42 results about "Local field potential" patented technology

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 discloses a local field potential signal generation method and system based on an improved diffusion model. The method comprises the following steps: acquiring and preprocessing an original noise-containing signal, timestamp information, a multi-channel behavior signal or a stimulation signal; performing multi-head self-attention calculation on the preprocessed data based on a Transform encoder to obtain a potential state of the LFP sequence in a potential space; carrying out potential variable sampling according to the potential state, carrying out continuous ODE solution and discrete GRU rule updating on the obtained initial potential variable, and generating a potential state sequence evolved along with time; performing forward noise addition and reverse noise reduction sampling on the potential state sequence based on a diffusion model to obtain a final potential sequence; and mapping a potential sequence finally obtained by the diffusion model back to an LFP signal of a preset dimension, and outputting LFP time sequence data generated by simulation. According to the invention, high-fidelity, controllable-condition and stable-structure neural signal generation is realized.

The invention relates to the technical field of nerve regulation and control, and discloses a nerve regulation and control method based on dynamic frequency band analysis, which is used for improving the accuracy of epileptic seizure judgment. The neural regulation and control method based on dynamic frequency band analysis comprises the following steps: acquiring a local field potential signal of a target brain region in a preset time period; performing dynamic frequency band analysis on the local field potential signal to obtain a pathological wave frequency band, a high-frequency oscillation band, a harmonic coupling band and a suppression wave band; calculating the energy ratio and the cross-band phase coupling strength of each feature sub-band, and generating a multi-dimensional feature vector; the epileptic seizure probability is calculated according to the multi-dimensional feature vector; and when the epileptic seizure probability is greater than a preset threshold value, selecting a corresponding stimulation parameter group according to the dominant characteristic sub-band type, and outputting an electrical stimulation pulse with a frequency band matching characteristic.

The invention discloses a multifunctional neural electrode for intracranial pressure detection and closed-loop nerve regulation, the multifunctional electrode sequentially comprises a PMUT electrode for intracranial pressure detection and nerve regulation and a macro-micro electrode for electroencephalogram signal detection from top to bottom, and the PMUT electrode is bonded on the macro-micro electrode. The PMUT electrode is bonded to the macro-micro electrode to serve as a multifunctional electrode, the PMUT electrode can serve as a pressure sensor and can also serve as an ultrasonic transducer, the dual functions of intracranial pressure detection and fixed-point ultrasonic nerve stimulation are achieved, the macro-micro electrode can achieve detection of local field potential and peak potential, and the ultrasonic nerve stimulation function is achieved. Therefore, a detection-feedback-regulation closed-loop system is realized.

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 embodiment of the application provides a kind of based on difference filtering neural signal action potential detection adaptive compression method and related equipment, belong to biological signal detection technical field.The method includes obtaining high flux neural signal, wherein, high flux neural signal includes at least one potential signal, potential signal includes action potential and local field potential;Based on the difference operation detection of high flux neural signal to high frequency narrow pulse mutation characteristics, obtain the action position information of action potential in high flux neural signal;According to action position information, high flux neural signal is carried out event-driven adaptive variable rate compression, and compressed data is obtained.The embodiment of the application can realize the accurate detection of action potential, while greatly reducing resource occupation.

The invention relates to a control parameter determination method and device of deep brain stimulation equipment, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring multi-source physiological data of a target user by using an in-vitro multi-mode sensor array, and acquiring multi-channel local field potential sensing data of the target user by using deep brain stimulation equipment; determining fusion feature information of the target user based on the multi-source physiological data and the multi-channel local field potential sensing data, and determining state information of the target user based on the fusion feature information; and determining control parameters of the deep brain stimulation equipment according to the state information, wherein the control parameters are used for controlling the deep brain stimulation equipment to work. By adopting the method, the working efficiency of deep brain stimulation equipment can be improved.

