94 results about "Neural regulation" patented technology

The invention belongs to the technical field of brain-computer interfaces, and particularly relates to a brain-computer interface driven emotion recognition and self-adaptive regulation and control system which comprises a multichannel nerve-peripheral coupling module, an emotion intensity probability mapping module and a closed-loop nerve regulation and control current module. The multi-channel nerve-peripheral coupling module is used for realizing overall quantification of central and peripheral emotional physiology; the emotion intensity probability mapping module is used for generating continuous emotion probabilities ranging from 0 to 1 through normalization and nonlinear mapping by utilizing emotion energy and combining eye movement fatigue and electroencephalogram entropy; and the closed-loop nerve regulation and control current module is used for dynamically adjusting the transcranial electrical stimulation intensity within the safety current upper limit according to the difference value between the emotion probability and the expected target. According to the invention, the recognition precision, the response speed and the use comfort are obviously improved.

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 real-time fatigue monitoring system based on an EEG signal, and relates to the technical field of neural regulation and control, and the system comprises a chaos reconstruction module which collects an original EEG signal, carries out the phase-space reconstruction of the original EEG signal through a chaos attractor processor, generates a chaos trajectory tensor, and carries out the phase-space reconstruction of the original EEG signal when the Lyapunov index of the chaos trajectory tensor exceeds a preset index threshold value, outputting a low-speed frequency band oscillation component and a high-speed frequency band oscillation component; and the closed-loop termination module is used for terminating the physical light field projection when the continuous detection periods of the updated alertness drop probability value, cognitive overload probability value and physiological fatigue probability value are all lower than a preset safety threshold value. Through multi-scale recursion phase space reconstruction, an original EEG signal is mapped to a three-dimensional chaotic trajectory tensor space, and dynamic frequency band decoupling is triggered by using a Lyapunov exponent threshold.

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 relates to the technical field of traditional Chinese medicine acupuncture and moxibustion, in particular to a human body acupuncture and moxibustion scheme intelligent screening method and system based on the brain-computer interface technology. Inputting the nerve response characteristic parameters into an acupoint efficacy prediction model, outputting nerve regulation efficacy scores of the acupoints, and generating an initial acupuncture scheme; in the process of executing the initial acupuncture scheme, electroencephalogram signals are collected in real time, and a neural feedback intensity value is calculated; and when the feedback intensity is lower than a preset response baseline, triggering a dynamic weight optimization algorithm, updating the scoring model and generating an optimized acupuncture scheme. According to the invention, objective evaluation and dynamic optimization of the human body to different acupoint nerve responses are realized, and the method has the advantages of high intelligence, high real-time performance, excellent individualized adaptation capability and the like, and is suitable for intelligent decision system deployment for assisting acupuncture therapy.

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 discloses a transcranial alternating current stimulation system and a transcranial alternating current stimulation method based on event-related potential double-component alignment. The system comprises an individualized feature extraction module, a dynamic phase mapping module, a real-time synchronous triggering module and a closed-loop feedback module, wherein the individualized feature extraction module extracts an ERP component latency period and a dominant oscillation frequency based on task state electroencephalogram data; the dynamic phase mapping module establishes phase alignment of an ERP component time window and a tACS waveform to realize a brain oscillation control process; the real-time synchronous triggering module calculates an evoked potential incubation period and a visual presentation incubation period of a corresponding task item, accurately matches a peak value of the evoked potential with a corresponding peak value of a tACS waveform, reads a current phase of the tACS waveform in advance, performs prospective prediction, adjusts the starting time of each stimulation, and realizes millisecond-level stimulation synchronization; the closed-loop feedback module monitors the brain state on line and dynamically corrects alignment parameters to ensure that the stimulation effect is stable; according to the method, accurate phase alignment of tACS and brain endogenous ERP components is realized, neural activities in a specific cognitive process can be effectively enhanced or inhibited, and the accuracy and individualization level of neural regulation and control are improved.

