27 results about "Stimulus pattern" patented technology

A peripheral nerve stimulator can be used to stimulate a peripheral nerve to treat essential tremor, Parkinsonian tremor, and other forms of tremor. The stimulation pattern may alternate between the nerves. Improved stimulation algorithms can incorporate at least one of tremor feedback, external data, predictive adaptation, or long-term monitoring data. The device may be configured as a wrist worn device to stimulate three nerves to provide tremor reduction.

Techniques for using a biological and artificial neural network (BANN) system to perform a task. The BANN system comprises a multi-electrode array (MEA); a biological neural network (BNN) comprising neurons arranged on the MEA, a trained statistical model trained using inputs generated using responses of the BNN to training data inputs; and at least one processor. The method comprises using the BANN system to receive an input signal; encode the input signal to generate a stimulation pattern; stimulate the BNN by using the MEA to generate electrical signals in accordance with the stimulation pattern; measure, using the MEA, a response of the BNN responsive to the stimulating; generate, based on the measured response, an input for the ANN; process the input with the trained statistical model to obtain corresponding output; and use the output from the trained statistical model in furtherance of performing the task.

An adaptive stimulation system optimizes pain relief, pleasure enhancement, and healing through closed-loop physiological effectiveness monitoring. The system comprises a stimulation device, sensors measuring physiological responses indicative of stimulation effectiveness, and a processor implementing real-time adaptive control. The system measures unconscious physiological responses that users cannot voluntarily control and involuntary reactions to objectively assess whether stimulation achieves desired outcomes. Through proxy learning, system sensors correlate with sophisticated clinical measurements via machine learning, enabling clinical-grade optimization using accessible devices. The system continuously explores multidimensional parameter spaces to discover optimal stimulation patterns personalized to individual users, automatically adapting parameters based on measured physiological effectiveness rather than subjective user control or preset patterns. Applications include medical devices, wellness products, and intimate stimulation systems.

An apparatus for delivering stimulation energy to a patient is provided. The apparatus comprises an algorithm for determining a stimulation paradigm comprising one or more stimulation parameter settings, and an implantable system comprising an implantable device. The implantable device comprises at least one sensor for measuring evoked compound action potentials (eCAPs), muscle responses, and / or other neural responses, and multiple stimulation elements configured to deliver the stimulation energy to tissue based on the stimulation paradigm. The algorithm is configured to determine the stimulation paradigm based on the measured neural responses. Methods of delivering stimulation energy are also provided.

System (100), method and computer program product for training an encoder neural network (110) to generate stimulation patterns (192) for an auditory cortical implant having a predefined number of electrodes arranged in a two‐dimensional electrode array (220) adapted for use in mammals (201) with impaired hearing. The encoder processes soundwave training data records (210i) and outputs for each corresponding soundwave a one‐ dimensional encoded sound vector (191) which is mapped to the electrode array (220) to obtain corresponding two‐dimensional stimulation patterns (192) for the auditory cortical implant. A decoder neural network (130) reconstructs the respective training data record from the encoder output wherein the parameters of the decoder are determined in parallel to the encoder during training. A cost function with a first term (CFr) to measure reconstruction performance and a second term (CFc) to measure continuity of the stimulation patterns is used when training the encoder (110) and decoder (130 while aiming at minimizing a weighted sum of the terms (CFr, CFc) of the cost function.

Provided is a heart-rate control device capable of adjusting the heart rate of a subject to a desired value between a base heart rate and an active heart rate, such as a heart rate immediately after physical exercise. The heart-rate control device includes a detector for detecting heartbeat information of a subject, an input unit to which information on a target value relating to a heart rate of the subject is to be inputted, a difference extracting unit for determining a difference between the target value and a current value relating to the heart rate of the subject, the current value being calculated from the heartbeat information, a pattern generating unit for generating a stimulus pattern depending on the difference so that the current value approaches the target value, the stimulus pattern being a combination of a sympathetic nerve stimulus for stimulating sympathetic nerves, a parasympathetic nerve stimulus for stimulating parasympathetic nerves, and an initialization stimulus for preventing stimulus saturation of the sympathetic nerves or the parasympathetic nerves, and an output unit for outputting the sympathetic nerve stimulus, the parasympathetic nerve stimulus and the initialization stimulus to the subject in accordance with the stimulus pattern.

