133 results about "Neural activity" patented technology

The invention discloses a Parkinson's dyskinesia individualized SCAN network positioning method based on a multi-modal image and deep learning. The method comprises the steps of obtaining multi-modal medical image data, preprocessing the multi-modal medical image data, obtaining a multi-modal structure image and functional connection data, and calculating a spontaneous neural activity index of a whole-brain voxel level; taking a priori brain region related to the spontaneous neural activity index and dyskinesia as a seed point, constructing a seed point voxel function connection graph representing individual brain function connection, and performing nonlinear feature fusion and extraction through the deep learning network model; the bilinear attention network is adopted to capture the interaction information of the feature data and the individual dyskinesia symptom which is significantly related, an individualized SCAN network positioning result is obtained, the structure-function coupling characteristics of the individual brain are comprehensively described, the cross-modal pathological features related to the dyskinesia can be more sensitively recognized, and the accuracy and accuracy of the diagnosis and treatment of the dyskinesia can be improved. And the accuracy and robustness of abnormal brain region detection are obviously improved.

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 embodiment of the invention discloses an ultrasonic nerve regulation and control system. The system comprises an ultrasonic transducer array, a signal generation module and a power amplification module, wherein the signal generation module is used for determining a target nerve rhythm in response to a nerve regulation instruction for a target brain region, and determining a target ultrasonic parameter matched with the target nerve rhythm from each candidate ultrasonic parameter; the signal generation module is also used for generating an ultrasonic signal according to the target ultrasonic parameter; the power amplification module is used for amplifying the ultrasonic signal to obtain an amplified ultrasonic signal; and the ultrasonic transducer array is used for outputting ultrasonic waves to the target brain region according to the amplified ultrasonic signals so as to induce neural activity matched with the target neural rhythm in the target brain region. According to the technical scheme provided by the embodiment of the invention, the fineness of nerve regulation and control can be improved.

The invention relates to an acupuncture brain machine interface system based on brain-like recursive network model decoding. The system comprises an electroencephalogram acquisition module; an electroencephalogram preprocessing module; the needling parameter fitting module is used for accurately fitting needle body motion parameters through a polynomial regression method; the state space model is used for accurately simulating the nonlinear change characteristic of the neuron membrane potential according to the needle body motion parameters output by the acupuncture parameter fitting module; the brain-like recursive network model is used for depicting time dependence of neural activities of a single brain region under the acupuncture action and correlation of neural activities among different brain regions; a Kalman filtering generator; and the neural decoder is used for performing behavior decoding on the optimal internal state output by the brain-like recursive network model updated by the Kalman filtering generator. The system integrates electroencephalogram signal acquisition, acupuncture parameter acquisition, brain nerve activity modeling and acupuncture manipulation decoding, can perform real-time online operation, and is high in time resolution and high in response speed.

A dichoptic visual stimulus is presented to a user's eyes by presenting a first visual stimulus to a first eye of the user, the first visual stimulus being presented with a first modulation, and presenting a second visual stimulus to a second eye of the user, the second visual stimulus being presented without the first modulation. The first visual stimulus and second visual stimulus are configured to cause the user to perceive the first visual stimulus and the second visual stimulus as a single visual element, and to perceive the visual element as being unmodulated by the first modulation. Neural signals are obtained from a neural signal capture device configured to detect neural activity of the user. In response to determining, based on the neural signals, that the first eye is focused on the first visual stimulus, a computing system executes a command associated with the dichoptic visual stimulus.

The invention discloses a dynamic image motion correction method based on a graph neural network, and the method comprises the following steps: a), constructing a target region trajectory tracking model based on the graph neural network, and achieving the high-precision motion trajectory modeling; b) analyzing an image displacement mode in real time through a dynamic discrimination algorithm; and c) realizing adaptive image motion correction based on the target area track features. According to the method, the problem of complex motion trail modeling limitation caused by dependence on fixed template matching in a traditional method is innovatively solved, and the problem of spatial-temporal characteristic aliasing caused by unsteady state deformation of nervous tissues is effectively solved. According to the technical scheme, the dependence on a hardware synchronization signal acquisition module is eliminated, the resource configuration requirement of the edge computing equipment is remarkably reduced, and meanwhile, the multi-scale time sequence integration efficiency is improved. According to the method, dynamic imaging reconstruction of subcellular neural activities can be realized, and dynamic change details in a target neuron issuing process can be accurately restored.

