1176 results about "Brain–computer interface" patented technology

The method can include: recording a neural state, determining a latent array, and determining an output based on the latent array. In variants, the method can function to enable a subject to use neural signals to navigate within a high-dimensional concept space to control an output (e.g., image generation, motor control, communication, etc.).

The invention discloses a brain-computer hybrid intelligent closed-loop swallowing rehabilitation system for treating dysphagia after stroke, and relates to the field of artificial intelligence medical instruments. The system is composed of a plurality of key modules: an intelligent swallowing brain-computer interface module for accurately identifying a swallowing intention through multi-modal physiological signal acquisition and processing; the intelligent swallowing functional electrical stimulation module is used for finely regulating and controlling stimulation parameters according to the condition of the patient; the AI auxiliary online integrated module is used for realizing non-invasive brain-controlled swallowing and high-precision AI regulation and control; the closed-loop intelligent feedback regulation and control module is used for real-time monitoring, early warning and self-adaptive optimization of electrical stimulation; the cloud platform remote monitoring and data storage module supports remote diagnosis and treatment of doctors; and the man-machine interaction interface display and printing module optimizes doctor-patient interaction. According to the system, a brain-computer interface and FES are combined, a personalized, safe and efficient rehabilitation scheme is provided for a patient based on data driving and an artificial intelligence algorithm, swallowing function recovery can be accelerated, complications are reduced, and the system has great clinical and social values.

A computer-implemented system and method for pupil-directed retinal projection in near-eye displays are disclosed. The computer-implemented system provides smart glasses with directed physical pixels that project light beams / signals directly onto a user's retina based on pupil position and size. The glasses comprise a frame, lenses with directed pixel layers, and sensors for tracking pupil movement. Each directed pixel may generate multiple virtual pixels by rapidly changing its emission angle. The glasses function as prescription lenses, virtual reality displays, and augmented reality devices without traditional optical systems. Additional features include depth sensors, cameras, and connectivity to peripheral devices. The glasses enable a seamless blend of virtual and real-world experiences, creating an immersive “Mixverse” environment. Various input methods, including gesture recognition and brain-computer interfaces, allow for intuitive control and interaction.

The invention discloses an immersive space virtual-real interaction method and system based on element universe, and relates to the technical field of brain-computer interfaces, and the method comprises the steps: capturing an electroencephalogram signal of a user, and obtaining brain wave data of the user in real time; transmitting the brain wave data to a neural feedback adaptive network for analysis, and generating a corresponding interaction intention; virtual environment elements in the meta universe are adjusted in real time according to the interaction intention; the physical environment of the user is monitored in real time, physical environment parameters are obtained, the physical environment parameters are compared with the virtual environment elements, and bidirectional synchronous adjustment is carried out; dynamically adjusting sensory elements in the virtual environment elements by utilizing electroencephalogram resonance based on the electroencephalogram signal of the user and the synchronized physical environment parameters; through bidirectional synchronous adjustment of the physical environment parameters and the virtual environment elements, high consistency between the virtual environment and the physical environment of the user is ensured, and the immersion and authenticity experience of the user are greatly improved.

The invention discloses a multi-mode electroencephalogram analysis model construction method and an online processing method and system. According to the method, multi-modal physiological signals such as electroencephalogram, electrocardio, skin electrical activity and eye movement are collected, and an integrated multi-modal signal collection device is used for preprocessing and feature extraction. A modal expert system based on an adaptive Transform architecture is adopted to replace a standard feed-forward network so as to enhance the feature processing capability. By means of the method, the features can be complemented under the condition of mode or data missing, the robustness, real-time performance and accuracy of the brain-computer interface technology in multi-mode data processing are improved, and the method is particularly suitable for the fields of emotion recognition, cognitive load monitoring, neural rehabilitation and the like.

The invention belongs to the technical field of brain-computer interfaces, and particularly relates to a brain-computer interface driven emotion recognition and self-adaptive regulation and control system which comprises a multichannel nerve-peripheral coupling module, an emotion intensity probability mapping module and a closed-loop nerve regulation and control current module. The multi-channel nerve-peripheral coupling module is used for realizing overall quantification of central and peripheral emotional physiology; the emotion intensity probability mapping module is used for generating continuous emotion probabilities ranging from 0 to 1 through normalization and nonlinear mapping by utilizing emotion energy and combining eye movement fatigue and electroencephalogram entropy; and the closed-loop nerve regulation and control current module is used for dynamically adjusting the transcranial electrical stimulation intensity within the safety current upper limit according to the difference value between the emotion probability and the expected target. According to the invention, the recognition precision, the response speed and the use comfort are obviously improved.

