31 results about "Local field potential" patented technology

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 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.

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.

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.

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.

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 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.

The invention provides a basal ganglion Beta rhythm regulation and control device and system based on reinforcement learning, and relates to the field of neural engineering.The basal ganglion Beta rhythm regulation and control device comprises an STN electrode and an intelligent regulator, and the STN electrode is connected with the intelligent regulator; the intelligent regulator is configured to obtain a local field potential signal collected by the STN electrode in real time, and extract a Beta rhythm amplitude at the current moment from the local field potential signal; constructing a current state based on the Beta rhythm amplitude at the current moment and the Beta rhythm amplitude at the previous moment, and inputting the current state into a trained stimulation current recommendation model to obtain the optimal stimulation current intensity; applying the stimulation current intensity to the STN electrode to perform basal ganglion Beta rhythm regulation and control; wherein the stimulation current recommendation model adopts a soft actor-commentator algorithm in reinforcement learning, takes a negative value of a deviation between a Beta rhythm amplitude at the current moment and a target value as a reward function, takes stimulation current intensity as an action, and regulates and controls a Beta rhythm in the brain to an expected level in a closed loop based on reinforcement learning.

The invention relates to a data time synchronization method and system. The method comprises the following steps: performing pulse modulation processing by using an implantable pulse generator to obtain a modulated target pulse, and acting the modulated target pulse on a target object; acquiring a local field potential signal of the target object by using a deep brain stimulation electrode; a body surface detection signal is collected through a body surface potential sensor; wherein the body surface detection signal is a signal formed by spreading the target pulse in the human tissue of the target object; and determining a synchronous correction parameter between the local field potential signal and the body surface detection signal by using the body surface potential sensor, and applying the synchronous correction parameter to the body surface detection signal to realize data time synchronization between the local field potential signal and the body surface detection signal. By adopting the method, the data synchronization precision can be improved.

The application relates to a data time synchronization method and system. The method comprises the following steps: performing pulse modulation processing by using an implanted pulse generator to obtain a modulated target pulse, and applying the modulated target pulse to a target object; collecting a local field potential signal of the target object by using a deep brain stimulation electrode; and collecting a body surface detection signal by using a body surface potential sensor; wherein the body surface detection signal is a signal formed by the target pulse propagating in the human body tissue of the target object; determining a synchronization correction parameter between the local field potential signal and the body surface detection signal by using the body surface potential sensor, and applying the synchronization correction parameter to the body surface detection signal to realize data time synchronization between the local field potential signal and the body surface detection signal. The method can improve data synchronization accuracy.

A method for determining an efficacy of medication treatment for a patient includes determining, by one or more processors, based on a local field potential (LFP) activity of the patient, when the patient takes medication and / or a duration of when the medication is effective. The method further includes outputting, by the one or more processors, an indication of when the patient takes the medication and / or the duration of when the medication is effective to facilitate a treatment for the patient.

The present disclosure relates to a closed-loop neuromodulation system based on sacral nerve electrophysiological activity monitoring. The closed-loop neuromodulation system includes an implantable neuromodulation module, a programming module, and a server module. The implantable neuromodulation module obtains monitoring data based on an implantable electrode and synchronously obtains a local field potential variation signal generated in a patient with urinary tract dysfunction during the sacral nerve conduction, and applies an electrical stimulation pulse to the sacral nerve of the patient when an abnormal monitoring result of nerve afferent impulses associated with urinary urgency symptom of the patient is obtained based on the local field potential variation signal. The server module stores the monitoring data and sends an intelligent recognition model that is pre-trained to the implantable neuromodulation module and the programming module, respectively.

This article discusses a neurostimulation device for monitoring electrical neural activity when connected to an implantable electrode. The neurostimulation device includes sensing circuitry and signal processing circuitry. The sensing circuitry is configured to sense a patient's local field potential (LFP) signal when connected to the implantable electrode, and the signal processing circuitry is operatively coupled to the sensing circuitry. The signal processing circuitry is configured to calculate the power spectral density (PSD) of the sensed LFP signal, calculate the slope of the PSD of the sensed LFP signal, and use the calculated slope of the PSD of the sensed LFP signal to determine the patient's physiological state.

The application discloses a kind of biological fusion type three-dimensional nerve electrode, including depth sensing unit and two parts of plane sensing unit.Local field potential and deep brain electrical complex signal can be acquired simultaneously in cortical surface, and it has good biocompatibility and compliance.The maximum length of plane sensing unit is 5-20mm, and the thickness is less than 100μm.To ensure the accuracy of the nerve electrode, the minimum diameter of the sensing circuit is less than or equal to 50μm, the diameter of the depth sensing unit is less than or equal to 75μm, and the implantation depth range is 2-5mm.The number of sensing channels in the plane sensing unit part is 16-50, and the number of sensing channels in the depth sensing unit part is 25-60.During the manufacturing process, microchannels and other coupling structures are reserved on the plane sensing unit, and the integrated manufacturing of the nerve electrode is realized by printing and assembling from bottom to top layer by layer.