130 results about "Neurofeedback" patented technology

The invention relates to the technical field of neural rehabilitation training, and discloses a neural feedback rehabilitation training method and system. According to the method, electroencephalogram, near-infrared, myoelectricity and heart rate variability signals are acquired based on a multi-modal nerve-physiological signal acquisition device, and a nerve-physiological signal sequence is generated and input into a fusion processing system. And carrying out denoising, artifact removal and feature extraction on the data sequence to generate standardized feature data. And based on the standardized data, identifying the neural state of the user by utilizing a graph neural network, and generating personalized neural state trend data by adopting an attention mechanism in combination with historical data. Based on trend data and a training target, reinforcement learning is adopted to generate a personalized training strategy, training is implemented through an immersive interaction system, user behaviors and neural feedback are recorded, the training strategy is dynamically adjusted and uploaded to a cloud end, and a cloud-side collaborative optimization neural state model and algorithm are adopted, so that the intelligence and accuracy of rehabilitation training are improved.

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 technical field of mixed reality. According to the ADHD neural feedback training system and method based on EEG multi-person cooperation, intelligent car cross-country adventure serves as the background, a rich personalized MR scene is constructed, an intelligent car is controlled to run along an autonomously-planned walking route through attention so as to conduct neural feedback training, a user can choose different control modes to play together by one or more persons, and the user experience is improved. The low-cost EEG equipment is used for obtaining the electroencephalogram signals of the users, the attention level of each user is calculated to control the speed of the intelligent trolley, corresponding feedback is provided by using the MR effect according to the attention level of each user, and the neural feedback training process is more effective.

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 invention discloses a child attention training device, and belongs to the technical field of attention training, and the device comprises a head-mounted device which is used for monitoring the brain activity of a child in real time, obtaining EEG and heart rate variability data, and providing a basis for neural feedback training; and the entity touch type operation table is used for training a child to perform a positive feeling training, a Schirt grid training, a visual tracking training, a memory game training and a reaction speed training. According to the invention, the problems that the attention training difficulty cannot be automatically adjusted, the attention training progress of the child cannot be tracked and corresponding feedback and suggestions cannot be provided according to the performance of the child in the prior art, so that the attention training effect of the child is poor are solved; according to the method, the attention training difficulty is automatically adjusted to achieve individualized attention training of children, the attention training progress of the children can be tracked, corresponding feedback and suggestions are provided to help the children to know own performance and adjust training strategies, and the attention training effect of the children can be improved.

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 discloses a self-adaptive closed-loop neural feedback system for improving anxiety based on individual specificity and an implementation method, and the system comprises a data collection and processing module, an open-loop control module and a self-adaptive negative feedback module, a complete treatment closed loop from signal acquisition and processing to open-loop control to adaptive negative feedback to signal acquisition and processing is realized; an individual fuzzy controller unit is combined with a traditional electroencephalogram neural feedback framework, the anxiety state of an individual is monitored and adjusted in real time, the task difficulty is accurately changed, and therefore the optimal anxiety intervention effect is rapidly achieved. According to the method, the self-adjusting ability of each individual within a period of time is reflected through iterative evaluation of the anxiety state of the individual, and the task difficulty is adjusted according to different individual characteristics, so that the individual is stimulated to play a higher potential; neural feedback training is carried out by applying the self-adaptive task difficulty, individualized and accurate multi-course nerve regulation training can be made in an auxiliary mode, and the anxiety emotion of an individual is improved.

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.

A method for training a predetermined subject population expected to experience or is experiencing at least one symptom of depression, including: selecting a population of subjects expected to experience in the future or is experiencing at least one symptom of depression; training the selected population of subjects using a neurofeedback (NF) training protocol to increase an activity of the brain positive valence system, the training comprises, presenting to each subject of the selected population of subjects a sensory indication, instructing the subject to modify the presented sensory indication by performing at least one specific activity, detecting an increase in a brain pleasure response in the subject, and providing to the subject a feedback signal indicating the increase in the brain pleasure response during the presenting.