The invention discloses a wireless high-density electroencephalogram and myoelectricity hybrid synchronous acquisition and monitoring system, and belongs to the field of human-computer interaction. The wireless high-density electroencephalogram and myoelectricity mixed synchronous collecting and monitoring system is composed of three parts, namely a high-density electroencephalogram and surface myoelectricity signal collecting system, a base station system and an upper computer system. The high-density electroencephalogram and surface electromyogram signal acquisition system is responsible for acquisition and primary processing of signals, and the base station system performs data interaction with the high-density electroencephalogram and surface electromyogram signal acquisition system in a wireless transmission mode and transmits the processed signals to the upper computer system for further analysis and display. According to the invention, a wireless and distributed architecture and a high-channel-number data acquisition scheme are integrated, the problems of efficient acquisition and synchronous processing of electroencephalogram and myoelectricity mixed signals are emphatically solved, and the application range in the fields of brain-computer interfaces, virtual reality, neural rehabilitation, cognitive training and the like is widened.

The invention discloses an acupuncture treatment effect real-time evaluation system based on a brain-computer interface technology, and belongs to the technical field of acupuncture treatment effect evaluation, and the acupuncture treatment effect real-time evaluation system comprises a brain-computer data collection module, an acupuncture data acquisition module, a treatment effect real-time evaluation module, a patient portrait generation module and a long-time treatment effect comprehensive evaluation module, comprising an electroencephalogram sensor which collects electroencephalogram signals of a patient before treatment and in the treatment process based on a non-invasive brain-computer interface technology and transmits the electroencephalogram signals to a follow-up module, and the acupuncture data acquisition module comprises a high-definition camera which is used for collecting actions of a doctor in the treatment process and judging acupuncture points and acupuncture depth of the current patient in the treatment process. According to the acupuncture and moxibustion treatment effect real-time evaluation system based on the brain-computer interface technology, by combining the brain-computer interface technology and acupuncture and moxibustion treatment characteristics, an acupuncture and moxibustion effect real-time evaluation scheme is established based on individual differences of patients, so that the accuracy and effectiveness of acupuncture and moxibustion treatment are improved.

The invention relates to a mechanical exoskeleton rehabilitation training system and method based on a brain-computer interface. The system comprises electroencephalogram acquisition equipment, a preprocessing unit, a feature extraction unit, a motion intention decoding unit, a mechanical exoskeleton control unit, a muscular tension state monitoring unit and a self-adaptive functional electrical stimulation feedback unit. The method comprises the following steps: preprocessing electroencephalogram and electromyographic signals; electroencephalogram and myoelectricity time-frequency features are obtained through feature extraction; an electroencephalogram decoding model is used for decoding to obtain the movement intention of the patient, and an exoskeleton control instruction is generated to control a mechanical exoskeleton control unit to drive the limb movement of the rehabilitation patient for rehabilitation training; meanwhile, the muscular tension state of the patient is analyzed according to the time-frequency characteristics of electroencephalogram and myoelectricity, functional electrical stimulation of different intensities is applied in a self-adaptive mode, stimulation feedback is enhanced, and the muscular tension state of the patient is adjusted. According to the invention, deep fusion of brain-controlled exoskeleton training and low-frequency nerve electrical stimulation adjustment can be realized, and the rehabilitation effect of a stroke patient is improved.

The invention provides an EEG (electroencephalogram) classification method based on multi-domain feature fusion. A multi-domain feature extraction network is constructed, the multi-domain feature extraction network mainly comprises a frequency domain feature extraction module and a space-time feature extraction module which are deployed in parallel, a feature fusion module and a classifier module, multiple view features such as a time domain, a frequency domain and a space domain can be separated, and electroencephalogram signal classification is achieved. According to the method, an efficient solution is provided for solving the problem of insufficient multi-domain feature utilization of the electroencephalogram signals, the cross-scene classification precision can be remarkably improved while the model efficiency is kept, and a technical foundation is laid for personalized deployment of brain-computer interfaces.

