74 results about "Neurofeedback" patented technology

The invention discloses an intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback, which comprises a multi-modal physiological signal acquisition module for acquiring electroencephalogram signals of a target child in real time and generating an original physiological signal set; the multi-modal fusion analysis module receives the original physiological signal set and generates a comprehensive evaluation signal representing the current neurocognitive state and the emotion awakening level of the child; the self-adaptive decision control module receives the comprehensive evaluation signal and generates a self-adaptive control signal containing a neural feedback parameter adjustment instruction and a game interaction strategy instruction; the neural feedback intervention module receives the neural feedback parameter adjustment instruction and generates an adaptive neural feedback stimulation signal to act on the child; and the intelligent interactive game module receives a game interactive strategy instruction and dynamically adjusts game scene contents. The intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback can solve the problems of poor individual adaptation of autism intervention, multi-modal data splitting and lack of dynamic adjustment.

The invention relates to a hearing aid intelligent noise reduction and human voice enhancement system based on electroencephalogram signals, and belongs to the field of biomedical engineering and acoustic signal processing. The system comprises an electroencephalogram signal acquisition module, a multi-channel acoustic sensor array, an embedded neural signal processor, an adaptive beam forming module, a dynamic speech enhancement engine and a dual-mode output device, and constructs electroencephalogram-acoustics joint features by extracting an alpha / theta wave power ratio, a P300 component and auditory cortical Gamma phase synchronism. A deep network is driven to separate target voice, a wave beam direction and a frequency response curve are dynamically adjusted based on neural feedback, a closed-loop calibration unit is innovatively adopted, gain is reversely adjusted according to N1-P2 wave amplitude, heart rate variability and eye movement data are fused to optimize decisions, and when the signal-to-noise ratio is-5dB, the voice recognition rate reaches 89%, the auditory fatigue is reduced by 37%, and the decision conflict rate is smaller than 6%. The defects of attention blind area, noise separation failure and physiological adaptation of a traditional hearing aid are overcome. The system is suitable for the fields of hearing impairment rehabilitation, special communication and intelligent cabins.

The invention discloses a continuous attention neural feedback training method and system based on a brain-computer interface, and relates to the technical field of neural feedback, and the method comprises the steps: collecting a multi-channel electroencephalogram signal of a user in visual task training in real time; extracting power spectral density characteristics of the multi-channel electroencephalogram signals in a beta frequency band, classifying the power spectral density characteristics by adopting a support vector machine algorithm, and outputting a judgment result of an alert or non-alert state; and according to a judgment result, dynamically adjusting an information fusion proportion alpha value in the visual task through a reward-punishment mechanism, updating image information feedback in the visual task in real time, and adjusting the attention state of the user through an image information feedback result. Neural feedback and a dynamic reward and punishment system are fused, real-time excitation feedback is obtained by autonomously adjusting electroencephalogram activity, the problem of insufficient training power caused by traditional static tasks or single positive feedback is solved, and the long-term training effect is enhanced.

Disclosed are devices, systems and methods for acquiring, analyzing, and utilizing neurofeedback to promote brain coherence. Neurofeedback is a form of biofeedback that allows an individual to regulate his / her brain activity by providing a visual metaphor of brain function, thereby making it accessible for manipulation. In some embodiments of the present technology, a system includes a brain signal detection device wearable by a subject and a computer device including a display and a brain-computer interface (BCI) configured to monitor brain signals and display visual, auditory, and / or tactile stimuli to the subject according to a neurofeedback threshold-based protocol to deliver brain signal coherence between the left and right hemispheres of a subject's brain.

The present invention relates to the technical fields of human-computer interaction and brain informatics, and in particular to a closed-loop neurofeedback method and device based on haptic memory electroencephalogram signal conditioning. The method comprises: collecting electroencephalogram signals of a subject, and performing real-time online data preprocessing; designing a haptic memory stimulation task, activating the haptic memory of the subject, and recording a corresponding electroencephalogram signal response; performing feature extraction and analysis on a corresponding electroencephalogram signal to obtain an electroencephalogram feature related to haptic memory; and on the basis of a haptic memory electroencephalogram feature signal, designing a closed-loop neurofeedback system, the closed-loop neurofeedback system monitoring the electroencephalogram signals of the subject in real time, performing real-time stimulation on the basis of a preset haptic stimulation task, and recording a corresponding electroencephalogram response. In the technical solution of the present invention, the haptic memory electroencephalogram feature signal is combined with the closed-loop neurofeedback system, thus providing new ideas and methods for the diagnosis and treatment of haptic memory-related diseases.