This disclosure is directed to devices, systems, and techniques for delivering electrical stimulation. In some examples, a system includes processing circuitry configured to: receive information representative of a bioelectric brain signal recorded from a brain of a patient; and determine, based on the information, at least one pathological frequency of the bioelectric brain signal. Additionally, the processing circuitry is configured to select, based on the at least one pathological frequency, a sequence of pulse bursts at a pulse burst frequency, the sequence of pulse bursts at least partially defining electrical stimulation deliverable to an area of a brain of a patient, wherein adjacent pulse bursts within the sequence comprise different intra-burst pulse frequencies; and control a medical device to deliver the electrical stimulation comprising the sequence of pulse bursts to the area of the brain of the patient.

The application discloses a stimulation mode control method, a control system, an electronic device and a medium. The control method comprises the following steps: constructing a specific data set of a measured physiological signal; judging whether a to-be-measured physiological signal belongs to the specific data set; when the judgment result is "yes", starting stimulation and selecting a matched stimulation mode, so that the stimulation mode is accurate and effective regulation of the stimulation mode is realized, and the accuracy of the stimulation is improved.

This invention provides a light source with high chromatic color richness. The high chromatic color richness light source is based on the difference in stimulation patterns of the three types of cone cells in the human eye's color perception mechanism between monochromatic light and mixed light that can be identified as that color. The visible light band from 380nm to 780nm is divided into three bands using 430nm and 680nm as sub-band wavelength values. Furthermore, it is designed based on the emission peak wavelength of LED chips and phosphors using the corresponding LED chips as excitation sources, ensuring the richness of the emission peak wavelengths of the high chromatic color richness light source in each band, thus guaranteeing the chromatic color richness of the light source. Therefore, it is beneficial in the human eye's color perception mechanism to make the chromatic color of the object being observed more similar to that of the observed object, thereby improving the human eye's ability to compare and filter differences in the observed object and providing a natural, realistic, and comfortable visual experience.

Techniques for using a biological and artificial neural network (BANN) system to generate a neural-based embedding of an input signal. The BANN system comprises a multi-electrode array (MEA); a biological neural network (BNN) comprising neurons arranged on the MEA, an artificial neural network; and at least one processor. The method comprises using the BANN system to stimulate the BNN by using the MEA to generate electrical signals in accordance with a stimulation pattern generated based on the input signal; measure, using the MEA, a response of the BNN responsive to the stimulating by deriving from the response of the BNN, multiple features of the at least one response; and process the multiple features derived from the response of the BNN with the ANN to generate the neural-based embedding, wherein the ANN is trained to process the multiple features derived from the response of the BNN.

Administration of low frequency electrical stimulation of the cranial nerves delivered during sleep increases the presence and function of aquaporin-4 (AQP4) channels in the astrocytic endfeet surrounding descending arterioles in the brain. This underlying low frequency stimulation pattern is overlaid with temporally patterned 'bursts' of higher frequency stimulation to pulse the underlying artery to drive cerebrospinal fluid (CSF) penetration into the parenchyma This also serves to create more movement in general within the parenchymal extracellular space to increase the probability of waste biomolecules to interact with sites for active transport out of the brain. During the period of sleep, these two layered patterns will be periodically replaced with multiple continuous periods of stimulation at gamma frequency to promote a more phagocytic phenotype in glial cells to help break down waste biomolecules and misfolded proteins for subsequent clearance. Administration of electrical stimulation can be selectively modified to adjust CSF clearance, for example, to quickly clear drug concentrations in the brain during an overdose.

Systems and methods define a stimulation pattern for a juvenile product utilizing a mobile device that executes a mobile application that is linked to the juvenile product. The method comprises the step of recognizing, by the mobile device when executing the mobile app, the user-defined stimulation pattern for the juvenile product. The stimulation pattern can be a vibration pattern or a motion pattern, and can be detected in a number of different ways by the mobile device. The method further comprises the step of determining control signals for the actuator(s) of the juvenile product based on the user-defined stimulation pattern that is recognized by the mobile device. The method further comprises the step of, in response to receiving a command to execute the user-defined stimulation pattern, controlling the actuator(s) of the juvenile product based on the stored control signals for the user-defined stimulation pattern.

This disclosure relates to a method, system, device, and medium for processing visual cortex photoelectrostimulation. The method includes: acquiring a visual stimulus pattern; determining a mathematical model based on the visual stimulus pattern; generating a visual stimulus signal of a target paradigm according to the mathematical model; acquiring the user's electroencephalogram (EEG) signal and eye and / or pupil response signal corresponding to the visual stimulus signal; processing the response signal to obtain a visual cortex response signal of a target format; acquiring the EEG signal and visual cortex response signal under multiple different target paradigms; selecting different processing methods to extract features from the EEG signal and visual cortex response signal according to different target paradigms to obtain multiple target features; evaluating based on the multiple target features to obtain an evaluation result; and displaying the evaluation result in the form of charts and numerical values ​​on the user terminal interface. This technical solution combines multiple advanced stimulus generation, recording, and analysis methods, making it suitable for various scenarios.