The invention relates to the technical field of medical treatment, and particularly discloses a minimally invasive implantable brain-computer interface intracranial multi-mode signal monitoring system, which comprises a flexible probe, a data acquisition module, a data return module and a data analysis module, the flexible probe is embedded into the high-precision microarray electrode and the sensor to collect neural signals in real time; the data acquisition module is used for synchronously acquiring electroencephalogram signals and intracranial pressure of specific parts of the brain; the data return module manages the collected data; and the data analysis module analyzes the electroencephalogram signal and the intracranial pressure data. According to the minimally invasive implantable brain-computer interface intracranial multi-mode signal monitoring system provided by the invention, lumbar puncture enters a cerebrospinal fluid space and is sent to a target spot, so that the infection risk can be effectively reduced, the limitation on the action of a patient is reduced, and the system has the characteristics of real-time performance, directness and accuracy; the method has very important significance and value for evaluating brain injury and brain functional lesion of a patient, prognosis judgment and the like.

The invention discloses an industrial internet of things real-time monitoring and predictive maintenance system based on digital twinning, and relates to the technical field of industrial digital twinning operation and maintenance, the system comprises a multi-modal data acquisition module, a sensor network is deployed, and edge calculation preprocessing is carried out; the digital twinning construction module is used for constructing a high-precision model by fusing a physical law and deep learning; the real-time monitoring module is used for detecting abnormity by using a space-time diagram neural network; the predictive maintenance module is used for optimizing a maintenance strategy in combination with a probabilistic algorithm; and the man-machine interaction module supports AR / VR and brain-computer interface operation. In addition, the system integrates functions of block chain security, energy management and the like, and realizes full-life-cycle intelligent management of equipment. The operation and maintenance efficiency of the industrial equipment is greatly improved. The data acquisition precision reaches the nanoscale, and the early warning time is advanced to 72 hours; the maintenance cost is reduced, and the equipment availability is improved; the AR interaction enables the operation efficiency to be improved and the training period to be shortened. And meanwhile, energy consumption reduction is realized.

The invention discloses a smart park facility predictive maintenance system based on a digital twinborn technology, and relates to the field of smart park facility maintenance. Comprising a data acquisition module, a preprocessing module, a digital twin model construction module, a fault prediction module, a maintenance decision module, a maintenance resource management module, a user interaction module, a system management module, a spatio-temporal data analysis and prediction module and a social-technical system fusion module. The method comprises the following steps: collecting and fusing a risk-dependent frequency modulation rate of quantum sensing, improving speed and precision by means of quantum calculation, fusing a digital twin model into a meta-universe concept and an intelligent agent, predicting a fault by combining quantum machine learning and causal inference, optimizing a maintenance decision based on a game theory and reinforcement learning, and managing resources by using a block chain-Internet of Things fusion technology. The invention discloses a brain-computer interface and holographic projection interaction and quantum encryption dual-protection management system. The system is accurate in data acquisition, vivid in model construction, accurate in fault prediction, scientific in maintenance decision, efficient in resource management, immersive in interactive experience and safe and stable in system, and ensures stable operation of park facilities.

The invention discloses an AI-based enterprise benefit policy intelligent matching and declaration auxiliary system, and relates to the technical field of policy declaration auxiliary systems. The data collection module collects policy and enterprise data in a multi-modal fusion manner, and dynamically updates according to activeness; the preprocessing module performs cleaning through a high-performance cluster, analyzes a policy through sentiment analysis and performs AES encryption; the feature extraction module uses GAN enhancement and cognitive calculation mining to update a knowledge graph; the matching calculation module calculates the matching degree in combination with a complex network, and the declaration auxiliary module demonstrates the process through VR and AR and answers questions through intelligent customer service. The risk assessment module assesses the risk by using a block chain and the like; the user interaction module supports a brain-computer interface and the like and adaptively adjusts the interface; and the data updating and feedback module records feedback by means of the block chain and optimizes a prediction mechanism. The system is advantaged in that multi-channel accurate acquisition is facilitated, enterprise matching policy bonus is facilitated, a declaration link is simplified, the cost is reduced, data authenticity is guaranteed, cooperation is promoted, continuous optimization is carried out according to feedback, and enterprise benefit policy landing is promoted.