Systems and methods associated with mind / brain-computer interfaces are disclosed. Certain implementations may include or involve processes of collecting and processing brain activity data, such as those associated with the use of a brain-computer interface that enables, for example, decoding and / or encoding a user's brain functioning, neural activities, and / or activity patterns associated with thoughts, including sensory-based thoughts, determining user attention and / or intentions during interactions within virtual environment and in other applications. Consistent with various aspects of the disclosed technology, systems and methods herein include and / or involve features and functionality enabling hands-free selection of UI elements in virtual environment or on other media.

The invention relates to the technical field of brain-computer interaction, in particular to a patient mental state assessment system for a clinician, which comprises a semantic manifold calibration module, a state trajectory projection module, a geometric curvature resolving module and an emotion transformation quantification module. According to the method, electroencephalogram data are collected and deeply analyzed, high-dimensional neural activity characteristics are converted into a visual three-dimensional state track in combination with titer and awakening degree information, continuous dynamic monitoring of the mental state of a patient is achieved, then the emotion conversion process is quantitatively analyzed according to the geometric curvature change rate of the state track, and therefore the mental state of the patient can be rapidly and accurately monitored. According to the method, rapid emotion fluctuation which is difficult to perceive in a traditional evaluation mode can be accurately recognized and marked, objective and fine data support is provided for clinical diagnosis, and the accuracy and instantaneity of mental state evaluation are remarkably improved.

A dichoptic visual stimulus is presented to a user's eyes by presenting a first visual stimulus to a first eye of the user, the first visual stimulus being presented with a first modulation, and presenting a second visual stimulus to a second eye of the user, the second visual stimulus being presented without the first modulation. The first visual stimulus and second visual stimulus are configured to cause the user to perceive the first visual stimulus and the second visual stimulus as a single visual element, and to perceive the visual element as being unmodulated by the first modulation. Neural signals are obtained from a neural signal capture device configured to detect neural activity of the user. In response to determining, based on the neural signals, that the first eye is focused on the first visual stimulus, a computing system executes a command associated with the dichoptic visual stimulus.

Methods of treating a subject for a dissociative disorder and methods of screening agents for the ability to modulate dissociative and associative states in a subject are provided. In particular, agents that alter rhythmic neural activity in the posteromedial cortex can be used to modulate dissociative and associative states in a subject.

Methods and systems are disclosed for modeling and decoding neural activities. A brain machine interface (BMI) measures neural activities. A processor receives signals corresponding to the neural activities from the BMI. The processor generates by applying a neural dynamics model signals corresponding to a de-noised state of the neural activities. By applying a BMI decoding model to the de-noised signals, the processor generates a control signal, which is used by a BMI plant model to generate a movement vector. An object is moved according to a predetermined path, in response to the movement vector and the current state of the object. In response to the object's movement, the BMI continuously measures the neural activities. Coefficients of the neural dynamics model and the BMI decoding model are iteratively updated, by executing a learning algorithm, in response to the BMI's continuous measurement.

The invention relates to a continuous writing track generation method, system and device based on a brain-computer interface and a medium. The method comprises the following steps: acquiring an electroencephalogram signal and a real writing track when a user imaginates writing through electroencephalogram acquisition equipment; extracting neural activity data related to hand writing movement by using a band-pass filter; extracting writing intention spatio-temporal features based on a preset spatio-temporal deep learning network; mapping an initial handwriting coordinate sequence and a pen state signal through a generator network comprising a full connection layer, a convolution time sequence generation layer and a pen state classification layer; calculating the similarity between the trajectory and a real trajectory by means of a discriminator, and generating handwriting and state signals through adversarial training optimization; and dividing stroke segments according to the pen state and sequentially connecting the stroke segments to generate a continuous writing track. By adopting the method, the brain motion intention can be efficiently and accurately decoded into a coherent writing track, and the practicability of a brain-computer interface in writing and interaction application is improved.

A computer-implemented method for decoding speech, language and related semantic neural activity includes: collecting neural signals from an array of electrodes implanted in or on a brain; extracting features from the neural signals to detect distributed signatures of linguistic encoding using non-contiguous coverage of the electrode array; and decoding linguistic units, including phonemes and semantic embeddings from the extracted features. The decoding can utilize a custom neural language model for a limited or impaired brain adapted from a generalized neural language model trained on other human brains with intact speech, linguistic and cognitive regions.

