24 results about "Cortex motor" patented technology

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

The present application relates to the technical field of medical treatment, and in particular to a stimulation system for treating a tremor, and an action method and a stimulation parameter adjustment method. The stimulation system comprises: an execution part, which is used for applying to at least one target position of a human body a target stimulation action for treating a tremor; a collection part, which is used for collecting a movement state signal of at least one target position of the human body and collecting motor cortex electrocorticogram of the human body; and a control part, which generates target stimulation parameters on the basis of the movement state signal and the motor cortex electrocorticogram, and adjusts, on the basis of the target stimulation parameters, the target stimulation action for treating a tremor that is applied by the execution part. Therefore, the problems in the prior art, such as the effect of drug treatment being poor, a brain pacemaker surgery having a high risk, a high cost and a poor safety, and vibrating gloves treatment having poor comfort and user-friendliness and thus affecting normal daily life of patients, are solved.

A digital method of therapeutic brain stimulation is provided. The method comprises presenting to a user in a UI a group of trials by sequentially displaying symbols against a background. The symbols are randomly displayed one at a time against one several contexts in the background to stimulate the user's parietal or frontal lobes or motor cortex. A user response is received for each display of a symbol indicating whether the symbol is the same or different from an immediately prior symbol for the same context, wherein each trial has a maximum allowed response time for the user input after which any response is counted as a false alarm. A user score is generated for the group of trials, wherein the score compares correct identification and incorrect false alarm responses for the group of trials against a user score of a previous group of trials.

Provided herein are targeting peptides and vectors containing a sequence that encodes the targeting peptides that deliver agents to specific substructures in the brain. Specifically, the targeting peptide is a component of a modified, sequence-specified adeno-associated virus (AAV) capsid protein further wherein the brain substructure may be the globus pallidus, putamen, internal capsule, caudate, claustrum, substantial nigra, motor cortex, insula,.temporal cortex, thalamus, hippocampus, subiculum, and deep cerebellar nuclei.

The application discloses a rehabilitation lower limb exoskeleton EEG decoding method based on ensemble learning, relates to the technical field of EEG decoding, and comprises the following steps: acquiring an EEG signal of a user's motor cortex through an EEG device; performing shallow layer processing on the EEG signal to obtain a first type of feature; constructing a deep layer processing network model of the EEG signal, performing deep layer processing on the EEG signal, and obtaining a second type of feature; combining the first type of feature and the second type of feature, training a classifier, and decoding the motor intention of the EEG signal; performing visual feedback on the user according to the decoded motor intention, then executing the decoded movement on a lower limb exoskeleton device, and obtaining proprioceptive feedback. The application combines shallow layer and deep layer neural networks to improve the performance of lower limb motor intention, performs continuous motor imagination experiments on the lower limb exoskeleton device, collects continuous EEG signals, and designs an experimental paradigm of online visual and proprioceptive feedback.

A brain-computer interface (BCI) includes a multichannel stimulator and a decoder. The multichannel stimulator is operatively connected to deliver stimulation pulses to a functional electrical stimulation (FES) device to control delivery of FES to an anatomical region. The decoder is operatively connected to receive at least one neural signal from at least one electrode operatively connected with a motor cortex. The decoder controls the multichannel stimulator based on the received at least one neural signal. The decoder comprises a computer programmed to process the received at least one neural signal using a deep neural network. The decoder may include a long short-term memory (LSTM) layer outputting to a convolutional layer in turn outputting to at least one fully connected neural network layer. The decoder may be updated by unsupervised updating. The decoder may be extended to include additional functions by transfer learning.

The application relates to the technical field of brain-computer interface, and discloses a signal acquisition method for brain-computer interface control of a lower-limb exoskeleton robot. The method collects original electroencephalogram signals through a multi-channel electrode array arranged on a motor cortex of a scalp, and synchronously collects motion data of a lower-limb inertial measurement unit. In the signal processing process, differential amplification is adopted to suppress common-mode interference, adaptive narrow-band filtering is adopted to dynamically track electroencephalogram rhythms, and a blind source separation algorithm based on independent component analysis is adopted to remove motion artifacts. Finally, the processed motion intention electroencephalogram signals are output, the precision and real-time performance of intention recognition are improved, and accurate and stable control signals are provided for the lower-limb exoskeleton robot.

