94 results about "Neurofeedback" patented technology

The invention discloses a psychotherapy and healing robot based on high human emotion fitting degree simulation analysis, and the robot comprises a multi-mode perception layer which is used for collecting the interaction data of physiology, movement and environment; the multi-modal sensing layer comprises a heterogeneous data acquisition module, a spatial-temporal feature extraction network and an attention fusion mechanism module; the dynamic decision-making layer is used for generating an intervention strategy based on the interaction data; the dynamic decision-making layer comprises a reinforcement learning strategy engine and a hierarchical intervention selection tree; the generative interaction layer is used for generating a co-estrus response conforming to ethical specifications based on the intervention strategy; the generative interaction layer comprises an ethical constraint system and an emotional response generator; the brain science verification layer is used for monitoring neural feedback in real time through EEG and adjusting an intervention strategy; and the brain science verification layer comprises a neural feedback regulation module and a multi-mode feedback design module. Therefore, a precise and personalized psychological intervention decision closed loop is provided, and the defects of an existing AI psychological product in the aspects of emotion recognition, intervention strategies and effect quantification are overcome.

A navigation system and method are disclosed for operation in GPS-denied environments using quantum-inspired sensor fusion, dynamic virtual anchor points (VAPs), and predictive environmental modeling. The system represents multiple position hypothesis using wavefunction-like expansions and integrates VAP-based triangulation for drift correction. A predictive modeling module ingests solar, geomagnetic, and environmental data to proactively adjust sensor weighting. A cybersecurity module employs quantum-algebraic key generation and location-derived ephemeral keys to secure inter-device communication. The system includes an augmented reality (AR) interface to visualize and edit anchor references, and a neurofeedback module that adapts the AR interface based on real-time physiological signals from the user. The method further enables anchor optimization via AI-driven repositioning and supports low-power edge execution using approximate amplitude filtering. Additional modules may include fractal antennas, neuromorphic processors, and adaptive forecasting layers to maintain positional accuracy and user experience in subterranean, multi-floor, or magnetically complex environments.

The invention relates to the technical field of traditional Chinese medicine acupuncture and moxibustion, in particular to a human body acupuncture and moxibustion scheme intelligent screening method and system based on the brain-computer interface technology. Inputting the nerve response characteristic parameters into an acupoint efficacy prediction model, outputting nerve regulation efficacy scores of the acupoints, and generating an initial acupuncture scheme; in the process of executing the initial acupuncture scheme, electroencephalogram signals are collected in real time, and a neural feedback intensity value is calculated; and when the feedback intensity is lower than a preset response baseline, triggering a dynamic weight optimization algorithm, updating the scoring model and generating an optimized acupuncture scheme. According to the invention, objective evaluation and dynamic optimization of the human body to different acupoint nerve responses are realized, and the method has the advantages of high intelligence, high real-time performance, excellent individualized adaptation capability and the like, and is suitable for intelligent decision system deployment for assisting acupuncture therapy.

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.

The invention belongs to the technical field of brain-computer interfaces and cognitive neural engineering, and discloses a gender impressions nerve feedback intervention method based on cooperation of a lightweight electroencephalogram sensor and a mobile terminal. According to the system, polyimide microneedle dry electrodes are adopted, interference is suppressed through double-layer shielding, and low-noise signal collection is achieved in combination with the chopping modulation technology; constructing a BDSAG model based on the alpha / theta frequency band differential entropy, and recognizing gender engraving plate activation, neutral concentration and cognitive fatigue states in real time by using a lightweight graph neural network; the dynamic closed-loop module adjusts the sex-free task density and the end-to-end delay lt according to the neural state; the time is 200 ms. The power consumption of the system is 2.7 mW, and the endurance is gt; the cost is 1 / 4 of that of traditional equipment, and experiments show that the IAT effect value of an intervention group is reduced by 25.3% (plt; 0.01) of the substrate. The method solves the problems of complex wearing, gender characteristic quantification and intervention lag of electroencephalogram equipment, and is suitable for vocational education and other scenes.

The invention belongs to the field of brain-computer interfaces, and particularly relates to a decision-making brain-computer method and device based on virtual reality induction, which combines two psychological decision-making tasks of auditory stimulation and visual stimulation and utilizes virtual reality equipment to create a decision-making brain-computer interface normal form of panoramic interaction, so that a subject can fit a scene facing a decision in reality to the greatest extent, and the accuracy of decision making is improved. The sensory motor cortex is effectively activated, and cooperative activation of the cognitive-motor neural network is induced; a decision interaction feedback link is added to enhance a decision stimulation effect and a cranial nerve feedback mechanism by analyzing related characteristics P300, power spectral density and brain network function connection quantity of the acquired electroencephalogram signals during normal form execution decision reaction. Compared with the prior art, the method has the advantages that immersive audio-visual stimulation and task interaction feedback are brought into a decision-making brain-computer interface for the first time, and a new scheme with neural rehabilitation and human-computer interaction functions is provided for neural feedback training of cognitive functions.

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.

