coupling index is determined according to the electroencephalogram signals and electrocardiogram signals. When the heart rate variability characteristics are greater than or equal to a corresponding heart rate characteristic baseline threshold, the electroencephalogram signal characteristics are greater than or equal to a corresponding electroencephalogram characteristic baseline threshold, and the dynamic coupling index is greater than or equal to a set threshold, it is determined that emotional disorders are improved, and the intensity of the stimulation current is unchanged. Otherwise, the intensity of the stimulation current is increased by a set threshold until the emotional disorders are improved. Through joint calculation of the electroencephalogram signals and the electrocardiogram signals, dynamic monitoring and accurate evaluation of the process of treating emotional disorders by using transcranial strong alternating current stimulation are realized, and the accuracy of the electric stimulation is ensured.

The invention relates to an ICU patient delirium risk early screening system based on AI electrocardiogram analysis. The system comprises a neural signal extraction module, a component mode determination module, a delirium type identification module, a risk trajectory prediction module and a risk alarm generation module, wherein the neural signal extraction module extracts neuromodulation related signal fragments based on an electrocardiogram signal sequence and a preset neuromodulation specificity index; a component mode determination module extracts time sequence features from the fragments and determines an abnormal component distribution mode; the delirium type identification module is used for matching potential delirium types through the feature database; the risk trajectory prediction module predicts a risk trajectory in combination with a long-short-term memory network; a risk alert generation module evaluates a risk level and generates an alert. With the adoption of the system, early and dynamic screening of delirium risks can be realized, the screening accuracy and timeliness are improved through multi-module collaborative analysis, and accurate monitoring support is provided for ICU (Intensive Care Unit) patients.

The invention relates to the cross technical field of biomedical engineering, artificial intelligence and neural regulation, and provides a cognitive fatigue evaluation and electrical stimulation intervention system based on an electroencephalogram-behavior joint AI model, comprising: a data acquisition and preprocessing module for synchronously acquiring electroencephalogram signals and behavior data of a user and performing data preprocessing; the real-time fatigue evaluation module is used for performing feature extraction and fusion on the preprocessed electroencephalogram signals and behavior data through an AI joint model and outputting a real-time fatigue evaluation result of the user; the AI joint model is constructed based on a transduction information maximization (TIM) algorithm and is used for realizing cognitive fatigue evaluation in a few-sample scene; and the closed-loop electrical stimulation intervention module is used for dynamically adjusting transcranial electrical stimulation parameters and executing targeted intervention according to the real-time fatigue evaluation result. Through multi-modal data fusion and closed-loop intervention, real-time accurate detection and personalized targeted intervention of the cognitive fatigue state are realized.

The invention relates to the technical field of daily chemicals, in particular to an anti-aging composition and application thereof. The composition is prepared from a white birch bark extract, snake venom peptide and Ectoine. The white birch bark extract promotes abnormal protein removal by activating an intracellular protein ubiquitination system, and improves the skin metabolism capability; the snake venom peptide slows down expression muscle contraction through a nerve regulation mechanism, and dynamic wrinkles are effectively faded; ectoin enhances cell membrane stability, protein protection and skin stress adaptive capacity, and under the synergistic effect of Ectoin, Ectoin and skin stress adaptive capacity, the comprehensive anti-aging effects of resisting wrinkles, tightening, relieving, brightening skin color and the like can be remarkably improved.

The embodiment of the invention provides a self-adaptive regulation and control signal generation system, a regulation and control system and regulation and control equipment, and relates to the technical field of medical equipment, and the system comprises an electrode which is connected with a designated position of a detection target, collects an original neural signal of the detection target in real time, and transmits the original neural signal to the regulation and control equipment; the regulation and control device obtains a to-be-detected neural signal of the detection target based on the original neural signal; performing anomaly detection on the to-be-detected neural signal; if the to-be-detected neural signal contains the abnormal features, calculating a disturbance impulse response signal based on the abnormal neural signal and the normal neural signal of the detection target; the disturbance impulse response signal represents disturbance generated when the normal neural signal is converted into the abnormal neural signal; and on the basis of the disturbance impulse response signal, a neural regulation signal for counteracting disturbance suffered by the normal neural signal is generated, the waveform characteristic of the neural regulation signal is that the amplitude of the sampling point adaptively changes along with the sampling point, adaptive real-time generation of the neural regulation signal is realized, and the real-time performance and effectiveness of neural regulation are improved.