The invention discloses an optical and neural feedback multi-modal data analysis method for a nerve regulation target, and belongs to the technical field of cranial nerve treatment, and the method specifically comprises the steps: receiving a head movement instruction of a patient through an interactive interface, pausing transcranial magnetic stimulation after receiving the instruction, and switching to a head movement monitoring state; an infrared optical navigation device is combined with a magnetic resonance image to construct an individualized brain three-dimensional model, head position changes are tracked in real time, and autonomic nerve feedback signals are collected at the same time; synchronously aligning the head coordinates with the neural feedback signals to generate a multi-modal data set, and analyzing relevance to obtain real-time regulation and control parameters; the positioning and stimulation intensity of the transcranial magnetic stimulation coil are adaptively adjusted according to the parameters, and related information is displayed on a treatment interface; after the head of the patient finishes moving and is stable, a rapid re-calibration process is automatically triggered, high-precision monitoring is recovered, and magnetic stimulation output is reactivated; according to the invention, the treatment stability and adaptability are improved.

The present invention specifically relates to a method and apparatus for tactile-motion electroencephalogram signal-based closed-loop neurofeedback training. The method comprises: collecting the current finger somatosensory temporal discrimination threshold and electroencephalogram data of a test individual; using a closed-loop neurofeedback system and an electroencephalogram amplifier to perform neurofeedback training on the test individual; collecting the current finger somatosensory temporal discrimination threshold and electroencephalogram data of the test individual again, and by means of processing behavioral data of the finger somatosensory temporal discrimination threshold of the test individual and electroencephalogram feature data of the test individual, acquiring an effect after the neurofeedback training. By utilizing the impairment of a primary somatosensory cortex to extract tactile electroencephalogram feature indicators that are less constrained by cognitive abilities, the present invention enables a patient, during the closed-loop neurofeedback training, to observe in real time signals of the patient's brain-related electroencephalogram feature indicators and perform self-regulation, thereby more effectively changing the patient's own basic neural mechanisms, and achieving the control and intervention of early Alzheimer's disease.

The invention relates to the technical field of artificial intelligence and big data, and discloses a learning disorder assessment and adaptive training system and method based on multi-dimensional behaviors, and the system comprises a multi-modal assessment module, a path generation module, a nerve regulation and control module and an adaptive engine module. By fusing multi-modal data features of language behaviors, electroneurographic signals and task response behaviors and combining a cross-modal attention mechanism to dynamically construct a cognitive state recognition model, learning disorder typing accuracy is improved, multi-dimensional ability defects such as attention, memory, language understanding and execution control are quantified based on a three-dimensional cognitive map, and learning disorder typing accuracy is improved. It is ensured that the assessment result comprehensively covers the core dimension of cognitive impairment; transcranial direct current stimulation is synchronously triggered in the cognitive task starting stage, space-time precise matching of task state nerve regulation and brain region activation is achieved, stimulation target point coordinates and current intensity are dynamically adjusted according to real-time nerve feedback, and the neuroplasticity reconstruction efficiency is improved.

The invention particularly relates to a closed-loop neural feedback training method and device based on tactile-motion electroencephalogram signals. The method comprises the steps that the current finger somatosensory time discrimination threshold and electroencephalogram data of a tested individual are collected; performing neural feedback training on the tested individual by using a closed-loop neural feedback system and an electroencephalogram amplifier; and collecting the current finger somatosensory time discrimination threshold and the electroencephalogram data of the tested individual again, and processing the behavioral data of the finger somatosensory time discrimination threshold of the tested individual and the electroencephalogram characteristic data of the tested individual to obtain an effect after neural feedback training. According to the method, the primary somatosensory cortex damage is utilized, fewer index tactile electroencephalogram characteristic indexes constrained by cognitive competence are extracted, a patient can observe signals related to the electroencephalogram characteristic indexes of the brain in real time in the closed-loop neural feedback training process, self-adjustment is carried out, and the accuracy of the patient is improved. Therefore, the basic nerve mechanism of the human body is more effectively changed, and the early Alzheimer's disease is controlled and intervened.