The invention relates to the technical field of medical auxiliary instruments, and discloses a brain-controlled rehabilitation instrument, which comprises an electroencephalogram signal acquisition module, a signal processing and control module, a mechanical exoskeleton module and a sensory feedback module, the electroencephalogram signal acquisition module is used for acquiring an electroencephalogram signal of a brain motor cortex in real time; the signal processing and control module is used for carrying out preprocessing, feature extraction and motion intention decoding on the electroneurographic signals and outputting a control instruction to the mechanical exoskeleton module; the mechanical exoskeleton module drives joints to move according to a control instruction, and the sensory feedback module feeds motion parameters back to a patient, so that a closed-loop neural rehabilitation system of'intention recognition-action execution-sensory feedback 'is formed. The cerebral cortex is activated through motor imagery, the training efficiency is improved compared with that of traditional equipment, non-invasive electrode design is adopted, operation is not needed, the infection risk is avoided, the mechanical structure is light in weight, wearing is comfortable and free of pain, and safety is high.

This invention relates to an instrument for treating neurological or psychiatric disorders using i-wave periodicity-dependent four-pulse theta burst stimulation (iQPS). The system includes a transcranial magnetic stimulation (TMS) stimulator configured to deliver test pulses to a target motor cortex region and therapeutic theta bursts to a treatment region, wherein the pulse frequency is customized according to the patient's individual i-wave periodicity. An electromyography (EMG) device records motor evoked potentials (MEPs) from the target muscles and analyzes the intervals between MEPs to determine the i-wave periodicity. Personalized stimulation enhances cortical plasticity by synchronizing stimulation with the brain's intrinsic neural timing. This invention is applicable to neurological and psychiatric conditions including depression, schizophrenia, stroke recovery, and neuropathic pain. Advantages include improved therapeutic efficacy, reduced patient response variability, and a non-invasive adaptive therapy model aligned with the patient's unique neurophysiological characteristics, thus providing a precise and scalable approach to brain stimulation.

A helmet with pre-positioned locations for transcranial magnetic stimulation (TMS) coils and sensors. The helmet has precision cut TMS coil guides for easy insertion and removal of the TMS coil. The helmet can have one or two TMS coil cut-outs positioned to enable inserted TMS coils to direct their generated magnetic waves to specific areas of a persons brain, including for example a dorsolateral prefrontal cortex, a motor cortex or a superior parietal lobule. The inner surface of the helmet can include at least one sensor array to detect levels of brain activity for neuroimaging. Different types of complementary sensing systems can be used at the same time. A helmet positioning system can be used to hold the helmet in place once the proper position has been determined, and can be manually or mechanically actuated such that one or more TMS coil modules are properly aligned over the targeted structure of the individual's head.

The present invention provides a method of treating or diagnosing an emotion or stress disorder comprising modulation of or detection of excitability of a motor cortex of a patient. The invention also provides a medicament for treating mood or stress disorders, wherein the medicament comprises a preparation for regulating the excitability of the motor cortex. The invention also provides a kit for diagnosing emotion or stress disorder. The invention also provides a medical device for treating or diagnosing mood or stress disorders.

The invention discloses a field brain injury rapid diagnosis system based on electroencephalogram data, and relates to the technical field of electroencephalogram data analysis and diagnosis, and the field brain injury rapid diagnosis system comprises an external stimulation module which utilizes sound waves and limb muscle electric pulse stimulation to induce specific event-related potential and motor cortex activity rhythm and utilizes a data acquisition module to perform detection; the electroencephalogram data analysis module is used for carrying out multi-scale time window preprocessing and deep learning algorithm analysis; and the damage judgment module is used for judging the damage type and quantifying the damage degree. The system can depend on a portable electroencephalogram acquisition helmet, portable pulse stimulation equipment and a chip-level handheld terminal to realize a method from stimulation to detection to diagnosis, and the whole equipment is light and convenient, is suitable for being deployed in a field operation scene and can stably run in a complex environment.

This disclosure provides a method for selecting parameters for stimulation of the thalamus using a deep brain stimulator to treat a motor disorder in a subject, such as paralysis of the upper limb and face muscles. Also provided are methods for treating motor disorder in a subject. Also provided is an implantable electrode lead for neuromodulation and recording, configured to provide targeted stimulation and / or recording to multiple anatomically distinct neural structures, specifically the thalamus, the motor cortex, and optionally, the corona radiata, using a single continuous elongate lead body.

The application provides an EEG-based rehabilitation corpus screening method, system and medium, which provides corpus screening for a language function rehabilitation system based on a motor imagery brain-computer interface. Through screening, the questions in the original question bank can be classified, and the question bank with a higher activation degree of the motor cortex is screened out, which is used for corpus stimulation of the language function rehabilitation system based on the motor imagery brain-computer interface. The motor cortex of the rehabilitation patient is better activated from the corpus stimulation part, the functional connection of the motor cortex is activated, the recovery and reconstruction of the language function are helped, and the difficulty of imagination when the stimulation is expected can be reduced, so that a better rehabilitation effect is achieved.