A method and system for detecting intentional selection of a user interface element using a binocular display. A first visual stimulus is presented stereoscopically to a user's eyes at a first virtual depth perceived by the user's depth perception and overlapping a first position within a field of view of the user. A second visual stimulus is presented stereoscopically to the user's eyes at a second virtual depth perceived by the user's depth perception and overlapping the first position. Neural signals are obtained from a neural signal capture device configured to detect neural activity of the user. In response to determining, based on the neural signals, that the user's eyes are focused on either the first visual stimulus or second visual stimulus, a computing system is placed into a first state or second state, respectively, associated with the first visual stimulus or second visual stimulus, respectively.

The invention relates to the technical field of medical assistance, in particular to a depression risk assessment method and system based on intestinal flora characteristics. The method comprises the following steps: acquiring an individual enteric microorganism sample, performing nucleic acid sequencing on the enteric microorganism sample, and determining abundance data of functional genes related to a neural activity metabolic pathway in enteric microorganisms; carrying out metabolite detection on the intestinal microorganism sample, and determining concentration data of metabolites related to the neural activity metabolic pathway in the intestinal microorganisms; based on the abundance data of the functional genes and the concentration data of the metabolites, data fusion processing is carried out, a neural activity metabolism function spectrum is constructed, and the neural activity metabolism function spectrum is a multi-dimensional feature vector; and inputting the neural activity metabolic function spectrum into a pre-trained risk assessment model, and outputting a depression risk score of the individual. The accuracy and repeatability of evaluation are remarkably improved, and the method has great potential for early screening, dynamic monitoring and personalized health management.

According to an aspect of the present inventive concept there is provided a system for determining an estimate of an anatomy of neural tissue of a subject. The system comprises an electrode arrangement comprising a plurality of electrodes, wherein different electrodes in the plurality of electrodes are configured to be arranged in different electrode locations adjacent the neural tissue, wherein the plurality of electrodes is configured to receive a plurality of stimulation signals, a sensor arrangement configured to generate indications of changes in at least one of a neural activity or a physiological activity in response to the stimulation signals and a processing unit, configured to receive data representing relations between the plurality of stimulation signals and the indications of changes in the neural activity or physiological activity, wherein the processing unit is configured to generate the estimate of the anatomy of the neural tissue of the subject.

Sensing an evoked response to electrical stimulation of target tissue of a patient in conjunction with an intra-luminal electrode. The intra-luminal electrode may be implanted in a blood vessel or similar lumen proximal to the target tissue and the sensed signals and / or delivered stimulation may pass through the blood vessel. or other lumen, walls. In some examples the evoked response may be an evoked compound action potential (ECAP), which may also be evoked resonant neural activity (ERNA). The electrical stimulation may elicit a measurable response indicative of a thought pattern or neural state that would otherwise be undetectable using a non-evoked biopotential.

Systems and techniques are provided for making genetically engineered ion channels (ICs) with bioluminescent resonance energy transfer (BRET) complexes and using such ICs for efficient readout of neural activity and output of biological neuronal networks.SOLUTION: In one implementation, the disclosed technology includes identifying a target location in the IC for expression of a target protein including a donor tag protein and an acceptor tag protein, and modifying the genome of the neuronal cell at a portion associated with the target location in the IC. The technique further includes causing the neuronal cell to express the target protein in the IC according to the modified genome. In the first (second) state of the IC, the donor tag protein is at a first (second) distance from the acceptor tag protein that is related to the absence (presence) of energy transfer between the donor tag protein and the acceptor tag protein.SELECTED DRAWING: Figure 3A-3C

The invention discloses a closed-loop analgesia method and system for regulating and controlling a secondary sensory cortex based on primary sensory cortex neural activity feedback. The system comprises a signal acquisition module used for acquiring neuron activity signals in the S1 region, a pain judgment module used for analyzing the signals and judging the pain state, a feedback control module used for generating stimulation control signals, and a stimulation execution module used for applying adjustable nerve regulation stimulation to the S2 region. According to the method, a specific pain nerve signal of S1 is monitored in real time and serves as a closed-loop feedback source, intervention on S2 is dynamically controlled, stimulation parameters are automatically started and optimized only when pain is detected, stimulation is automatically weakened or stopped after the pain fades away, and therefore self-adaptive, on-demand and accurate intelligent analgesia is achieved. According to the invention, accurate pain determination is realized by using a specific signal at the upstream of a pain pathway, personalized, efficient and low-energy-consumption intelligent analgesia is realized through closed-loop control, and a brand new solution is provided for chronic pain treatment.