The application discloses a transcranial electrical stimulation target parameter optimization method, system, device and computer readable storage medium, and belongs to the technical field of neural regulation and medical auxiliary equipment. The optimization method comprises the following steps: S1, acquiring a head medical image, and constructing a head finite element simulation model based on the medical image; S2, in the head finite element simulation model, defining a target stimulation brain area as a ventromedial prefrontal cortex area; S3, simulating transcranial electrical stimulation in the finite element simulation model, and through simulation calculation, taking the concentration of current density in the ventromedial prefrontal cortex area as an optimization target, and determining at least one transcranial electrical stimulation parameter; wherein the transcranial electrical stimulation parameter comprises an electrode position and / or an electrical stimulation waveform parameter. The optimization method changes the parameter selection from experience dependence to scientific calculation based on a biophysical model, improves the accuracy of the stimulation parameter, and thus realizes precise brain area targeted stimulation.

The application provides a minimally invasive intracerebral passive adhering brain-computer interface system based on a neuroendoscope, a minimally invasive neuroendoscope implanting module provides a channel and a visual field, and is an operation entrance of the system; a flexible passive chip is a function execution core, signal acquisition, stimulation or drug release are realized; a fixing and adhering module provides structural stability, and reliable long-term operation is ensured; and an external energy / signal coupling module constitutes an energy and information interaction interface, and closed loop control and feedback of the system are realized. Through space position, energy transmission and signal channels, the four modules form a multi-layer cooperative system, and finally precise implantation, stable work and remote control of the intracerebral flexible function chip are realized. The application adopts the above-mentioned minimally invasive intracerebral passive adhering brain-computer interface system based on a neuroendoscope, has good clinical operability and engineering expandability, and provides a reliable implementation path for local precise treatment, neural regulation and brain-computer interface application of central nervous system diseases.

The invention relates to the technical field of biological detection and toxicological evaluation, and discloses a neural behavior multi-parameter integration-based caenorhabditis elegans exposure evaluation method which comprises the following specific steps: S1, synchronous culture of caenorhabditis elegans; s2, pollutant exposure treatment; s3, multi-parameter behavior detection; s4, data standardization processing; and S5, constructing a comprehensive evaluation model, and performing weighted integration on four core parameters including the movement speed, the chemotactic index, the synaptic spot density and the calcium signal amplitude after standardization to construct a neurotoxicity index NTI. According to the method disclosed by the invention, a multi-layer and multi-dimensional neurotoxicity evaluation system is constructed by integrating four types of neurobehavior parameters including motion behaviors, chemotactic reactions, synaptic morphology and neuron calcium signals, and the defect that a traditional single endpoint index cannot comprehensively reflect overall disturbance of a neural regulation network is overcome; by utilizing the high homology of the nervous system of caenorhabditis elegans and the human nerve gene, the clinical correlation and mechanism analysis capability of environmental pollutant neurotoxicity evaluation are remarkably improved.

The invention discloses a neural signal control parameter migration system and method based on cross-species multi-modal information, belongs to the technical field of artificial intelligence and neuroscience, and aims to solve the problems that cross-species neural signal control parameters are difficult to migrate, the experiment efficiency is low and the individual modeling precision is insufficient. The system comprises a cross-species multi-modal data acquisition module, a cross-species spatio-temporal co-characterization calculation module and a cross-species parameter migration and fine adjustment module, and a cross-species spatio-temporal feature representation space is constructed by acquiring and preprocessing multi-modal neural data of mice and humans to eliminate distribution differences among species. Mouse experiment data are mapped into parameters suitable for human neural signal control and response modeling, and individualized optimization and strategy recommendation are carried out. According to the invention, multi-modal neural signal characterization and control parameter migration from mice to human beings can be realized, and intelligent and individualized development of brain-computer interfaces and neural regulation strategies is promoted.