According to the closed-loop feedback training method and device for hypoglycemia perception reconstruction, a closed-loop training system integrating monitoring, stimulation, recording and evaluation is established, a patient is helped to reestablish the correct recognition and perception ability of the brain on hypoglycemia early signals, and the serious hypoglycemia risk is reduced fundamentally. The method comprises the following steps: (1) acquiring continuous blood glucose monitoring data of a user through a data interface, and judging whether a training trigger condition is met or not based on a blood glucose lowering trend of the data; (2) when it is judged that the training opportunity arrives, a preset specific tactile stimulus is applied to the user through a tactile feedback device; (3) when the tactile stimulation is applied, a guide instruction is sent to a user through a user interface, and a recording entrance is provided; (4) the blood glucose value / trend at the training time, the applied tactile stimulation mode and the subjective feeling recorded by the user are stored in an associated mode, a training log is formed, and the training log is analyzed regularly.

The application provides an enhanced non-invasive deep brain stimulation system and method based on time interference, which comprises: an enhanced sawtooth wave stimulation waveform; a stimulation circuit supporting the enhanced sawtooth waveform; and a finite element simulation subsystem for guiding the stimulation mode. Compared with the traditional sine wave time interference, the sawtooth wave time interference generates a steeper envelope, thereby enhancing the stimulation intensity. The sawtooth wave spectrum has more complex components and contains more high-frequency components, and a specific constant-current source stimulator is required to ensure that the high-frequency component output constant current is not distorted. The system further comprises a simulation subsystem for determining stimulation parameters using a corrected finite element method and a stimulation subsystem for stimulating according to the parameters. Compared with the traditional sine wave, the application provides a non-invasive deep brain stimulation based on sawtooth wave time interference, improves the stimulation intensity, and provides a more effective method for non-invasively regulating the brain and intervening in diseases.

This invention discloses an implantable closed-loop neuromodulation system and method, relating to the field of neuroengineering, aiming to solve the problems of poor electrode adaptability and slow closed-loop response in existing implantable neuromodulation systems. The system includes intracranial electrodes, a one-to-one matching adapter plate for adapting different electrodes, a feedthrough component, and an implantable host fixed to the skull surface. The processing unit within the host decodes neural signals in real time within 10 ms to determine the brain state; if an abnormality is detected, the stimulation output unit selects a pattern from a preset safe stimulation pattern library to generate stimulation commands for intervention. This application achieves multi-electrode adaptation through the adapter plate and millisecond-level closed-loop response through high-speed processing, improving the timeliness and accuracy of intervention and enhancing safety.

Techniques for using a biological and artificial neural network (BANN) system to perform a task. The BANN system comprises a multi-electrode array (MEA); a biological neural network (BNN) comprising neurons arranged on the MEA, a trained statistical model trained using inputs generated using responses of the BNN to training data inputs; and at least one processor. The method comprises using the BANN system to receive an input signal; encode the input signal to generate a stimulation pattern; stimulate the BNN by using the MEA to generate electrical signals in accordance with the stimulation pattern; measure, using the MEA, a response of the BNN responsive to the stimulating; generate, based on the measured response, an input for the ANN; process the input with the trained statistical model to obtain corresponding output; and use the output from the trained statistical model in furtherance of performing the task.

Techniques for using a biological and artificial neural network (BANN) system to perform a task. The BANN system comprises a multi-electrode array (MEA); a biological neural network (BNN) comprising neurons arranged on the MEA, a trained statistical model trained using inputs generated using responses of the BNN to training data inputs; and at least one processor. The method comprises using the BANN system to receive an input signal; encode the input signal to generate a stimulation pattern; stimulate the BNN by using the MEA to generate electrical signals in accordance with the stimulation pattern; measure, using the MEA, a response of the BNN responsive to the stimulating; generate, based on the measured response, an input for the ANN; process the input with the trained statistical model to obtain corresponding output; and use the output from the trained statistical model in furtherance of performing the task.

The invention provides a method and system for predicting the possibility of individual epileptic seizure. Specifically, the present disclosure relates to an intracranial implant comprising one or more electrodes attached thereto, and an associated method of delivering an active perturbation pattern to a brain region, the method comprising: sending electrical stimulation to the brain through the intracranial implant, where the electrical stimulation is sent according to a stimulation pattern; measuring the output of the brain to the electrical stimulation; inputting the output into a prediction model; and detecting, through the prediction model, an output indicating a high likelihood of epileptic seizure. Based on detecting an output indicative of a high likelihood of a seizure, the intracranial implants and related methods may also prophylactically treat the brain through brain contact electrodes to prevent a seizure.