The purpose of the present invention is to provide a novel method for targeting a neuron population that is a neural circuit element which has more limited functional localization. The purpose of the present invention is also to provide a method for treating a disease or the like by targeting a specific neuron population involved in a disease or the like by said novel method and controlling the neural activity thereof.　The present invention is a vector set used for targeting a specific neuron population in a neural circuit, said vector set being characterized by comprising first to third vectors, wherein: the first vector is an antegrade trans-synaptic virus vector that contains a base sequence which codes for a split-Cre recombinase consisting of a C-terminal-side sequence of a Cre recombinase; the second vector is a retrograde trans-synaptic virus vector that contains a base sequence which codes for a split-Cre recombinase consisting of an N-terminal-side sequence of a Cre recombinase; the third vector is a non-trans-synaptic virus vector that contains an inverted sequence of a gene sequence which codes for a desired protein and that has a loxP sequence and a loxP mutation sequence which face opposite to each other and which are at both ends of the inverted sequence; and a functional Cre recombinase is formed in a cell that has been infected with the first vector and the second vector.

This invention discloses a method and system for evaluating the effectiveness of photostimulation based on multimodal feature fusion, belonging to the field of big data analysis technology. The method and system include the following steps: S1, acquiring raw observation data and performing time axis relocation, dynamic denoising, normalization, and sliding time window segmentation; S2, evaluating the state of the stimulation process and selecting effective windows; S3, extracting neural activity response features and hemodynamic response features, and performing physiological response reliability assessment; S4, performing consistency fusion to evaluate the effectiveness of photostimulation. This invention effectively improves the stability and reliability of effectiveness evaluation during head-mounted photostimulation, solving the problems of difficulty in distinguishing between abnormal stimulation pathways and true brain tissue responses, as well as insufficient consistency of results across batches of experiments.

This invention provides a health monitoring method and system based on headphones. The method includes: acquiring multi-channel PPG signals from relevant areas of the user's ear using a photoplethysmography (PPG) sensor array integrated into the headphones; preprocessing the multi-channel PPG signals to remove motion artifacts and obtaining a target signal, extracting its time-domain and frequency-domain features; obtaining heart rate parameters from the time-domain features, body movement time series from the target signal, and neural activity assessment parameters from the time-domain and frequency-domain features; and finally obtaining a health status report through a health assessment model. By combining these methods with the user's daily headphone-wearing habits, physiological signal extraction is achieved without additional burden, solving the problem of discomfort associated with wrist-worn devices. By acquiring heart rate parameters, body movement time series, and neural activity assessment parameters, and combining them with a health assessment model, a closed-loop comprehensive health monitoring system is completed.

This specification describes systems and methods for using Zero Echo Time (ZTE) magnetic resonance imaging (MRI) sequences for applications to functional MRI (fMRI). In some examples, a system for functional magnetic resonance imaging includes a magnetic resonance imaging (MRI) scanner and a control console implemented on at least one processor. The control console is configured for executing, using the MRI scanner, a zero echo time (ZTE) pulse sequence; acquiring, using the MRI scanner, magnetic resonance data in response to the ZTE pulse sequence; and constructing at least one MRI image using the magnetic resonance data and measuring tissue oxygenation (PtO2)-related T1 changes as a proxy of neural activity changes of a subject using the at least one MRI image.

The application provides a personalized rehabilitation path making method and system based on amygdala function evaluation. First, the application obtains multi-modal neural function data of an individual. Second, the amygdala function lateralization mode is determined based on amygdala sub-region functional connection data. Third, the amygdala function lateralization mode is cooperatively analyzed with preset target individual behavior representation data. Finally, the personalized rehabilitation path is generated according to the cooperative analysis result obtained by the analysis. The technical scheme provided by the application not only realizes sub-region level accurate evaluation of amygdala function by integrating multi-modal neural function data and individual behavior representation data, but also generates a highly individualized rehabilitation path with precise coupling of neural activity and rehabilitation training, thereby effectively improving the pertinence and effect of intervention.

A method includes, for each corresponding electrode of one or more electrodes attached to a head of a patient, receiving corresponding electrical activity data representing neural activity within a corresponding area of a brain of the patient based on corresponding electrical activity measured by the corresponding electrode. The method also includes receiving stimulation data representing a plurality of stimulation times each corresponding to a stimulation applied to the patient while the one or more electrodes are attached to the head of the patient, and processing the corresponding electrical activity data and the stimulation data to determine a modulation depth for the patient. The method also includes assessing, based on the modulation depth, cognitive impairment of the patient due to a diagnosis of schizophrenia.