An electrical non-destructive real-time cell monitoring device according to an exemplary embodiment of the present invention comprises: a first electrode in contact with a cell; a second electrode provided at a position spaced apart from the first electrode; an impedance measurement unit connected to the first electrode and the second electrode; an LFP measurement unit connected to the first electrode and the second electrode; and a collected signal processing unit connected to the impedance measurement unit and the LFP measurement unit so as to receive and process measurement results, wherein the impedance measurement unit applies an alternating current to the second electrode so as to measure the impedance between the first electrode and the second electrode, the LFP measurement unit measures a local field potential between the first electrode and the second electrode and outputs a first digital signal, the impedance measurement unit and the LFP measurement unit operate simultaneously, and the collected signal processing unit processes a first digital signal so as to obtain a second digital signal.

The invention discloses a differential low-noise integrated biosensor array for cell culture detection and a preparation method thereof, and the differential low-noise integrated biosensor array integrates an ISHFET device, an REHFET device and an EXHFET device, so that extracellular action potential signals and local field potential signals can be detected during in-vitro cell culture; therefore, interference of factors such as tissue trauma and immune response on cell activity signal detection is avoided, and good reliability is realized; the ISHFET device and the REHFET device can form a differential sensing unit, so that the effect of reducing the detection error is realized; the EXHFET device not only can detect extracellular action potential signals, but also can independently detect local field potential signals, so that more diversified cell activity signals can be detected, and more data support is provided for in-vitro cell culture research. The method is widely applied to the technical field of semiconductors.

A method and apparatus are provided for determining stimulation parameters for DBS. The method includes receiving first data indicating a value of a local field potential (LFP) over a first time period from first contacts positioned adjacent a brain region. The method further includes receiving third data indicating a value of a LFP over second time periods from the first contacts, where the brain region is stimulated by second contacts over second time periods based on stimulation parameter values. The method further includes determining a frequency band encompassing a difference between a first frequency spectrum of the first data and a second frequency spectrum of the third data. The method further includes determining a value of the second frequency spectrum over the frequency band for each stimulation parameter value. The method further includes determining a value of an optimal stimulation parameter based on the value of the second frequency spectrum and each stimulation parameter value.

The invention discloses a Parkinson's disease dyskinesia recognition system based on cross-frequency coupling tensor decomposition, and the system comprises a signal collection and processing module which is used for synchronously collecting and preprocessing local field potential signals of multiple sites of a brain; the cross-frequency coupling calculation module is used for calculating a cross-frequency coupling map of each data slot; the signal-to-noise ratio balance processing module is used for generating an average cross-frequency coupling map of each tested individual; the tensor decomposition feature extraction module is used for aggregating the average cross-frequency coupling maps of all the tested individuals, constructing a third-order tensor and decomposing the third-order tensor to obtain three tensor components; moreover, when the average cross-frequency coupling atlas of the tested individual to be tested is input, weight vectors corresponding to the three tensor components are output; and the classification and identification module is used for inputting the weight vector of the tested individual to be tested into a pre-trained machine learning classification model and outputting an identification result of the dyskinesia of the Parkinson's disease. According to the method, the problem of frequency drift existing among different individuals can be solved, and the recognition accuracy is improved.

The present application relates to the technical field of data processing, and especially relates to a brain-computer interface data preprocessing method and device. The present application provides a brain-computer interface data preprocessing method, which comprises separating action potential signals from local field potential signals in original signals, performing variable rate compression on the local field potential signals in the local field potential channel, dividing the local field potential channel into a key channel and a compression channel, respectively compressing the data of the key channel and the compression channel to different degrees, and recombining data streams to reconstruct the compressed data. The present application utilizes the correlation between channels to jointly compress multi-channel data, condenses common information to more completely preserve at a higher rate, and combines the original information of each channel at a low rate and high compression ratio to maximize multi-channel data compression. Meanwhile, the Kalman method is used to combine high-frequency low-precision data and low-frequency high-precision data to realize the ability of high-frequency high-precision reconstruction.

The invention provides an implantable closed-loop nerve stimulation system and a stimulation parameter regulation and control method thereof, and the method comprises the steps: carrying out the impedance measurement through applying a dual-frequency test current to a plurality of electrodes, and obtaining a first composite impedance signal; obtaining a three-dimensional space impedance gradient based on the space coordinate of each electrode in the plurality of electrodes and the corresponding impedance value; according to the three-dimensional space impedance gradient and a first impedance change rate determined based on the first composite impedance signal, regulating and controlling a stimulation parameter of the electrical stimulation pulse; impedance measurement is carried out in a first time period after the electrical stimulation pulse is applied, a second composite impedance signal is obtained, and a first local field potential signal of an area where the multiple electrodes are located is obtained in the first time period; and when the second composite impedance signal satisfies a first condition, and / or the first local field potential signal satisfies a second condition, calibrating the regulated stimulation parameter.

