62 results about "Neural control" patented technology

A neurological control system for modulating activity of any component or structure comprising the entirety or portion of the nervous system, or any structure interfaced thereto, generally referred to herein as a “nervous system component.” The neurological control system generates neural modulation signals delivered to a nervous system component through one or more intracranial (IC) stimulating electrodes in accordance with treatment parameters. Such treatment parameters may be derived from a neural response to previously delivered neural modulation signals sensed by one or more sensors, each configured to sense a particular characteristic indicative of a neurological or psychiatric condition.

A neurological control system for modulating activity of any component or structure comprising the entirety or portion of the nervous system, or any structure interfaced thereto, generally referred to herein as a “nervous system component.” The neurological control system generates neural modulation signals delivered to a nervous system component through one or more neuromodulators to control neurological state and prevent neurological signs and symptoms. Such treatment parameters may be derived from a neural response to previously delivered neural modulation signals sensed by one or more sensors, each configured to sense a particular characteristic indicative of a neurological or psychiatric condition.

A neurological control system for modulating activity of any component or structure comprising the entirety or portion of the nervous system, or any structure interfaced thereto, generally referred to herein as a “nervous system component.” The neurological control system generates neural modulation signals delivered to a nervous system component through one or more intracranial (IC) stimulating electrodes in accordance with treatment parameters. Such treatment parameters may be derived from a neural response to previously delivered neural modulation signals sensed by one or more sensors, each configured to sense a particular characteristic indicative of a neurological or psychiatric condition.

A neurological control system for modulating activity of any component or structure comprising the entirety or portion of the nervous system, or any structure interfaced thereto, generally referred to herein as a “nervous system component.” The neurological control system generates neural modulation signals delivered to a nervous system component through one or more neuromodulators, comprising intracranial (IC) stimulating electrodes and other actuators, in accordance with treatment parameters. Such treatment parameters may be derived from a neural response to previously delivered neural modulation signals sensed by one or more sensors, each configured to sense a particular characteristic indicative of a neurological or psychiatric condition.

A neurological control system for modulating activity of any component or structure of some or all of the nervous system, or any structure interfaced thereto, generally referred to herein as a “nervous system component.” This system generates neural modulation signals delivered to a nervous system component through one or more neuromodulators to control neurological state or autonomic state and prevent neurological or metabolic signs and symptoms. Such treatment parameters may be derived from a neural response to previously delivered neural modulation signals, with sensors configured to sense a particular characteristic indicative of a neurological, psychiatric, or metabolic condition.

Apparatus is provided for applying current to a nerve, including a housing that is adapted to be placed in a vicinity of the nerve. First, second, and third electrodes are fixed to the housing at first, second, and third longitudinal sites of the housing, respectively. The first, second, and third electrodes do not come in direct physical contact with the nerve. The second site is between the first and third sites. The second electrode has an electrode surface axial length. The apparatus also includes first and second internal insulating elements, which are fixed to the housing between the first and second longitudinal sites, and between the second and third longitudinal sites, respectively. The first and second internal insulating elements are shaped so as to define, upon placement of the housing, a nerve axial distance between the first and second internal insulating elements that is less than the electrode surface axial length.

The invention provides an automatic tracing identification system for artificial neural control. The system comprises a fixed viewing field video acquisition module, a full-functional variable viewing field video acquisition module, a video image identification and judgment algorithm module, an artificial neural control module, a moving object track tracing module, a database comparison and alarm judgment module, a monitoring characteristic entering and rule setting module, a light monitoring and controlling module, a background light source module, an alarm output, display and storage module and a safety monitoring sensor. The system fully solves the problems of unclear doubtful target characteristics, single function, low intelligence degree and the like in post processing and images of the prior image monitoring system, uses the artificial neural control, biological identification technology and intelligent image identification algorithm, and has the characteristics of high intelligence degree, precaution, clear doubtful object images, high cost performance and the like.

A Switching Kalman Filter Model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A “hidden state” models the probability of each mixture component and evolves over time in a Markov chain. Gaussian mixture models and Expectation Maximization (EM) techniques are extended for automatic spike sorting. Good initialization of EM is achieved via spectral clustering. To account for noise, the mixture model is extended to include a uniform outlier process. A greedy optimization algorithm that selects models with different numbers of neurons according to their decoding accuracy is used to automatically determine the number of neurons recorded per electrode. Closed loop neural control of external events are demonstrated using neural control of a computer curser.

A system using neurological control signals to control a device is disclosed. The system may include a sensor sensing electrical activity of a plurality of neurons over time and a vector generator generating a neural control vector from the sensed electrical activity of the plurality of neurons over time. The system may also include a control filter to which the neural control vector is applied to provide a control variable and an output device controlled by the control variable.

Disclosed is a multi-channel wireless closed loop deep brain neural sensing and control system, which has three operating modes. In the first open-loop operating mode, acquired signals are saved through an SD card and are transmitted in real time to an upper computer through a wireless communication device, and electric pulse stimulation parameters of an outboard closed loop deep brain stimulation system are manually controlled. In the second closed loop operating mode, the acquired signals are automatically adjusted in real time to the output electric pulse stimulation parameters; and the acquired signals are stored through the SD card, or transmitted to the upper computer in real time. In the third closed loop operating mode, the acquired signals are stored through the SD card for offline analysis, and are meanwhile transmitted to the upper computer in real time; and the upper computer conducts closed loop algorithm computation of data of the signals, and controls the electric pulse stimulation parameters of the outboard closed loop deep brain stimulation system. The multi-channel wireless closed loop deep brain neural sensing and control system can be used as a temporary stimulator in a DBS surgery or as a research tool for obtaining clinical trial data, and a solution can be adjusted through recording and analyzing the effects of neural control.