The invention provides a multi-mode freezing gait self-adaptive training method and system based on a brain-computer interface, and relates to the technical field of physical therapy. The method comprises the following steps: extracting a first electroencephalogram feature for representing the activation state of the motor cortex based on a first electroencephalogram signal; extracting a second electroencephalogram feature for judging perception consistency based on the second electroencephalogram signal; based on the first electroencephalogram feature and the second electroencephalogram feature, judging whether the user has a motion intention or not; electromyographic signals corresponding to lower limb muscle groups of the user are obtained, and whether an electromyographic active state exists or not is judged based on the electromyographic signals; in response to existence of the motion intention but no myoelectricity active state, judging that the current state of the user is a frozen gait state, and generating a corresponding multi-mode intervention trigger instruction; and applying a signal to the user, and performing adaptive training on the gait movement of the user. According to the technical scheme, accurate judgment of the frozen gait state and self-adaptive triggering of multi-mode intervention can be achieved.

The invention relates to a mechanical arm self-adaptive grabbing system based on cortical neural signal and computer vision sharing and an implementation method of the mechanical arm self-adaptive grabbing system. The system comprises a sensing module used for collecting neural signals of a brain motor cortex through an intrusive microelectrode array brain-computer interface, identifying a macroscopic motion intention of a user, identifying a semantic category of a target object, and calculating a three-dimensional pose of the target object in a mechanical arm coordinate system; the shared control decision module is used for realizing automatic switching of control rights based on a finite-state machine and generating a mechanical arm motion track planning instruction and a manipulator grabbing gesture instruction; and the sharing control execution module is used for driving the mechanical arm to move to a target position according to the mechanical arm movement track planning instruction and controlling the mechanical arm to execute a self-adaptive grabbing gesture corresponding to the semantic category according to the mechanical arm grabbing gesture instruction. The intention of a brain-computer interface and the autonomous control ability of the mechanical arm can be fully fused, and the grabbing success rate and stability of the mechanical arm are improved.

The invention belongs to the field of neurobiology and neuroengineering, and provides a method for predicting and preventing dyskinesia diseases (such as Parkinson's disease) caused by dopamine system damage before clinical symptoms appear by taking abnormal low-frequency beta neural oscillation of a specific brain region as a biomarker. The method is used for predicting abnormal high-frequency beta neural oscillation occurring after the diseases (especially the period after motion symptoms appear); wherein the monitoring and intervention target brain region of the abnormal low-frequency beta oscillation is striatum, and the abnormal high-frequency beta oscillation is visible in the striatum, the motor cortex and the subthalamic nucleus.

The application discloses an olfactory training system for activating a motor-olfactory-hippocampal neural network and application, the system comprises a plurality of different odor sources, the odor source is used for alternating olfactory stimulation to stroke patients, so as to improve the cognitive function and / or olfactory function of the patient. The application firstly discloses that the function connection of the motor-olfactory-cognitive brain area is disordered after stroke, the function of the VIP neuron of the motor cortex (MO) around the infarction is inhibited; the VIP neuron of the MO region dominates the GABA neuron of the dorsal peripeduncular area (DP) through an inhibitory pathway, activating the pathway can remove the inhibition of the glutamatergic pyramidal neuron of the DP, enhance the excitatory input to the hippocampal CA3 region, and restore the olfaction and spatial memory. The application proves that the structured olfactory training can activate the loop, and significantly improve the cognitive function of the stroke patient. The application provides a simple and non-invasive stroke rehabilitation strategy.

This invention discloses a method for assessing motor cortical excitability based on threshold-tracking magnetic stimulation, relating to the field of biomedical engineering technology. The method includes the following steps: electrical stimulation of the median nerve in the subject's wrist; recording on the abductor pollicis brevis (APB) muscle; recording the stimulus-response curve; determining the cortical stimulus-response (SR) curve by increasing the magnetic stimulation intensity to the specified levels; calculating the statistical parameters of the mean and standard deviation of the excitability threshold; and determining the dynamic trend of cortical excitability. This invention determines the cortical stimulus-response curve by increasing the magnetic stimulation intensity to the specified levels, and calculates the central motor conduction time based on the stimulus-response curve using the F-wave method, thus completing the initial stimulus and threshold determination. Subsequent threshold tracking is then performed based on this technique, providing an efficient and accurate means of detecting motor cortical function for the diagnosis of neurological diseases.