Disclosed are a system for neural stimulation, a device for neural stimulation, and a computer-readable storage medium. The system includes a neural stimulator, the neural stimulator includes a stimulation module, configured to: determine, based on a received local field potential signal, a first energy value of a frequency band of interest in the local field potential signal; determine a threshold range in which the first energy value is; and output, according to a stimulation protocol corresponding to the threshold range, a corresponding stimulation signal. According to technical solution of the embodiments of this disclosure, a closed-loop neural stimulation mode can be achieved.

The embodiment of the invention provides a self-adaptive compression method based on difference filtering neural signal action potential detection and related equipment, and belongs to the technical field of biological signal detection. The method comprises the steps that high-throughput neural signals are obtained, the high-throughput neural signals comprise at least one potential signal, and the potential signals comprise action potentials and local field potentials; performing difference operation detection on the high-throughput neural signal based on high-frequency narrow pulse mutation characteristics to obtain action position information of the action potential in the high-throughput neural signal; and performing event-driven adaptive variable rate compression on the high-throughput neural signal according to the action position information to obtain compressed data. According to the embodiment of the invention, the action potential can be accurately detected, and meanwhile, resource occupation is greatly reduced.

Various embodiments of the present technology generally relate to closed loop deep brain stimulation based on inferred sleep stage from physiological data using machine learning classifiers. Some embodiments, for example, may use subthalamic nucleus (STN) deep brain stimulation (DBS) to treat advanced Parkinson's Disease motor symptoms and improve sleep by identifying sleep stages commensurate with clinician-scored polysomnography (PSG). The DBS may be adapted to include a novel artificial neural network (ANN) that triggers targeted stimulation in response to inferred sleep state from STN local field potentials (LFPs) recorded from implanted DBS electrodes. A feedforward neural network can be trained to prospectively identify sleep stage with PSG-level accuracy. In some embodiments, the machine learning model stored within the DBS may also adapt stimulation during specific sleep stages to treat targeted sleep deficits.

The invention provides a real-time removal method, system and device for electrical stimulation artifacts and a storage medium, and relates to the technical field of signal processing, and the method comprises the steps: collecting a local field potential signal of a deep brain region, carrying out the baseline removal of the local field potential signal through a high-pass filter, and obtaining a preprocessed potential signal; the rising edge of the preprocessed potential signal is monitored on line, an amplitude threshold value is set, and a low-amplitude trailing artifact segment and a high-amplitude stimulation artifact segment in the preprocessed potential signal are jointly judged; and performing artifact removal on the low-amplitude trailing artifact segment by adopting a template subtraction method to obtain a first signal, and processing a high-amplitude stimulation artifact segment in the first signal by adopting an interpolation method aiming at the high-amplitude stimulation artifact segment to obtain a restored reconstruction signal. According to the method, the electrical stimulation artifacts can be removed efficiently and accurately in real time, so that the quality and the accuracy of signals are improved.

Provided herein is a mesoscopic electrophysiological system including a structural system sized and configured to be attached to a head of a patient and to hold a plurality of intracranial electrode shafts in predetermined positions around the head of the patient, a plurality of mesoscopic intracranial electrode shafts that includes a first plurality of electrode contacts configured to measure local field potentials (LFPs) at a plurality of locations in a brain of a patient, wherein each electrode shaft of the plurality of intracranial electrode shafts includes a second plurality of electrode contacts, and a plurality of guide pins sized and configured to receive the plurality of mesoscopic intracranial electrode shafts positioned therein.