An integrated EEG-fNIRS neurofeedback system for interacting with participant's brain activity includes EEG electrodes, fNIRS detectors, at least one information receiver, a computation module, and a report generator. The EEG electrodes collect EEG signals. The fNIRS detectors collect fNIRS signals. The at least one information receiver receives and processes the collected EEG and fNIRS signals. The computation module executes an EEG and fNIRS signal processing pipeline with the EEG electrodes, the fNIRS detectors, and the information receiver. The computation module is further configured to: calculate score information based on received EEG and fNIRS signals; select a minimum score from the calculated score information; and discard an alternative score that is not selected as the minimum score, so as to enable the computation module to choose a single representative score for the shared target objective from both EEG and fNIRS signals. The report generator provides a report of the selection.

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.

A system operates by: sending gamified neurofeedback displays for display via a graphical user interface of a client device and receiving client device interactions with the graphical user interface from the client device; receiving neurosensing device signals via at least one neurosensing device corresponding to a user of the client device; preprocessing and filtering the neurosensing device signals to reduce artifacts and to produce filtered signals corresponding to a plurality of different brain waves of the user; generating frequency and time analysis data based on the filtered signals; extracting feature data based on the frequency and time analysis data; generating, via an artificial intelligence (AI) neuro-classification engine trained via machine learning, neuro-classification data based on the feature data, generating brain assessment data based on the neuro-classification data, generating, via at least one gaming application, the gamified neurofeedback displays based on client device interactions and / or generating neurofeedback results based on the neuro-classification data.

The invention discloses a music teaching system based on artificial intelligence, and particularly relates to the technical field of artificial intelligence, and the music teaching system comprises a neurocognitive adaptation module, a music DNA map construction module, a cognitive load monitoring module, an anti-AI dependence adjustment module and a multi-source data fusion unit. According to the music teaching system based on artificial intelligence, a dynamically evolved personalized teaching model is constructed through multi-modal data fusion and real-time analysis driven by artificial intelligence. The neurocognitive adaptation module is combined with electroencephalogram feature analysis and physiological signal monitoring to accurately capture cognitive preferences and ability bottlenecks of the learner; the music DNA map is based on the quantum enhancement modeling technology, the skill development trajectory is continuously updated and predicted, the AI teaching strategy can realize millisecond-level dynamic adjustment according to the neural feedback and behavior data of the learner, the skill mastering efficiency and the knowledge retention rate are remarkably improved, and the limitation of staticization and simplification of a traditional teaching system is broken through.

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.

The present invention discloses a brain vision detection method based on neurofeedback for scientific research laboratories, comprising the following steps: fusing the depth information of a visual target on the basis of detecting the two-dimensional position information of an image to achieve visual target detection in a three-dimensional space and obtain the three-dimensional coordinates of the visual target; fitting a response model of brain vision according to the optic nerve feedback signal and the three-dimensional coordinates of the visual target. The present invention takes a deep learning algorithm as the core and integrates the depth information of traditional machine vision, solves the deficiency that a single deep learning algorithm can only obtain two-dimensional coordinate information, and obtains complete three-dimensional coordinates; fitting a response model of brain vision according to the optic nerve feedback signal and the three-dimensional coordinates of the visual target for visual detection of the brain vision to be predicted, and the model recognition improves the efficiency of visual detection and reduces the complexity.

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.

A method for neurofeedback training to output brain-area reality is disclosed. The method includes transmitting a physical and mental parameter related to a subject as a neurophysiological signal; performing signal processing, feature extraction and pattern determination on the neurophysiological signal; providing a neurophysiological feedback parameter and conducting a brain region network activity; and converting a brain-area reality through a brain-computer interface to present an interactive scene and an interactive element to the subject for brain / brain-area (an Electroencephalography (EGG) brain waves and / or brain network) training. In this way, the subject's brain area training status can be known in real time and the subject can understand the state of his own brain area through visual means, so as to facilitate communication between subjects (or their family members or related persons) and professionals (such as doctors).

The present invention discloses a method and apparatus for constructing a brain-computer interface system for neurofeedback training, relating to the field of neurofeedback training. The method comprises: designing a T-shaped channel layout for a near-infrared brain imaging device, including: employing a T-shaped arrangement of paired light sources and receivers on a plane, wherein adjacent light sources and receivers are combined to form data acquisition channels, and the multiple data acquisition channels thus formed can be mapped to the entire brain; configuring a BCI communication module, a bandpass filtering module, a baseline calculation and adaptation module, a feedback mode selection and calculation module, and an interactive display module within a brain-computer interface computer connected to the near-infrared brain imaging device. The present invention can determine the regulatory effects of resting-state brain states during various task feedback training sessions.

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.

The telemedicine system includes a gateway for transmitting monitoring information including a stimulation signal and a biosignal corresponding to the stimulation signal to a cloud; and a server for extracting the stimulation signal and the biosignal from the transmitted monitoring information, providing the extracted stimulation signal and biosignal as inputs of a pre-stored artificial intelligence machine learning algorithm-based stimulation control model, regenerating the biosignal as an output of the stimulation control model into a stimulation signal to be regulated for the balance between sympathetic and parasympathetic nerves, and feeding back the regenerated stimulation signal to a personalized vagus nerve stimulation and pulse electromagnetic field treatment device.

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.