The invention relates to a closed-loop tRNS system and method based on energy landscape analysis and brain state decoding, and the method comprises the steps: building an individualized model through offline calibration of an offline modeling unit, and generating a brain state decoding information table through energy landscape analysis; performing real-time electroencephalogram monitoring by using a signal acquisition module; then the online decoding and stimulation decision-making unit carries out real-time state decoding, and then the online decoding and stimulation decision-making unit triggers and applies tRNS; and finally, periodically evaluating the effect and adaptively optimizing parameters. According to the method, dynamic space-time precise regulation and control are achieved, static space positioning (right side apical leaf upward return) and a dynamic time window (specific brain state) are combined, a normal form of'fixed target and fixed parameters' of a traditional nerve regulation and control technology is broken through, and the regulation and control space-time precision is improved to a millisecond level and a network level.

The application discloses a brain regulation device, an electronic device, a medium and a computer program product. The device comprises a control module, a mechanical wave emitting module and a magnetic field generating module. The control module is configured to determine a first intensity and a second intensity corresponding to a regulation area of a target object based on an effective induced electric field intensity of the target object. The mechanical wave emitting module is configured to emit mechanical waves with the first intensity to the regulation area. The magnetic field generating module is configured to generate a magnetic field with the second intensity in the regulation area. The embodiments of the application are beneficial to improving the non-invasive neural regulation effect on patients.

The application provides a neural internal medicine rehabilitation training system and regulation method based on artificial intelligence, extracts the rhythm power of a stimulation pulse of a rehabilitation training device to a patient and the blood oxygen concentration of a target nerve point of the patient, classifies the efficiency of the remodeling level of the neural function in the rehabilitation training through the blood oxygen concentration and the rhythm power, and obtains a graded remodeling index of the neural function regulation in the rehabilitation training; determines the cortex excitability index of the patient in the neural internal medicine rehabilitation training through the motor evoked potential of the target patient muscle cortex, the stimulation frequency and the stimulation intensity of the stimulation pulse in the rehabilitation training device; multi-modal synergistically integrates the cortex excitability index and the graded remodeling index of the neural function regulation, obtains the neural regulation target parameter of the remodeling index in the rehabilitation training device, and then dynamically regulates the stimulation parameter in the rehabilitation training device based on the neural regulation target parameter. Based on the above scheme, multi-modal coupling regulation of the stimulation parameter in the rehabilitation training process can be realized.

The invention discloses an electroacupuncture stimulation scheme personalized generation method fusing myoelectricity and brain function network states, and relates to the technical field of nerve regulation and epilepsy electroacupuncture treatment.The method comprises the steps that alpha wave and beta wave power spectral density, a myoelectricity root-mean-square value and brain region metabolism and blood flow signals are collected, and electroacupuncture stimulation intensity, waveform and frequency are recorded; calculating a brain-muscle collaborative response index, judging brain-muscle pathway collaboration and stimulation effectiveness, and optimizing parameters; performing time-frequency analysis and waveform identification on the electroencephalogram signals in the effective stimulation state, extracting epilepsy sample discharge characteristics, calculating a power change rate, and adjusting stimulation parameters to form a safe stimulation parameter set; constructing a brain-muscle network state space, establishing a reinforcement learning initial model by taking BMEI as a reward and punishment function, and performing offline training to form a model M0; and introducing an online learning module to update the model in real time to form a second model M1, and outputting an individual optimal stimulation parameter Popt to realize safe, effective and individualized long-term treatment optimization.

The application relates to a system for realizing depression subtype classification processing based on deep learning multiple fusion brain network graph technology, wherein the system comprises the following modules: a data acquisition and processing module for acquiring resting-state functional magnetic resonance imaging data of a subject; a data preprocessing module for preprocessing the acquired data; a multiple functional brain network construction module for generating three functional connection matrices from obtained functional magnetic resonance imaging (fMRI) data and constructing a graph representation for each coefficient connected matrix; a multiple brain network graph fusion module for improving performance under small sample capacity by using a regularization term based on data enhancement and mapping each graph representation to a feature space by using GAT fusion area groups and difference pool area groups; and a depression subtype classification module for classifying depression subtypes. The application also relates to a corresponding method, device, processor and storage medium thereof. The system, method, device, processor and storage medium thereof help to further optimize the target intervention scheme of neural regulation technology in depression treatment.