The invention relates to the technical field of nerve regulation and control, and provides a closed-loop ultrasonic brain-computer interface regulation and control method for improving post-stroke dysfunction, which comprises the following steps: S1, constructing integrated closed-loop ultrasonic brain-computer interface equipment which comprises a behavior control unit, a signal generator unit, a power amplifier unit and an electrophysiological signal monitoring unit; s2, constructing a mouse model, and embedding an electrode provided for the electrophysiological signal monitoring unit for monitoring; s3, dividing the mouse model into a random stimulation group and a brain-computer interface treatment group, and simultaneously performing closed-loop ultrasonic stimulation for preset time; s4, after closed-loop ultrasonic stimulation for a preset time, performing various behavioral test evaluations including representation of a sensory movement function and representation of an emotional emotion function; and S5, carrying out neural circuit remodeling evaluation, and observing the projection change of the high-order thalamus PO nuclei region of the mouse to the primary cortex. In the aspects of targeting, closed-loop control and integrated design, the method is expected to become a novel and effective post-stroke dysfunction treatment method.

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 task state brain-computer interface training system for closed-loop transcranial magnetic stimulation, and particularly relates to the field of cognitive rehabilitation assistion.The task state brain-computer interface training system comprises the steps that multi-channel electroencephalogram signals and brain function activation records are synchronously collected in the rehabilitation training process, task segments are divided, and frequency domain energy characteristics of all channels are extracted; identifying a state transition candidate segment based on the frequency band energy distribution change; the method comprises the following steps: constructing a cross-channel phase synchronization matrix, and inputting a graph convolutional neural network to generate an activation topology vector reflecting a brain region cooperation mode; the system constructs an activation state recognizer by taking a topological vector and a brain function activation label as supervision data, and realizes real-time judgment of different function states. The recognition result drives a stimulation parameter recommendation module, a preset stimulation strategy mapping table is inquired according to the recognition state, recommended stimulation parameters are dynamically generated and transmitted to a magnetic stimulation execution interface, and personalized closed-loop nerve regulation and control are achieved.

The invention provides a brain-computer interface-based TMS and VR collaborative rehabilitation training system, which is characterized in that a multi-mode collaborative positioning module is used for collecting VR interaction data and electroencephalogram data and determining the optimal stimulation state of a subject; the motion intention analysis module is used for matching a rehabilitation mode according to the collected data and in combination with the type of the subject; and the closed-loop rehabilitation interaction module is used for adjusting parameters of the TMS equipment according to the rehabilitation mode and stimulating the final stimulation area under the optimal stimulation state of the subject. For a mild stroke patient, the system dynamically monitors the plastic change of the autonomic nerve and adaptively adjusts the treatment parameters according to the plastic change of the autonomic nerve; for a severe stroke patient, the virtual reality situation is combined to effectively induce motor imagery and synchronously collect and analyze electroencephalogram signals, closed-loop nerve regulation is achieved, synchronous accurate intervention and curative effect optimization of the severe stroke patient are achieved, and an intelligent solution is provided for rehabilitation treatment of the nerve function of the severe stroke patient.

The present invention discloses a closed-loop neural regulation system based on the medication and movement state of Parkinson's patients, including: a parameter setting module for determining the low beta frequency band, the high beta frequency band, the upper threshold and the lower threshold; a signal acquisition module for collecting and preprocessing the local field potential signal of the STN of Parkinson's patients; a feature calculation module for calculating the low beta frequency band energy and the high beta frequency band energy through short-time Fourier transform, and calculating the average value of the ratio of the two; a judgment output module for comparing the average value with the upper threshold and the lower threshold; if it is greater than the upper threshold, it is judged that the patient is in the drug failure-movement state and outputs high-intensity stimulation; if it is greater than the lower threshold and less than the upper threshold, it is judged that the patient is in the drug failure-resting state and outputs medium-intensity stimulation; if it is less than the lower threshold, it is judged that the patient is in the drug effective state and outputs low-intensity stimulation. The present invention can achieve precise closed-loop deep brain stimulation based on medication and movement state.

The invention discloses an ear acupoint closed-loop ultrasonic stimulation system and method based on sleep electroencephalogram signal feedback, and belongs to the technical field of ultrasonic stimulation nerve regulation and control. The ear acupoint closed-loop ultrasonic stimulation system comprises an electroencephalogram acquisition module, a regulation and control module, an ultrasonic transmitting module and a dynamic association module; the electroencephalogram acquisition module is used for acquiring electroencephalogram signals within limited time specified by a user according to specified sampling frequency, amplifying and filtering the acquired signals and sending the processed signals to the regulation and control module; the regulation and control module judges the current sleep stage of the user, drives the ultrasonic transmitting module, performs variable parameter closed-loop ultrasonic stimulation on the user, and automatically adjusts stimulation parameters and duration according to the current sleep stage; and the dynamic association module sets a multi-threshold triggering mechanism based on the real-time electroencephalogram characteristic parameters and discriminates a specific physiological status. According to the invention, accurate acupoint stimulation can be realized, a real-time feedback mechanism is provided, and the accuracy and effectiveness of stimulation can be ensured.

