12 results about "Neurofeedback" patented technology

This invention relates to the field of neuromorphic computing technology and provides a method and system for dynamic adjustment of synaptic weights in real-time neurofeedback for neuromorphic chips. The method includes: capturing the pulse signals and timestamps emitted by presynaptic and postsynaptic neurons; calculating the time difference between the presynaptic and postsynaptic pulses; when the absolute value of the time difference is less than a preset time window threshold, querying a pulse timing dependency plasticity rule base based on the sign of the time difference to determine the corresponding synaptic weight adjustment type; generating corresponding voltage pulse parameters based on the adjustment type and the current conductance state of the target memristor synapse; and applying a write voltage pulse to the target memristor synapse according to the voltage pulse parameters to adjust its conductance value in situ in real time, thereby dynamically updating the synaptic weights. This invention solves the problems of poor dynamic environment adaptability, low energy efficiency, and high learning latency caused by traditional offline weight update mechanisms.

The application relates to the cross field of biomedical engineering and artificial intelligence, and discloses a personalized brain power development training method based on electroencephalogram signals. The method comprises the following steps: collecting resting state and task state multi-channel electroencephalogram signals of a subject, constructing a functional connection matrix after pretreatment, identifying individualized weak connection target points through difference operation and cluster analysis; matching a neural feedback training protocol from a preset paradigm library based on the target points, and generating a feedback signal by extracting a target point synchronicity feature in real time during training to guide the subject to actively enhance the weak connection; updating the model after each training and dynamically optimizing subsequent parameters to form a closed-loop regulation. The application improves working memory and attention through individualized targeted training, induces neural plasticity, and realizes efficient and accurate brain power development.

The application discloses an evaluation system for an epilepsy electroacupuncture treatment target based on an amygdala nerve response prediction, relates to the technical field of nerve regulation and brain function modeling, and realizes the multi-modal synchronous collection of brain region potentials, blood oxygen and metabolic activities through high-density EEG, electrophysiological sensors, fMRI and PET, and constructs a standardized nerve response dataset and a brain region function mapping matrix. An amygdala prediction response index is calculated and compared with a response threshold, a stimulation response compliance judgment and parameter correction are realized. The nerve response propagation delay, cooperative coupling coefficient and epilepsy wave front tracing time are calculated, a comprehensive target evaluation coefficient is obtained, and effective target points are screened. Based on the treatment scheme implementation of the effective target point set, the nerve feedback data in the continuous treatment cycle is monitored in real time, and a nerve plasticity correction index is calculated, compared with a plasticity stability threshold, an adaptive feedback regulation closed loop is formed, and precise prediction and dynamic optimization control of epilepsy electroacupuncture targets are realized.

The present invention relates to a real-time interactive cognitive guidance system and method using a Magnetic-Sync Engine (MSE) that monitors a user's cognitive state in real time and pre-projects a target trajectory to compensate for the delay in the brain's visual information processing. The MSE of the present invention calculates a virtual gravitational force corresponding to the distance between the user's input point and the target trajectory to pull the user toward the target path like a magnet, and forms a cognitive highway by projecting a visual guide 0.1 to 0.2 seconds earlier than the expected point the user will reach through pre-pulling timing control. By fusing multimodal sensors such as eye tracking and tactile input, the cognitive load is precisely calculated, and accordingly, the size of the virtual gravitational force and the shape of the trajectory can be varied in real time. Upon successful synchronization, neurofeedback is provided through solfège frequency audio and blooming animation to maximize learning efficiency, and the invention can be applied to various fields such as medical rehabilitation, cognitive training, and precision work education.

The present application relates to the technical field of signal processing, and discloses a muscle tension nerve feedback electric signal data processing method, which comprises preliminary noise reduction, noise suppression, feature extraction, determination of muscle tension baseline and nerve feedback threshold, muscle tension data calculation and correction: a multi-stage noise reduction mode combining sliding average filtering, median filtering and wavelet transform is adopted to effectively remove periodic, pulse and high-frequency noise; the root mean square value and discrete Fourier transform are used to extract features from amplitude and frequency components in multiple dimensions, so as to accurately reflect physiological information such as muscle contraction strength and fatigue state; the muscle tension baseline is determined by collecting individual relaxation state signals, and the nerve feedback threshold is set in combination with subjective feeling, so that the individualization and accuracy of evaluation are enhanced; muscle tension data are calculated in real time, and are dynamically corrected according to energy characteristics of different frequency bands, so that the evaluation result is further optimized, and high-precision and reliable data support is provided for muscle function evaluation and nerve feedback treatment.