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 discloses a neural feedback intervention system and method based on meditation and meditation, and belongs to the technical field of neural intervention. The problems that existing meditation intervention equipment is mostly based on directional voice guidance or respiratory regulation, personalized and dynamic psychological intervention cannot be performed by fully combining with a neural feedback technology, and an expected standard is difficult to realize are solved, the electroencephalogram signal of an intervention object is monitored in real time, and the meditation intervention accuracy is improved. Mapping the features into a specified interval by using a neural feedback index normalization model to obtain a normalized neural feedback index; converting the audio signal into an audio signal through a time domain signal processing technology; personalized psychological intervention content is provided for an intervention object in combination with a system based on a meditation technology; in the intervention process, the physiological status of the intervention object is monitored in real time, the current intervention scheme can be properly adjusted in time, the effect of meditation training and the experience of the intervention object are effectively improved, and high practicability and market prospects are achieved.

The biometric characteristics of a user are collected and aggregated for use as inputs to control computing devices, and a neurofeedback system facilitates the user's learning of the computer controls. The biometric characteristics of the user are translated into real-time inputs for computing devices. The neurofeedback system provides visual, audio, and / or haptic feedback on the physical, behavioral, physiological, and / or psychological states of the user. The graphical user interface elements indicate the status of computer controls to the user in real time, providing information on whether or not the user is activating the controls.

Systems and methods of neurofeedback intervention reduce the negative effects of neurological disorders, including Parkinson's disease, essential tremor, dystonia, epilepsy, dysfunction following stroke, obsessive-compulsive disorder, Tourette syndrome, dementia, complex regional pain syndrome, and the like. Haptic devices, including eccentric-mass actuators and computer-controlled tightening haptic straps provide haptic sensation to a body. The haptic sensations are controlled by a haptic control signal determined by a control program. The control program may determine the haptic control signal from a neurological signal from an EEG, an electrode detecting a signal in a nerve fiber, or an accelerometer. Haptic therapy un-trains and desynchronizes abnormal neuron synchrony, to reduce the negative effects of neurological disorders.

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.

The application discloses a kind of fatigue monitoring intelligent head ring and method based on electroencephalogram differential entropy and spectral feature, fatigue monitoring intelligent head ring includes electroencephalogram signal acquisition module, pre-processing unit, feature extraction unit, fatigue judging unit and neural feedback unit.Electroencephalogram signal acquisition module is used to collect user forehead single channel electroencephalogram signal and carry out preliminary filtering amplification;Pre-processing unit carries out pre-processing to signal and obtains pure electroencephalogram signal;Feature extraction unit extracts frequency domain feature and time domain feature from the electroencephalogram signal after pre-processing, fatigue judging unit is based on individualized baseline model and dynamic threshold, calculates fatigue score and judges whether in fatigue state;Neural feedback unit triggers audiovisual alarm when judging fatigue, guide user self-regulation brain state.The application can monitor user fatigue state in real time, accurately, individualizedly, realize fatigue intervention by closed-loop neural feedback, and be applicable to driving, operation monitoring and other high safety requirement scene.

Provided herein are methods of neurofeedback. In some aspects, the methods of neurofeedback are individualized for a subject based on the neural activity of the subject. In some aspects, the individualized neurofeedback methods involve providing feedback to the subject regarding the subject's own neural activity in order to facilitate the subject volitionally controlling their own neural activity, such as to promote or inhibit a cognitive state.

The invention relates to a multi-modal neural feedback training method for improving attention. According to the technical scheme, the multi-modal neural feedback training method is characterized by comprising the steps that S1, original electroencephalogram signals and original electrocardiosignals of a subject when the subject plays a severe game are obtained; s2, the original electroencephalogram signals and the original electrocardiosignals are preprocessed, and corresponding electroencephalogram signals and electrocardiosignals with baseline drift removed are obtained; s3, classifying the brain wave signals and the baseline drift removed electrocardiosignals through a preset convolutional neural network model to obtain the attention state of the subject; s4, adjusting the scene of the severe game according to the attention state, and returning to the step S1; by means of the closed-loop multi-mode neural feedback training system, the subject can conveniently and actively adjust and control the attention state of the subject, and therefore the purpose of improving attention is achieved.