The application discloses a Parkinson's disease dyskinesia identification system based on cross-frequency coupling tensor decomposition, comprising: a signal acquisition and processing module, which is used for synchronously collecting local field potential signals of multiple sites of the brain and performing pretreatment; a cross-frequency coupling calculation module, which is used for calculating a cross-frequency coupling atlas of each data segment; a signal-to-noise ratio balance processing module, which is used for generating an average cross-frequency coupling atlas of each subject; a tensor decomposition feature extraction module, which is used for aggregating the average cross-frequency coupling atlas of all subjects, constructing a third-order tensor, and decomposing to obtain three tensor components; and when the average cross-frequency coupling atlas of a to-be-tested subject is input, a weight vector corresponding to the three tensor components is output; and a classification and identification module, which is used for inputting the weight vector of the to-be-tested subject into a pre-trained machine learning classification model, and outputting an identification result of Parkinson's disease dyskinesia. The application can overcome the frequency drift problem existing between different individuals and improve the identification accuracy.

This invention discloses an odor decoding method based on olfactory electroencephalogram (EEG) signals, comprising the following steps: inputting a training set into a random forest model for training to obtain a trained random forest model; inputting the sample to be tested into the trained random forest model and outputting the classification result; each sample is a dimension-reduced feature matrix corresponding to a target characteristic gas. The method for obtaining the dimension-reduced feature matrix includes: depriving a rat model of water for 12-15 hours; stimulating the rat model with a gas containing a target characteristic gas; and synchronously acquiring the local field potential signal of the rat model based on an array electrode within 5 seconds after stimulation; and obtaining the dimension-reduced feature matrix based on the local field potential signal. The odor decoding method based on olfactory EEG signals of this invention has the advantages of high specificity, high sensitivity, strong real-time performance, and portability, with a response time ≤1s and a decoding accuracy of up to 94%.

Physiological signals, such as local field potential signals, are sensed by an implantable medical device and displayed for a clinician to view during the provision of aspects of stimulation therapy. In one example, a local field potential signal that has been sensed during an automatic capture of a threshold to be used for stimulation therapy, such as deep brain stimulation, may be displayed, where the automatically captured threshold is displayed with the physiological signal. In another example, a local field potential signal is displayed with a stimulation signal such that the relationship between the local field potential signal and the stimulation signal is visible, and this display of the signals may be done in real time while the implantable medical device is capturing the signals and transmitting them to the external device.

The present invention relates to a cell chip for measuring local field potential (LFP), the chip comprising: an electrode array in which a reference electrode and a plurality of working electrodes are spaced apart from each other; transmission lines connected to respective electrodes; and a terminal pad part, wherein the surface of each working electrode is modified with a conductive nanomaterial to reduce interfacial impedance and improve sensitivity to electrical signals.

The invention discloses a multi-modal joint evaluation method for the repairing effect of an eye-brain assembly, and belongs to the technical field of biomedical engineering and neuroscience. The method aims at solving the problems that an existing evaluation means cannot reflect function integration in real time in a living body state, a specific index is lacked to distinguish effective remodeling and disordered growth, and a single behavioral test is prone to interference, so that false positive is caused. Specific visual stimulation is presented to a transplantation receptor, local field potential of a primary visual cortex is synchronously collected, and Low Gamma frequency band signals are extracted to calculate direction selectivity indexes and brain network topological attributes; meanwhile, a hierarchical visual behavioral test for sensing spatial cognition from basic light is carried out; and finally, constructing a binary decision matrix based on thresholding scores of electrophysiology and behavioral indexes, and outputting a four-classification quantitative evaluation conclusion including effective functional repair, thereby realizing accurate and standardized judgment of the visual cortex repair effect.

The object of the invention is based on a flexible skin layer and intra-skin array of graphene solution gated field effect transistors (gSGFETs) capable of recording ultra-slow signals as well as signals in the typical local field potential bandwidth. The object of the invention is based on a graphene transistor system for measuring electrophysiological signals, comprising a processing unit and at least one graphene transistor (gSGFET) comprising graphene as a channel material contacted by two terminals, a tunable voltage source on the drain and source terminals of the transistor (gSGFET) referred to as gate voltage, and at least one filter configured to acquire a signal from the transistor and split the signal into at least two frequency bands, a low frequency band and a high frequency band, wherein a first signal and a second signal are amplified with a gain value, respectively.