The invention discloses a closed-loop binaural vagus nerve stimulation system integration and a closed-loop binaural vagus nerve stimulation method for neural rehabilitation. The system integration comprises a host, a binaural stimulation module, a rehabilitation training execution module and a myoelectricity acquisition module. The rehabilitation training execution module integrates a myoelectricity synchronous hemiplegic upper limb rehabilitation system (including a visual assistance and mirror image training subsystem), a dysphagia synchronous training system and a breathing pairing synchronous training awakening system. When the system works, a corresponding mode is started according to an evaluation result of a patient to guide the patient to execute a target training action through a healthy-side target muscle group; a myoelectricity acquisition module synchronously acquires a myoelectricity signal generated by the action; the host processes the signals, generates or adjusts electrical stimulation parameters of the vagus nerves of the two ears in real time according to the signals, and applies electrical stimulation in a target time window of action execution, thereby forming closed-loop nerve regulation and control with healthy-side myoelectricity as real-time feedback. According to the invention, accurate and personalized synchronization of multi-mode rehabilitation training and central nervous regulation and control is realized.

The application discloses a deep learning-based electroencephalogram slow wave real-time feedback transcranial electrical stimulation method and system, relates to the cross technical field of neural regulation and deep learning, and comprises the following steps: collecting electroencephalogram signals of a target object and pre-processing the electroencephalogram signals to obtain pre-processed electroencephalogram signals; inputting the pre-processed electroencephalogram signals into a pre-constructed deep learning model for analysis to obtain a latent representation representing the dynamic state of electroencephalogram slow waves; wherein the deep learning model is obtained through the collaborative training of three types of self-supervised targets, namely self-predictive representation, unsupervised target conditional reinforcement learning and inverse dynamics modeling; transcranial electrical stimulation parameters are decided based on the latent representation, wherein the stimulation parameters at least include a stimulation type, a stimulation intensity and a stimulation frequency; and corresponding transcranial direct current stimulation or transcranial alternating current stimulation is output to specific brain regions of the target object according to the transcranial electrical stimulation parameters.

The invention discloses a post-stroke limb movement rehabilitation nerve regulation and control method, system and device and a medium, and relates to the technical field of neural engineering and rehabilitation. The method comprises the following steps: identifying a current motor imagery intention from a current bio-electricity signal of a stroke patient and determining a target stimulation mode, a first stimulation nerve and a first stimulation pulse which are matched with the current motor imagery intention, the implantable main body controls the implantable stimulator to output a first stimulation pulse to the stimulation electrode channel corresponding to the first stimulation nerve according to the target stimulation mode; adjusting the amplitude of the stimulation pulse required by the next training according to a first feedback signal in the hand muscle group contraction process caused by the first stimulation pulse; and acquiring a new current bio-electricity signal and repeatedly executing the process until the early-stage rehabilitation training of the cerebral apoplexy is determined to be completed, and then executing the later-stage rehabilitation training of the cerebral apoplexy. According to the method, the stimulation selectivity and stability of peripheral nerves in cerebral apoplexy upper limb rehabilitation are improved, and precise regulation and control aiming at hand fine movement are achieved.

The application relates to the technical field of a brain-computer interface driven wearable adaptive brain-spinal cord synergistic neural regulation system, and particularly discloses a brain-computer interface driven wearable adaptive brain-spinal cord synergistic neural regulation system, which aims to solve the problems of brain and spinal cord regulation device synergistic stimulation, brain signal decoding based on a brain-computer interface, and a closed-loop adaptive neural regulation mechanism. The system comprises a brain-computer interface signal acquisition and decoding module, a multi-modal physiological state sensing module, and an adaptive brain-spinal cord synergistic neural regulation decision and execution module. The system realizes closed-loop adaptive neural intervention by collecting electroencephalogram and electromyogram signals in real time and dynamically generating brain and spinal cord synergistic regulation parameters. The application improves the individualization, precision and continuity of neural function repair, and has wearable integration and safety monitoring capabilities.

The invention discloses a chronic pain relieving method based on virtual reality and time interference nerve regulation, and relates to the technical field of nerve regulation and virtual reality medical treatment, and the method comprises the steps: constructing an individual function vulnerability quantitative model of a multi-dimensional index; generating a dynamic adaptive virtual reality scene synchronized with the pain rhythm and emotion based on the model; configuring double-frequency time interference electric field parameters of targeted thalamus and anterior clasp back; performing millisecond-level synchronization on cognitive emotion events in neural regulation and virtual reality, and performing closed-loop adjustment on intervention parameters based on gamma wave band phase amplitude coupling strength; the pain evolution trend is predicted through the long-short-term memory network, and the subsequent intervention strategy is optimized. According to the invention, multi-dimensional precise targeted intervention on chronic pain is realized, the safety, individual suitability and long-term curative effect of treatment are remarkably improved, and the problems of poor suitability and difficult maintenance of curative effect in chronic pain intervention of old people are solved.

