228 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 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 a device for risk assessment of nervous system diseases, and a processor of the device is configured to receive eye movement data and electroencephalogram data which are associated in a time sequence, and fuse a double-branch deep neural network in a model through multiple modes to assess the risk of the nervous system diseases, performing feature extraction and attention mechanism-based cross-modal feature fusion on the eye movement data and the electroencephalogram data to obtain cross fusion features; and outputting risk assessment results and risk assessment parameters of at least one type of nervous system diseases through an output layer of the multi-modal fusion assessment model. According to the method and the device, the problem that diagnosis sensitivity and universality are insufficient due to the fact that diagnosis only depends on a single information source in related technologies is solved, disease features can be more comprehensively constructed from two levels of neural activity and behavior response by fusing data of two modes of eye movement and electroencephalogram, parallel diagnosis is further performed for multiple types of diseases, and the diagnosis efficiency is improved. The sensitivity and applicability of neuropsychiatric diagnosis are improved, and particularly, the method has more remarkable accuracy in early stages such as mild cognitive impairment and the like.

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 discloses an electroencephalogram data processing method for electroencephalogram glasses, which comprises the following steps: acquiring original electroencephalogram signals acquired by the electroencephalogram glasses, synchronously acquiring multi-mode auxiliary signals such as eye movement, acceleration and myoelectricity, and monitoring and acquiring quality parameters in real time; forming an initial data set containing the original electroencephalogram signal, the multi-mode auxiliary signal and a quality mark; taking the initial data set as input, and outputting a processed data set containing pure electroencephalogram signals and neural activity features through preprocessing, multi-modal artifact separation and personalized feature extraction in sequence; and packaging the processed data set and processing process meta-information thereof into a standard format file, executing local structured storage and cloud synchronous storage, recording a data operation log through a block chain technology, ensuring data security through an encryption technology, and completing whole-process data management.

The invention discloses a three-dimensional convolution method for decoding an imaginary language electroencephalogram topographic map. The method solves the problem that for a multi-rhythm electroencephalogram topographic map, an existing convolutional network method is insufficient in modeling capability, so that the decoding precision of the multi-rhythm electroencephalogram topographic map in an imaginary language is remarkably reduced. In the aspect of rectangular BEAM reconstruction, a frequency band specificity self-adaptive variation Kriging interpolation model is built, an optimal variation function is automatically selected according to spatial variation characteristics of electroencephalogram power of each frequency band, a high-resolution BEAM graph sequence under multiple frequency bands and multiple time steps is built, and spatial continuity and CNN structure adaptability are considered. A double-branch three-dimensional convolutional neural network is designed, high-dimensional feature extraction is performed on space-time and space-frequency band tensors, a multi-scale structure and dynamic expression ability of neural activity are mined, discrimination of two feature vector branches is dynamically weighted, and the recognition ability of a classifier to a language imagination electroencephalogram mode is 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.

The invention discloses an MEG data bad segment detection method based on a variational auto-encoder and clustering, and belongs to the technical field of magnetoencephalogram data processing. The method comprises the following steps: collecting cranial nerve activity original data, extracting time domain features and frequency domain features, inputting the time domain features and the frequency domain features into a variational auto-encoder, and converting the time domain features and the frequency domain features into low-dimensional potential variables; taking the sum of the reconstruction loss and the KL divergence as a total loss function in the variational auto-encoder, and minimizing the total loss function through a back propagation algorithm; extracting low-dimensional potential variables in the variational auto-encoder, and performing clustering analysis on the low-dimensional potential variables by using a trained clustering algorithm to distinguish bad segments from good segments; and decoding the MEG bad segment data identified by the trained clustering algorithm back to the original feature space. According to the method, the robust performance is shown in the low-signal-to-noise-ratio, large-source and multi-source environments, and the strong robustness and the excellent imaging performance are shown.

The invention provides a brain wave detection data processing method and system based on AI, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly, obtaining an original electroencephalogram signal set composed of a plurality of segments of electroencephalogram signal sequences which are continuously collected through multiple channels and have time stamps, and then carrying out the quality optimization processing of the original electroencephalogram signal set; the method comprises the following steps: acquiring an effective electroencephalogram signal set according with a detection standard, then performing feature extraction processing on the effective electroencephalogram signal set to obtain an electroencephalogram feature combination reflecting a neural activity mode, and then calling a pre-trained electroencephalogram analysis model to perform mode recognition processing on the electroencephalogram feature combination to obtain a neural activity model. An electroencephalogram detection result containing the abnormal activity time period identifier and the corresponding brain region positioning information is generated, finally, an electroencephalogram processing instruction containing space-time information is generated based on the electroencephalogram detection result and sent to the target device to trigger response operation, and the accuracy and efficiency of electroencephalogram detection are 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.