A method and a system of optimizing a neurofeedback training session are presented. The neurofeedback training session includes an alternating sequence of neurofeedback training periods and cognitive tasks to be performed by a user on a computer device. An EEG signal is obtained from electrodes worn on the user's head during the neurofeedback training periods and during the cognitive tasks, the EEG signal including, amongst others, alpha waves and beta waves. A relative power is determined based on at least an alpha power of the alpha waves and a beta power of the beta waves. One or more characteristics of the relative power over time are determined during the neurofeedback training periods and during the cognitive tasks. A duration of the neurofeedback training periods and / or the cognitive tasks are set based on the one or more characteristics.

The present application relates to the field of electroencephalogram signal processing and artificial intelligence technology, in particular to a method and system for continuous evaluation of electroencephalogram state based on individual baseline correction. The method comprises: collecting and preprocessing electroencephalogram signals; extracting time-frequency domain and brain network features; constructing an individual baseline model using user resting state data; dynamically adjusting and fusing real-time features based on the model to generate a fused feature sequence; calculating a continuous state value through an evaluation function; and finally mapping it to a preset state level output. The core of the present application is to eliminate inherent differences between users through individual baseline correction and to adaptively capture key state information through dynamic feature fusion, thereby achieving personalized, continuous and high-robust evaluation of electroencephalogram state, significantly improving the model generalization ability and practical value, and can be widely applied to intelligent neuroengineering fields such as neurofeedback, fatigue driving monitoring, cognitive ability evaluation and the like.

The application provides a method and system for detecting non-responders of neurofeedback, which can extract features from electroencephalogram (EEG) signals after obtaining the EEG signals and behavior data, extract EEG features, calculate an intervention effect value according to the EEG features and the behavior data, evaluate the intervention result of neurofeedback training according to the intervention effect value, and then label the EEG category of each EEG feature. Then, a neural network model is trained according to the EEG features labeled with EEG category labels to obtain a neurofeedback detection classification model. The intervention effect value is used to represent the intervention effect of neurofeedback training. The method combines EEG data and behavior data at different stages to more comprehensively and accurately measure the intervention effect of neurofeedback training, thereby improving the detection accuracy of the neurofeedback result.

This invention discloses a personalized binaural beat music generation method based on quantitative analysis of EEG frequency bands. This method automatically parses eight mainstream EEG data formats and standardizes the data through adaptive preprocessing. It uses the Welch periodogram method to calculate the power spectrum of five frequency bands, extracts key clinical indicators, and outputs a quantitative state score. Based on the EEG state, it intelligently recommends or allows manual selection of target beat frequencies, embedding the binaural beats into user-selected background audio using AM double-sideband modulation technology. A quantitative verification step for the modulation effect is introduced to ensure the effectiveness of neural feedback. This invention achieves full-process integration of EEG analysis, personalized recommendation, audio synthesis, and playback control, providing a scientific, convenient, and quantifiable technical solution for mood regulation, concentration improvement, and sleep enhancement.

The application is particularly a self-adaptive closed-loop brain-computer interface neural feedback training system, and relates to the technical fields of brain-computer interface and neural feedback, comprising: a multi-modal signal acquisition module; a multi-modal feature extraction module; a coefficient fusion module; and a feedback training module.In the application, high-precision crystal oscillator clock is adopted to realize time synchronization of electroencephalogram, eye movement and behavior signals, and fusion distortion caused by signal misplacement is completely eliminated.Three types of signals cover cognitive state, visual attention and motion characteristics respectively, forming complementary state evaluation dimensions; and fine quantization algorithms are designed for each modality: electroencephalogram coefficients are screened by extreme value to eliminate instantaneous artifacts, eye movement coefficients calibrate pixel diameter to physical diameter to eliminate imaging interference, and behavior coefficients take into account large motion intensity and high-frequency posture micro-variation.

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.