The invention particularly relates to a self-adaptive closed-loop brain-computer interface neural feedback training system, and relates to the technical field of brain-computer interfaces and neural feedback. A multi-modal feature extraction module; a coefficient fusion module; and a feedback training module. In the invention, a high-precision crystal oscillator clock is adopted to realize time synchronization of electroencephalogram, eye movement and behavior signals, fusion distortion caused by signal dislocation is thoroughly eliminated, and the three types of signals respectively cover cognitive states, visual attention and motion characteristics to form complementary state evaluation dimensions; a refined quantization algorithm is designed for each mode, wherein instantaneous artifacts are eliminated through extreme value screening of the electroencephalogram coefficient, the pixel diameter is calibrated into the physical diameter through the eye movement coefficient so as to eliminate imaging interference, and the large-amplitude movement intensity and high-frequency posture micro change are considered in the behavior coefficient.

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 invention discloses a task-driven fNIRS decoding neural feedback training method and a task-driven fNIRS decoding neural feedback training system, which are characterized in that an individualized decoder is constructed by utilizing task state fNIRS data in a pretest stage, and a preselected feature set with the strongest discriminating power is screened out, so that a stable brain activity mode highly related to a specific cognitive function is targeted to neural feedback training; therefore, a foundation is laid for realizing accurate nerve regulation and control; in a real-time training stage, the system continuously decodes the brain activity into a positive state instead of a negative state as a feedback index, so that a subject can induce a target neural mode through self-regulation without being exposed to external unfavorable stimulation completely, and the target neural mode can be used as a target neural mode. The training pertinence and nerve specificity of advanced cognitive functions such as interference control are enhanced, a closed-loop training mode based on data driving and an individualized model can be applied to treatment of psychological and mental disorders, and discomfort and risks possibly brought by a traditional exposure therapy are effectively avoided.

The invention discloses a target detection dual-threshold self-adaptive regulation and control method based on a neural feedback state machine, belongs to the technical field of brain-computer fusion, and solves the problems that an existing brain-computer fusion system is low in collaborative efficiency and poor in robustness in a dynamic environment. The method comprises the following steps: performing target detection on an image flow task, and outputting a target detection result of each target in each frame of image; the target detection result comprises a category label of a target and a confidence score corresponding to the category label; electroencephalogram signals generated by an operator in the process of responding to the image flow task are received, and multi-dimensional feature extraction is conducted on the electroencephalogram signals; fusing the results of the multi-dimensional feature extraction to obtain a comprehensive quality index; the neural feedback state machine performs self-adaptive regulation and control on target detection double thresholds according to the relation between the comprehensive quality index and the high and low thresholds of the electroencephalogram quality state; and according to the relation between the target detection double threshold values after self-adaptive regulation and control and the confidence score, discriminating and outputting each target.

The invention belongs to the field of flight personnel state monitoring, and discloses a meta-consciousness ability training method and system based on a neural biofeedback technology. The system can obtain neural activity data directly related to the distraction state in a real and continuous task situation, hysteresis and unreliability caused by dependence on subjective reports or behavioral expressions are avoided, and the accuracy and objectivity of distraction monitoring are improved from the source. The decoding model is embedded into a closed-loop neural feedback training system, so that an acoustic feedback signal can be triggered in time when a distraction state occurs, a trainee is prompted to perceive attention deviation, and the self consciousness state is autonomously regulated and controlled without external guidance. The closed-loop feedback can strengthen a conditioned reflex mechanism paying attention to a regression task, and the self-monitoring and self-adjusting capability of meta-awareness is remarkably improved.

The invention relates to a brain region positioning magnetic resonance data processing method based on nerve stimulation target optimization, and the method comprises the steps: obtaining the functional magnetic resonance imaging data of a subject, extracting the signal response sequence of each brain region before and after nerve stimulation through time domain analysis, and determining the brain region with a characteristic response time window according to the response delay difference; the method comprises the following steps: detecting delayed oscillation characteristics in a cortex signal based on a neural feedback relationship between a cortex region and a deep structure to determine potential regulatory pathways of thalamus and other deep brain regions so as to position indirect stimulation targets which cannot be directly imaged; decoupling analysis of cerebral blood flow signals and nerve activation signals is carried out on the candidate brain area, and when it is detected that blood flow fluctuation exists in the area but synchronous nerve activation is lacked, it is judged that the area is a pseudo activation area and excluded; according to the method, the time delay correlation degree between the stimulation sequence and the brain region signal is calculated through the improved cross-correlation integral function, and the high-frequency noise interference is suppressed in combination with the nonlinear attenuation item, so that the estimation of the brain region response delay is more stable and repeatable.