The invention discloses a brain-computer interface decoding system, a training method and a decoding method, and belongs to the technical field of brain-computer interfaces. In order to solve the problem that a peak signal and a local field potential signal have differences in signal characteristics and information expression, the system constructs a plurality of parallel and mutually cooperative decoding branches, and comprises a first decoding module for decoding the peak signal, a second decoding module for decoding the local field potential signal, and a third decoding module for decoding the local field potential signal. And the fusion decoding module is used for fusing the peak signal and the local field potential signal, and finally, the decoding results of the three decoding modules are synthesized through the comprehensive decision module for judgment. In the process, through multi-branch cooperative decoding and multi-level feature fusion, complementary information of the spike signal and the local field potential signal on the spatial-temporal scale can be effectively mined, and the accuracy and reliability of brain-computer interface decoding are improved.

Provided is a drug discovery support device provided with a processor which acquires measurement data of the extracellular potentials of nerve cells, including local electric field potentials, and selects true synchronous bursts and false synchronous bursts on the basis of the intensity in a frequency band relating to the local electric field potentials.

The invention discloses a brain-computer interface local field potential decoding system, a training method and a decoding method, and belongs to the technical field of brain-computer interfaces. A parallel architecture of an LMP time domain branch and a spectrum power frequency domain branch is constructed, LMP time domain features and spectrum power frequency domain features are subjected to VMD decomposition to obtain a plurality of intrinsic mode functions as corresponding key features, deep feature extraction and fusion are achieved through a feature extraction module and Transform, and finally time sequence decoding is completed through a decoder. Fine time-frequency characteristic changes related to user intentions in the LFP signals can be captured, the time domain response of the LFP signals to neural activities is reserved, and group rhythm synchronization information reflected by spectrum power is accurately extracted; on the basis, cross-scale and cross-feature collaborative modeling is realized through Transform, and deep analysis of neural signals can be realized, so that the decoding precision and the system robustness are remarkably improved.

Disclosed are a Parkinson's disease sample feature typing method, a feature typing system, and a sample classification model training method based on a local field potential. The typing method includes: calculating a power spectral density of a Beta-band signal of a local field potential of a sample to obtain an average power spectral density curve, and performing peak analysis on the average power spectral density curve to obtain a peak power spectral density; separately modeling a periodic element and an aperiodic element of an average power spectrum to extract a periodic power spectrum and an aperiodic component; calculating a phase-amplitude coupling feature of the Beta-band signal of the local field potential of the sample; and performing feature clustering analysis, and outputting sample typing results. In the disclosure, a multidimensional data model is constructed, and classification accuracy and reliability are improved.

The application discloses a deep brain nerve stimulation system based on adaptive adjustment, and relates to the technical field of deep brain nerve regulation, and comprises the following steps: collecting a local field potential signal of a target region in the deep brain of a target patient, extracting a beta-band power spectral density and a gamma-band phase synchronization index as a neural activity characteristic parameter; determining an individual baseline value and a preset threshold value based on historical data, outputting a stimulation adjustment trigger signal when the beta-band power spectral density exceeds the individual baseline value and the gamma-band phase synchronization index is lower than the preset threshold value; calculating an optimal stimulation parameter combination by using a gradient descent optimization algorithm and performing neural regulation in response to the trigger signal; and monitoring a signal change after regulation, calculating a relative change rate and updating the threshold value. Through a double-parameter joint judgment mechanism and a threshold value updating strategy, the application realizes individualized adaptive adjustment of stimulation parameters, and improves stimulation accuracy and treatment effect.

This application provides an implantable closed-loop neurostimulation system and a method for regulating its stimulation parameters. The method includes: measuring impedance by applying a dual-frequency test current to multiple electrodes to obtain a first composite impedance signal; obtaining a three-dimensional spatial impedance gradient based on the spatial coordinates of each electrode and its corresponding impedance value; regulating the stimulation parameters of an electrical stimulation pulse according to the three-dimensional spatial impedance gradient and a first impedance change rate determined based on the first composite impedance signal; measuring impedance during a first time period after applying the electrical stimulation pulse to obtain a second composite impedance signal, and acquiring a first local field potential signal of the region where the multiple electrodes are located during the first time period; and calibrating the regulated stimulation parameters when the second composite impedance signal satisfies a first condition and / or the first local field potential signal satisfies a second condition.