The invention discloses a sympathetic cervical spondylosis autonomic nerve regulation enhancing method based on a brain-computer interface, and relates to the technical field of nerve regulation and repair. According to the method, by synchronously collecting electroencephalogram and skin conductance signals and conducting time-frequency domain conjoint analysis and Lempel-Ziv complexity evaluation, accurate quantitative evaluation on sympathetic nerve activity and nerve disorder degree is achieved, double-model collaborative decision-making of machine learning and deep reinforcement learning is utilized, personalized nerve regulation parameters are intelligently generated, and the method has the advantages of being high in accuracy and high in accuracy. High-targeting and safe intervention is implemented through double-target hierarchical regulation and control aiming at neck sympathetic nerves and vagus nerves, and stimulation parameters are dynamically optimized in a closed-loop manner by adopting a genetic algorithm on the basis of real-time feedback of heart rate variability, skin conductance and autonomic nerve balance indexes, so that the stimulation accuracy is improved. Therefore, the sympathetic cervical spondylosis autonomic nerve regulation efficiency and effect are enhanced.

The invention relates to a neural function detection and regulation method and system for a magnetic stimulation coil, and the method comprises the steps: driving a capacitor through a control circuit to discharge to a dual-path stimulation coil, and generating a millisecond pulse magnetic field; utilizing the pulsed magnetic field to act on nervous tissue to generate a nerve regulation effect comprising suprathreshold stimulation and subthreshold stimulation; acquiring evoked potential signals generated by stimulation and induction of the pulsed magnetic field in real time; the evoked potential signal is analyzed, and the nerve conduction time and excitation inhibition ratio is calculated; according to the nerve conduction time and the excitement inhibition ratio, a double-path stimulation time sequence and / or a capacitor discharge parameter are / is adjusted, detection and cooperative regulation and control of the nerve function are carried out, and the purposes of real-time detection, quantitative evaluation and self-adaptive precise regulation and control of the nerve function are achieved.

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 invention relates to a nerve regulation and control method based on multi-target point cooperation, and the method comprises the following steps: building a dual-channel photocurrent model, building a quick response type photocurrent model for a PV channel, and building a continuous inhibition type photocurrent model for an SOM channel; establishing an electromagnetic coupling model, determining an electromagnetic induction stimulation voltage according to a memory equation of the memristor, and coupling the light current and the electromagnetic induction stimulation voltage determined based on the dual-channel light current model to obtain a total current; performing multi-target cooperative control effect analysis on the electromagnetic coupling model, and optimizing off-line parameters to obtain an off-line optimal parameter combination; and performing nerve regulation based on the electromagnetic coupling model after the offline parameter optimization, and performing online optimization of the operation parameters according to the result of the nerve regulation. Compared with the prior art, the method has the advantages of being capable of achieving accurate regulation and control of different target spots and the like.

The invention discloses a non-invasive human sleep nerve regulation and control method and a non-invasive human sleep nerve regulation and control system. The method comprises the following steps: acquiring a first physiological signal of a user, and performing real-time sleep staging on the user to judge a deep sleep stage of the user; on the basis of a sleep staging result, when the user is in a deep sleep state, slow wave power detection is carried out through the second physiological signal; the phase angle of the slow wave is calculated according to the slow wave state, so that tACS electrical stimulation is carried out when the phase angle meets a preset condition; after the tACS electrical stimulation is carried out for a period of time, a third physiological signal of the user is obtained, and the physiological active state of the user is obtained by analyzing the third physiological signal; and when the physiological active state is an uplink state, performing TMR sound stimulation. Therefore, through mutual cooperation of tACS electrical stimulation and TMR sound stimulation, non-invasive nerve regulation and control on the user are jointly realized, and the nerve regulation and control effect on sleep is improved.

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.