This invention belongs to the field of non-invasive neurointervention technology, and relates to a balance perception enhancement system and method based on neurofeedback. The balance perception enhancement system includes: a balance perturbation application module, a data acquisition and processing module, a neurofeedback parameter calculation and mapping module, and a feedback information presentation module. The neurofeedback parameter calculation and mapping module calculates neurofeedback parameters, configures threshold ranges for these parameters, calculates feedback values ​​based on the parameters and threshold ranges, and maps the neurofeedback parameters to neurofeedback information based on the feedback values. The feedback information presentation module presents the neurofeedback information to the trainee in a visual form, allowing the trainee to adjust their neural activity in real time based on the presented information to enhance their balance perception ability. This invention also provides a balance perception enhancement method based on neurofeedback. The system and method proposed in this invention can effectively enhance the balance perception ability of trainees.

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 invention discloses an ADHD neural feedback training system and method based on bimodal fusion, and belongs to the technical field of electroencephalogram and eye movement tracking, and the ADHD neural feedback training system is composed of four core modules: a data acquisition module, a feature fusion module, a classification recognition module and a real-time feedback module. According to the method, the limitation that traditional recognition dimensions are single in ADHD neural feedback regulation is broken through, finer-grained judgment is achieved by fusing electroencephalogram and eye movement behavior information, a judgment index is upgraded to a comprehensive decoding attention state from multi-dimensional decoding fusing visual gaze and neural activity, the accuracy and adaptability of attention state recognition are remarkably improved, and the method is suitable for popularization and application. A bimodal time synchronous acquisition and alignment mechanism is provided, and the problems of delay and errors existing in the modal fusion stage of an existing system are solved.

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 invention discloses a neural feedback brain concentration state detection and adjustment method and system, and relates to the technical field of electroencephalogram signal processing and neural feedback. Acquiring original electroencephalogram signals of the target object in an eye-opening resting state and a target task state; performing noise suppression processing to obtain noise-reduced electroencephalogram signals, calculating full-band power spectral density data by adopting a Welch method, and extracting power information corresponding to a theta band and a beta band; calculating a single lead concentration index based on the power information of the two types of frequency bands, and determining a reference concentration threshold by combining quartile abnormal elimination and statistical operation; and circularly executing the signal acquisition and processing flow to obtain a real-time concentration index and verify the validity, comparing the real-time concentration index with a reference threshold value to generate a hierarchical adjustment instruction, outputting a matched audio-visual feedback signal to realize neural feedback adjustment, and dynamically calibrating the reference threshold value after preset training times are completed. The method improves the accuracy of concentration state detection and the pertinence of adjustment, adapts to different groups and scenes, and is high in practicability.

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.

A neurofeedback system includes an EEG apparatus, a presentation apparatus and a controller. The controller is configured to: (i) cause the presentation apparatus to display an overlaid image to the user that comprises a first image flickering at a first frequency and a second image flickering at a second frequency different than the first frequency, the first image being an affective distractor stimulus image and the second image being a task-relevant stimulus image, (ii) receive from the EEG apparatus a number of first steady-state visual evoked potential (SSVEP) signals generated in response the first image of the overlaid image and a number of second SSVEP signals generated in response the second image of the overlaid image, and (iii) calculate feedback indicative of how much attention of the was user allocated to the task-relevant stimulus image versus how much attention of the user was allocated to the affective distractor stimulus image.

Provided is a neurofeedback device with elastic electroencephalography (EEG) cap, including: a cap body, a knob base, an electrode device, and a signal amplifier. The cap body is made of elastic material and includes a circuit layer and a buffer material. The buffer material covers the circuit layer elastically to ensure a wearer's comfort. The knob base passes through the cap and is electrically connected to the circuit layer. The electrode device includes a knob shell, a buckle cover and an electrode head. The knob shell has an inner space, and an outer surface with an external thread for threading to an internal threaded hole of the knob base to allow the electrode device to rotate for adjusting the wear tightness. The signal amplifier is used to receive electroencephalogram signals sensed by the electrode device, amplifies, analyzes, and transmits the signals to external devices.