System, methods, and other embodiments described herein relate to enhancing operator vigilance without causing an adverse reaction. In one embodiment, a method includes receiving a disengagement likelihood; selecting a stimulus pattern that if executed adjusts a vehicle function; and executing the stimulus pattern if the disengagement likelihood is above a first threshold and below a second threshold.

Methods and systems for customizing blood pressure reduction stimulation therapy for a patient are disclosed, which according to an embodiment, may involve establishing baseline blood pressure parameters of a patient; applying predefined stimulation patterns and determining if any predefined stimulation patterns meet designated acceptance criteria; if no predefined stimulation patterns meet the designated acceptance criteria, calculating and applying new stimulation patterns and determining if any new stimulation patterns meet the designated acceptance criteria; determining, from among the applied predefined stimulation patterns and the applied new stimulation patterns, a stimulation pattern most suitable for the patient; and applying the most suitable stimulation pattern to the patient.

The present application relates to a kind of brain deep electric stimulation equipment capable of realizing burst stimulation mode, the electric stimulation equipment includes brain deep stimulation unit and brain deep stimulation electrode, and they are connected by in-vivo lead wire.Wherein, brain deep stimulation unit is configured to be able to make brain deep stimulation electrode at adjustable stimulation parameter burst stimulation mode to implement the deep brain stimulation neuroregulation of target target point.Therein, the stimulation signal of burst stimulation mode can be adaptively adjusted according to the different electrophysiological characteristics of target target point one or more of group frequency, intra-group frequency and the number of sine waves in single stimulation period.

The invention relates to a software-based method for activating and analyzing a neural network state of a subject for neurophysiological data analysis, pattern identification and characterization involving, the method comprising the steps of: (i) administering a resting-state condition during which the subject's brain activity is measured in the absence of rhythmic sensory stimulation or task; (ii) administering to the subject a sensory stimulation, possibly supplied with a cognitive task, selected from the group consisting of auditory stimulation, visual stimulation, haptic stimulation, electrical stimulation, and magnetic stimulation modalities such as transcranial alternating current stimulation, subauditory stimulation devices, functional electric stimulation, and skin-applied stimulation systems, or any combination thereof, at a frequency between at least 0.01 Hz and at most 120 Hz to induce rhythmic brain responses; (iii) measuring the subject's brain responses with one or more brain imaging or recording devices; (iv) analyzing the measured brain responses by performing a software analysis that generates either a brain topography or a network connectivity map in response to the sensory stimulation and a brain topography or connectivity map of the default mode network during the resting state; and (v) comparing the obtained brain topographies or functional connectivity maps to a database of normative data in order to identify abnormalities.

Techniques for calibrating a system comprising a multi-electrode array (MEA); a biological neural network (BNN) comprising neurons arranged on the MEA, and a processor. The method comprises using the system to select a subset of a electrodes of the MEA by stimulating the BNN by using the electrodes of the MEA to generate electrical signals in accordance with a calibration stimulation pattern; measuring a response of the BNN to the stimulating; selecting, based on the measured response of the BNN, the subset of electrodes based on an amount of neuronal activity induced by respective ones of the electrodes; receiving an input signal to be processed by the BNN; encoding the input signal to generate a stimulation pattern for stimulating the BNN; and stimulating the BNN using only the selected subset of the electrodes to generate electrical signals in accordance with the stimulation pattern.

An apparatus for one or more of measuring cognitive state of a user and inducing a change of cognitive state of a user is disclosed. The apparatus is configured to: receive a response signal indicative of an active user response provided in response to a stimulus provided by a stimulus means; determine a control signal in dependence on a characteristic of the response signal and in accordance with a dynamic stimuli pattern, wherein the dynamic stimuli pattern is indicative of possible stimuli to be provided in response to the active user response; and provide the control signal to the stimulus means to control the stimulus means to provide a further stimulus in accordance with the control signal.

The present disclosure describes a method for providing sensory stimulation to a user with a neurological disorder, such as post-traumatic stress disorder. The method includes receiving a set of sensory stimulation instructions by a processor within a headset. The method also includes administering a therapeutically effective amount of the sensory stimulation, wherein the sensory stimulation alternates between a first sensory stimulus comprising simultaneously providing a left visual stimulation pattern to the user's left eye and a right auditory stimulation pattern to the right side of the head, and a second sensory stimulus comprising simultaneously providing a right visual stimulation pattern to the user's right eye and a left auditory stimulation pattern to the left side of the head. The first sensory stimulus and the second sensory stimulus comprise a first stimulation pattern, a second stimulation pattern, and a third stimulation pattern, respectively.