At least one electrical brain signal is received from a patient and is demultiplexed into an efferent motor intention signal and at least one afferent sensory signal (such as an afferent touch sense signal and/or an afferent proprioception signal). A functional electrical stimulation (FES) device is controlled to apply FES to control a paralyzed portion of the patient that is paralyzed due to a spinal cord injury of the patient. The controlling of the FES device is based on at least the efferent motor intention signal. A demultiplexed afferent touch sense signal may be used to control a haptic device. The afferent sensory signal(s) may be used to adjust the FES control.

The invention discloses a dredging process intelligent decision analysis method based on a fuzzy neural control system. The method comprises the steps that 1, data influencing related decision parameters of a dredging construction technology are collected; 2, a matlab principal component analysis method is used to find out the feature thresholds of several decision parameters with the maximum contribution rate; 3, a knowledge base is established; 4, a fuzzy neural control system mechanism is selected and artificial neurons are established, and the number of hidden layers of a neural network is determined according to the dredging complexity; and 5, the fuzzy neural control system is established to carry out assisted decision on dredging construction. According to the invention, the space complexity of the neural network and fuzzy control time complexity are fused, so that the degree of dredging automation is high.

A novel ₁ adaptive/neural control architecture provides a device and method that permits fast adaptation and yields guaranteed transient response simultaneously for both the system's input and output signals, in addition to providing asymptotic tracking. The main feature of the invention is rapid adaptation with a guaranteed low frequency control signal. The ability to adapt rapidly ensures the desired transient performance for both the system's input and output signals, simultaneously, while a low-pass filter in the feedback loop attenuates the high-frequency components in the control signal.

The invention relates to a physiological index detection system for evaluating psychological neural activity. The physiological index detection system comprises a photoelectric combination probe, an excitation source device, a low-frequency oscillator, a skin blood flow detection device, a skin potential electrical property detection device, a digital signal processor and a display terminal, wherein the photoelectric combination probe is used for acquiring physiological signals of peripheral sympathetic nervous activity of a human body; the skin blood flow detection device is used for measuring the velocity and the flow of skin blood flow and obtaining physiological indexes of microcirculations controlled by epinephrine sympathetic nerves; the skin potential electrical property detection device is used for measuring the electrical property of the evoked potential of the skin and obtaining the physiological indexes of the sweat gland controlled by acetylcholine sympathetic nerves. The physiological index detection system has the advantages that a human body palm/sole skin evoked potential electrical property and skin blood flow testing method is ingeniously used for detecting the physiological indexes related to the activity and the reaction of the peripheral sympathetic nerves, and scientific and accurate biomarker data are provided for the evaluation and diagnosis on the mental activity of human body nerves.

The invention discloses a high supersonic speed aircraft neural network composite learning control method based on a high gain observer and aims to solve a technical problem of poor practicality of ahigh supersonic speed aircraft control method in the prior art. The method is advantaged in that a strict feedback mode of an attitude subsystem is transformed to acquire an output feedback mode, a high gain observer is utilized to estimate unknown variables, and base is provided for subsequent controller design; system lumping uncertainty is considered, only one neural network is needed for approximation, the controller is simple to design, and engineering realization is facilitated; a system modeling error is introduced, composite new neural network learning rules are constructed, stable high supersonic speed aircraft neural control under uncertain situations is realized, and good practicality is realized.

The invention discloses a system and a method for animal robot autonomous navigation. The system comprises a GPS module arranged on a controlled animal, a multi-channel neural signal stimulation module and a control module. The GPS module is configured to receive a GPS satellite signal, and outputs current position information of an animal robot to the control module. The control module is configured to receive the current position information of the GPS module, and compare the current position information with set target position information, to determine a navigation path and form a control instruction. The multi-channel neural signal stimulation module is configured to include a mirror current source, a boost circuit, and an analog multiway switch. Through the connected mirror current source and the boost circuit, constant current is obtained. According to the control instruction, a corresponding channel of the analog multiway switch is selected to output constant current which acts on a neural control region of the controlled animal, to realize output of simulation neural coding information and realize navigation.

The invention discloses an artificial neural control method for upper limb motions of a humanoid robot. The method comprises a first part of establishing a model and templates, and a second part of selecting the templates and controlling output. The first part comprises establishment of the upper limb dynamic model and the initial motion templates of the humanoid robot. The second part comprises the steps that (1) a new motion task position is given; (2) the motion templates are selected in a distinguished manner according to the quantity of execution times of the step; (3) based on a weight and a control signal of each motion template, a control signal needed by the new motion task is calculated, and a destination position of upper and lower limb motions under the signal is calculated; and (4) circulating execution conditions are set, and the steps (2), (3) and (4) are repeated, the final motion template of the new tasks is output, and the motion is executed. The method has the advantages that the calculation quantity is greatly reduced; the responding speed is increased; and learning capability is strengthened.