The application provides a specific neurofeedback training system for improving anxiety based on multi-modal fusion, which forms specific electroencephalogram signals through the projection strategy of magnetoencephalogram signals to electroencephalogram, and is used for the regulation and improvement of anxiety emotion. The application belongs to the technical field of medical treatment, and comprises an electroencephalogram acquisition module, a real-time processing module and a visual feedback module which are connected with each other; a complete treatment closed loop from signal acquisition, signal processing, signal feedback and signal acquisition is realized. The application decodes and analyzes electroencephalogram signals in real time, extracts features, and uses a specific mapping model of multi-modal feature fusion to establish a specific electroencephalogram mapping signal reflecting the real-time activity of core brain regions related to emotion (such as amygdala), which has stronger spatial accuracy and symptom specificity; the specific mapping signal is applied to neurofeedback treatment, which can assist in formulating individualized and multi-course neuroregulation training, improving the anxiety emotion of users, and providing a non-invasive and convenient neuroregulation platform for users.

The invention relates to a neural feedback training system and method based on aperiodic electroencephalogram components, and the system comprises an electroencephalogram collection device, a data processing unit and a feedback presentation unit. In a baseline stage, an electroencephalogram power spectrum is decomposed to obtain an individual aperiodic index, and a baseline mean value and a standard deviation are calculated; meanwhile, estimating and storing an eye movement regression coefficient; in a real-time training stage, the same processing chain is multiplexed to carry out filtering, re-reference and eye movement artifact removal on electroencephalograms in a sliding window, a current aperiodic index is extracted, standardization is carried out relative to a baseline, double-target visual feedback is driven through nonlinear mapping and smooth interpolation, and training success is judged based on a threshold value and continuous detection. According to the scheme, individualization and ecological effectiveness are improved, interpretability is enhanced, a low-delay stable closed loop is achieved, and the method is suitable for scenes such as cognitive control training, pressure emotion regulation and educational training.

The application discloses a kind of active and passive pressure regulation system and method based on individualization music eeg neural feedback, the system includes: through the eeg acquisition module of evoked eeg, the eeg signal of subject is collected to carry out music neural feedback experiment, through eeg signal preprocessing module, eeg signal is preprocessed, through pressure feature extraction module, the eeg signal after pre-processing is carried out feature extraction, obtain pressure eeg feature, through pressure state music feedback module, individual baseline threshold analysis is carried out to pressure eeg feature, obtain individualization music feedback to carry out real-time adjustment to the music played to subject, guide the pressure state regulation iterative training of subject.

The invention provides a work memory ability training method based on electroencephalogram neural feedback, a storage medium and equipment, and the method comprises the steps: obtaining electroencephalogram signal data of a tested object in a process of executing a training task, and obtaining task execution result data of the tested object, the training task at least comprising a digital memory breadth task and a spatial memory breadth task; extracting physiological features of the electroencephalogram signal data, and calculating a cognitive state index and a global cognitive ability index according to the physiological features and the task execution result data; the training task is adjusted according to the global cognitive ability index, and the tested object is trained again according to the adjusted training task; and when it is judged that at least one of the training times, the global cognitive ability index and the cognitive state index meets a preset training condition, generating a training report at least including the cognitive state index of the tested object during each training. The computing resource consumption, the training delay and the deployment cost in the multi-dimensional training of the working memory ability are reduced.

A system (114) for dynamically optimizing content engagement for a subject (112) is disclosed. The system (114) includes a plurality of biosensor electrodes (102), a controller (106), and a recommendation engine (108). The biosensor electrodes (102) measure at least one physiological parameter, i.e., EEG signal, of the subject (112). The controller (106) detect response generated in a brain of the subject, while consuming the content, via at least one Artificial Intelligence Model, based on the EEG signal. The controller (106) performs filtering of the content and dynamically curate and recommend content based on the detected response via another Artificial Intelligence Model and transmits, simultaneously, a signal associated with the detected response to an engagement analysis engine to generate content neurofeedback insights. The recommendation engine optimize, dynamically, the content engagement and media delivery strategies via refining content recommendations, based on the content neurofeedback insights.

This invention belongs to the field of mixed reality technology. It provides an ADHD neurofeedback training system and method based on EEG-based multi-user collaboration. Using an intelligent car off-road adventure as a backdrop, it constructs rich and personalized MR scenes. Neurofeedback training is conducted by controlling the intelligent car along a self-planned walking route through attention control. Users can play together individually or in groups, choosing different control modes. Low-cost EEG equipment is used to acquire users' brainwave signals, calculate each user's attention level to control the intelligent car's speed, and provide corresponding feedback using MR effects based on each user's attention level, making the neurofeedback training process more effective.

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.