The invention provides a monitoring and awakening experiment system for a neurological severe coma patient. The monitoring and awakening experiment system comprises a multi-mode signal processing system, a medical image analysis system and a desktop remote monitoring system, the multi-modal signal processing system is used for identifying the neural activity category of a patient based on behavior visible light, human body infrared thermal imaging and electroencephalogram signal multi-dimensional data; the medical image analysis system obtains disease quantification information; and the desktop remote monitoring system is used for monitoring electroencephalogram signals of the coma patient, performing information management on each module, performing comprehensive analysis on a quantification result of the disease quantification module and an identification result of the abnormity identification module, and performing early warning and alarming on the disease.

The present application belongs to the technical field of medical signal processing and neural electrophysiological diagnosis, and particularly relates to a method for establishing an epilepsy diagnosis system based on time-varying brain network and evoked potential regulation. The present application aims to solve the problems that the prior art is difficult to capture the dynamic causal flow of neural activity, cannot accurately depict the transient reorganization characteristics of brain regions of juvenile myoclonic epilepsy (JME) patients, and lacks multi-modal feature integration analysis. The present application comprises the following steps: collecting whole brain electroencephalogram signals; extracting TMS evoked potential waveform after preprocessing; constructing a time-varying adaptive directional transfer function matrix; screening a time-varying brain network visualization atlas; performing baseline correction and superimposed averaging on the evoked potential; identifying the differences in connection and potential between JME patients and healthy controls and different clinical subgroups; and finally integrating the whole process to establish a standardized automated analysis system and a structured database. Through the method, the joint quantification of cortical excitability and time-varying causal connection is realized, and the objectivity, sensitivity and mechanism analysis ability of epilepsy diagnosis are significantly improved.

The invention belongs to the field of flight personnel state monitoring, and discloses a meta-consciousness ability training method and system based on a neural biofeedback technology. The system can obtain neural activity data directly related to the distraction state in a real and continuous task situation, hysteresis and unreliability caused by dependence on subjective reports or behavioral expressions are avoided, and the accuracy and objectivity of distraction monitoring are improved from the source. The decoding model is embedded into a closed-loop neural feedback training system, so that an acoustic feedback signal can be triggered in time when a distraction state occurs, a trainee is prompted to perceive attention deviation, and the self consciousness state is autonomously regulated and controlled without external guidance. The closed-loop feedback can strengthen a conditioned reflex mechanism paying attention to a regression task, and the self-monitoring and self-adjusting capability of meta-awareness is remarkably improved.

The invention discloses an electroencephalogram signal reconstruction method and system based on artifact removal. The method comprises the steps that multi-channel electroencephalogram signals are obtained and subjected to filtering preprocessing; blind source separation is carried out through independent component analysis, and statistical independent signal source components and a hybrid matrix are obtained through decomposition; constructing an equivalent current dipole model to calculate a space artifact index, evaluating signal complexity in combination with a time sequence sample entropy, and judging and identifying an artifact component and a neural activity component through weighted fusion and a self-adaptive threshold value; neural activity components are reserved, artifact components are zeroed, and pure electroencephalogram signals are reconstructed through mixed matrix inverse transformation. According to the method, accurate identification and effective removal of the electroencephalogram artifacts are realized, and high-quality data are provided for electroencephalogram analysis.

This application relates to an auxiliary analysis system based on neural activity synchronization, comprising: a cooperative task output module that outputs a multi-person cooperative task; a task execution module that adjusts the synchronous participation of each target object in the cooperative task within the same time window based on the multi-person cooperative task; a data acquisition module that collects brain function signals and behavioral data of each target object during the execution of the multi-person cooperative task; an interference control module that collects interference information; a group neural activity synchronization calculation module that, based on the collected brain function signals, behavioral data, and interference information of each target object, quantifies the neural activity synchronization of any two target objects during the cooperative task to obtain a neural activity synchronization index; a group synchronization index and a group synchronization network structure are obtained based on the neural activity synchronization index; and a risk monitoring module that obtains auxiliary analysis results. This system can achieve objective auxiliary identification, dynamic assessment, and closed-loop optimization of ASD risk status.