The invention provides a clinical motion control device, method and system, electronic equipment and a medium, and the device comprises an ultra-micro array implantation probe which is configured to be implanted into a brain motor cortex of a patient and collect a neuroelectric signal of the brain motor cortex; the integrated data processing module is electrically connected with the ultra-micro array implanted probe and is configured to convert the neuroelectric signals into digital signals and perform neural decoding on the digital signals to obtain motion control signals containing the motion intention of the patient; and the data transmission module is in communication connection with the integrated data processing module and is configured to transmit the motion control signal to external rehabilitation equipment through a wired interface or a wireless communication mode, so that the external rehabilitation equipment drives the limbs of the patient to execute corresponding rehabilitation motions. According to the invention, large-scale, high-precision and synchronous monitoring can be carried out on brain motor cortex neural activities, the signal acquisition quality is improved through an integrated structure design, and the signal decoding precision is improved at the same time.

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 intracranial pressure monitoring, in particular to a multi-modal fusion-based intracranial pressure dynamic prediction method for a patient with craniocerebral injury, which comprises the following steps of: acquiring an electroencephalogram signal, extracting a peak value to a termination section path, judging whether a waveform slope is consistent with an intracranial pressure direction or not, identifying an unsynchronized response segment, and obtaining a neural migration section mark set. According to the method, the non-cooperative state between the neural activity and the pressure change is distinguished by recognizing the periodic waveform which does not cause the intracranial pressure response in the electroencephalogram signal, and the time period with the inconsistent regulation rhythm is positioned by combining the continuous expression of the ventricular volume and the arterial pressure direction deviation; the method comprises the following steps of: extracting response sequence and direction characteristics of arterial pressure, cerebral blood flow and neural signals in a continuous section, enhancing a rhythm corresponding relation among multi-modal signals, adjusting a signal alignment structure according to offset direction difference and a rhythm starting point, forming a linkage fragment sequence under a unified time reference, and obtaining a multi-modal signal sequence; and linkage and deduction of asynchronous response information on a structural level are promoted.

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.

The invention relates to the technical field of medical equipment, in particular to an intelligent closed-loop vagus nerve stimulation regulation and control method and system. The method comprises the following specific steps: collecting an electroencephalogram signal and a stimulation signal during real-time stimulation, and obtaining an electroencephalogram signal prediction value of future N steps through an ambulatory electroencephalogram signal prediction model; calculating a depression biomarker estimated value for representing the brain activity state at the next moment according to the electroencephalogram signal predicted values of the next N steps; calculating an optimal stimulation signal at the next time according to a difference value between the depression biomarker target value and the depression biomarker estimated value; and updating the stimulation signal according to the optimal stimulation signal to stimulate the vagus nerve, and updating the stimulation signal. Meanwhile, the invention discloses a system for executing the method, the optimal stimulation parameter is calculated according to the difference between the predicted brain activity state at the next moment and the target state, so that the neural activity is controlled, and the adjusting efficiency and the control precision of the percutaneous vagus nerve stimulation system are improved.

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 lower limb dyskinesia adjusting equipment and a gait adjusting method, and the equipment comprises a wearing part which comprises a housing matched with a lower limb; the projection module comprises a projector arranged on the shell, and the projection angle of the projector is adjustable; the control unit comprises a control mainboard arranged in the shell and an acquisition module communicating with the control mainboard, and the acquisition module is configured to acquire gait data of the wearing piece in real time; the projector communicates with the control mainboard, and the control mainboard is configured to control and adjust the projection angle of the projector based on the gait data acquired by the acquisition module. The lower limb dyskinesia adjusting device prompts gait disorders by using vision, and the mechanism is that the lower limb dyskinesia adjusting device can provide external sensory cues for the patient and stimulate the visual system of the patient, so that neural activity of the premotor area is activated. Visual information is used for guiding gaits, and the deficiency of internal movement rhythm caused by basal ganglion dysfunction is made up.

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.