The invention relates to the technical field of brain-computer interfaces, and provides a rehabilitation training method and system based on a mixed normal form brain-computer interface. The method comprises the steps that electroencephalogram signals of the prefrontal lobe or the motor cortex of a patient are collected; performing cooperative decoding on the electroencephalogram signals according to a preset mixed normal form decoding model, and identifying signal features; if the electroencephalogram signal is a prefrontal lobe electroencephalogram signal, the signal feature is whether to imagine an intention intensity score of walking and movement, and if the electroencephalogram signal is a motor cortex electroencephalogram signal, the signal feature is a motor imagery type and an intention intensity score of movement; generating a lower limb rehabilitation training instruction matched with the rehabilitation stage of the patient according to the signal characteristics, wherein the training instruction at least comprises any one of walking, knee bending, leg lifting, left leg lifting and right leg lifting; the training instruction is transmitted to an execution module of the rehabilitation robot, and the rehabilitation robot is driven to complete corresponding training actions.

A method and system for assessment of cognitive function of an individual. The method includes having an individual execute a standardized cognitive task, followed by a brain perturbation which either excites or suppresses cognitive ability. The individual executes at least one further standardized cognitive task after the brain perturbation. Neuroimaging can be used concurrently while the individual is executing each of the tasks to obtain additional data to supplement the task scores. The post perturbation scores are compared against the pre-perturbation score to provide a standardized metric which can then be compared against individuals of the same gender and approximate age. These results can help predict and diagnose various brain diseases. A fitted helmet with integrated neuroimaging sensors, and with preset cutouts for receiving a TMS coil, facilitates positioning and orientation of the coil proximate the motor cortex and the dorsolateral prefrontal cortex for the above methods.

The application relates to the technical field of brain-computer interfaces, and discloses an intelligent rehabilitation wheelchair based on a brain-computer interface and a control method thereof, wherein the method comprises the following steps: S1: collecting a first electroencephalogram signal of a user's motor cortex, pre-processing the first electroencephalogram signal, and obtaining a second electroencephalogram signal; S2: classifying the second electroencephalogram signal, decoding a wheelchair motion instruction, and calculating a cognitive load level according to the second electroencephalogram signal; and S3: generating a wheelchair speed parameter and a wheelchair turning radius according to the cognitive load level, and driving a wheelchair motor to perform a corresponding motion action based on the wheelchair motion instruction, the wheelchair speed parameter and the wheelchair turning radius. The method improves the motion intention decoding accuracy and reduces the decoding delay, and solves the problem of insufficient decoding performance caused by signal redundancy of a traditional model.

The invention discloses a non-invasive brain network nerve regulation and control system and a control method thereof. Magnetic resonance imaging / diffusion tensor imaging data of a testee are collected and processed, and double-target reliable positioning is carried out; determining an acoustic transducer channel layout and initial acoustic stimulation parameters according to the network model; transmitting a multi-channel multi-mode modulation acoustic stimulation signal, stimulating the cochlea to synchronously generate a multi-mode bioelectric pulse signal, and stimulating the cochlea to generate a multi-mode bioelectric pulse; synchronously or asynchronously acquiring feedback signals of different stages of EEG / EMG and extracting orbicular oculi muscle and cortex response characteristics; acoustic stimulation parameters of all channels are dynamically adjusted and optimized through a feedback algorithm, an acoustic stimulation parameter report is generated, cochlea and deep nuclei thalamus-ganglion-motor cortex loops are accurately excited and regulated to generate electrophysiological response through audible acoustic multi-mode stimulation, and the device has the advantages of being non-invasive, safe, adjustable in height, low in cost and the like.

The application provides a motor imagination training system based on a brain-computer interface, and relates to the technical field of intelligent control, and comprises: a rapid processing module, which is used for acquiring electroencephalogram signals when a training video is watched and performing time-frequency domain and spatial mode processing to obtain dynamic characteristics, and determining a target training video according to a mirror neuron activation probability in the dynamic characteristics; a fine processing module, which is used for acquiring multi-modal sensing signals when the target training video is watched to determine static characteristics of a brain motor cortex, the static characteristics including activation intensity of the brain motor cortex, connection intensity of inter-cortical functions and coupling intensity of nerves and muscles; and an adaptive adjustment module, which is used for adaptively adjusting at least one of an exoskeleton torque, a virtual reality task difficulty and a neural feedback sensitivity according to the activation intensity, the connection intensity and the coupling intensity. The application realizes individualized adaptation of training content and real-time dynamic closed-loop regulation and control of parameters, and improves the effect of individualized training.