The application discloses a dynamic multi-wavelength light stimulation neural regulation system and method based on a closed-loop brain-computer interface, and the system comprises a multi-wavelength dynamic light stimulation module, a variable-frequency stimulation control module, an electroencephalogram monitoring and feedback module and a central control and individualized algorithm module. The application realizes individualized precise stimulation through real-time electroencephalogram feedback and parameter self-adaptive adjustment, overcomes the limitations of traditional fixed parameters, and improves the neural regulation treatment effect. The application realizes non-susceptible treatment through the introduction of non-video frequency beat technology, eliminates the discomfort of frequency flicker, and improves the user use compliance. The application realizes deep and shallow brain area collaborative regulation through dynamic adjustment of RGB multi-wavelength combination and the introduction of 650nm red light, and expands the regulation effect. And a "stimulation-monitoring-analysis-adjustment" closed-loop system is constructed, intelligent self-adaptive regulation is realized, and the optimal effect can be maintained without manual intervention.

The invention discloses a cognitive state evaluation and regulation training integrated system and method for weight loss, and relates to the technical field of brain function regulation, the system monitors the attenuation degree of multiple beams of near-infrared light with the wavelength of 760 nm and 850 nm after the near-infrared light is emitted from the brain so as to calculate the concentration change value of oxyhemoglobin in the frontal parietal lobe area of the brain, and the concentration change value of the oxyhemoglobin in the frontal parietal lobe area of the brain is calculated. And inputting the oxyhemoglobin concentration change value into a long-short-term memory neural network fused with an attention mechanism, outputting a food desire state of an overweight / obesity user when the overweight / obesity user faces food picture stimulation, and further taking the right dorsal-lateral prefrontal lobe as a regulation and control target, and obtaining the food desire state of the overweight / obesity user through an exogenous or endogenous nerve regulation and control mode. The activity level of the brain region is enhanced, and the food desire state of the overweight / obese user is reduced. The diet control ability of the overweight user can be remarkably enhanced through multiple regulation and control training, the food desire is reduced, and then the effects of improving the ingestion behavior and reducing weight are achieved.

The application discloses a state evaluation and closed-loop feedback regulation method and system, the method comprising: step 1: acquiring the multi-modal physiological data of a user; step 2: performing feature extraction and attention allocation, outputting an original prediction score; weighting calculation is performed on the dynamic correction coefficient and the original prediction score, and a continuous instantaneous state score is obtained; step 3: based on the instantaneous state score, a feedback guide instruction for the multi-sensory dimensions of the user is generated and executed; when the instantaneous state score reaches a preset target state threshold, a sensory self-adaptive extinction mechanism is triggered. The application collects the brain waves and multi-modal physiological signals of a user, performs comprehensive evaluation by using a special interactive feedback program, converts boring brain electrical data into visual, audible and tactile perception real-time dynamic feedback, and realizes closed-loop digital neural regulation.

The invention relates to an electroencephalogram high-order connection analysis method based on a time-spectrum super network, which is technically characterized by comprising the following steps of: constructing an electroencephalogram stimulation experiment group, collecting multichannel electroencephalogram data in a stimulation process, preprocessing multichannel electroencephalogram signals, constructing a brain time-spectrum super network, performing feature extraction and quantization on the brain time-spectrum super network, and analyzing the electroencephalogram high-order connection of the brain. Time domain connection strength features and spectral domain graph topological features are obtained and analyzed, a classification model is constructed to distinguish subjects with effective and ineffective spinal cord stimulation, and correlation between the features and clinical behavior scores is revealed through statistical analysis. According to the method, a multi-channel electroencephalogram signal acquisition and graph signal processing method is combined, and the brain network response process in a real application scene can be simulated, so that the objectivity and the interpretability of neural regulation effect evaluation are improved, the quantitative characterization function of brain time-spectrum collaborative dynamics under the neural regulation condition is realized, and the neural regulation effect evaluation accuracy is improved. And a new technical means is provided for quantitative analysis of complex brain function state changes.