The invention provides an electroencephalogram eye tracker based on a brain-computer interface, a calibration method and device of the electroencephalogram eye tracker, computer equipment and a storage medium. According to the scheme, a stable stimulation source is provided for data acquisition by orderly displaying the calibration points, the eye movement data and the electroencephalogram signals are synchronously acquired, and the user state information is acquired from multiple dimensions. The attention index is quantified according to the electroencephalogram signal, the data quality is preliminarily controlled, the target calibration point is displayed again, the data is updated, and errors caused by inattention of the user are corrected. And finally, high-quality eye movement data is utilized to calibrate and compensate eye tracker system errors, individual differences and environmental influences, and the calibration robustness is greatly improved, so that the calibrated electroencephalogram eye tracker can more accurately detect the watching position of the user, and reliable data support is provided for subsequent research and application based on eye movement tracking.

The invention relates to the technical field of brain-computer interfaces, and provides a brain-computer interface instruction issuing method, device and equipment based on regulation enhancement simulation, and the method comprises the steps that an electroencephalogram decoding model comprises an encoder, a feature enhancer and a task classifier, the encoder encodes a real-time electroencephalogram signal to obtain compression representation before nerve regulation, and the feature enhancer is used for classifying the compression representation before nerve regulation; the feature enhancer performs feature enhancement on the compression representation to obtain enhanced representation, and the task classifier classifies the enhanced representation to obtain an electroencephalogram decoding result. According to the method, a feature enhancer is obtained by combining training of a state discriminator based on a sample electroencephalogram signal collected before nerve regulation and a real state label after nerve regulation, and the feature enhancer is driven to learn a feature migration relation between a compression feature before nerve regulation and a feature after nerve regulation; the feature characterization capability of an electroencephalogram decoding model on electroencephalogram signals is remarkably improved, so that the decoding robustness on weak stimulation signals is enhanced on the premise of not depending on high-intensity external stimulation.

The invention provides a neural rehabilitation training method and system integrating a brain-computer interface and virtual reality, and relates to the technical field of brain-computer interfaces. The method comprises the following steps: constructing an aligned multi-modal feature sequence by collecting electroencephalogram, myoelectricity, joint kinematics, eye movement and physiological load signals; generating an immersion parameter prescription in the baseline stage and setting a time delay and synchronization strategy; according to the nerve quality index, performing cooperative self-adaption of decoder parameters, prescriptions and peripheral assistance; establishing a drift model after the session to update the prior and shorten the re-calibration time; and monitoring dizziness and task load in real time and executing grading treatment. According to the invention, stable closed-loop individualized rehabilitation training is realized, the decoding performance and the rehabilitation effect are improved, and the safety and long-term convergence are ensured.

The invention relates to the technical field of neural engineering and brain-computer interfaces, in particular to a closed-loop multi-modal nerve stimulation system and method based on time interference, and the system comprises a multi-modal stimulation module which is used for integrating electrical stimulation, magnetic stimulation and optical genetic stimulation, and generating a time interference field domain; the neural state sensing module is used for collecting real-time electroencephalogram signals, blood oxygen concentration and neural metabolite level data; the neural control center is used for fusing neural state data and stimulation parameters, and dynamically adjusting time interference frequency and stimulation intensity through an adaptive algorithm; the time sequence cooperation engine predicts a neural response time phase based on a deep learning model, and optimizes a stimulation time sequence and a mode switching strategy; and the visual interaction platform is used for rendering the nerve activation thermodynamic diagram and the stimulation parameter adjustment curve in real time. Therefore, the problems of adjustment strategy solidification, low adjustment precision, insufficient energy conversion efficiency and the like in the prior art are solved.

The invention relates to the technical field of brain-computer interfaces, and discloses a multi-interface fusion dynamic brain-computer interaction method based on frequency phase information multiplexing and a terminal. Based on an offline electroencephalogram experiment of a user, the most brain electrical stimulation classification number U of the user for achieving the optimal electroencephalogram stimulation classification effect, frequency-phase information of the brain electrical stimulation of the U classification and a first corresponding relation between the brain electrical stimulation of the U classification and electroencephalogram signal characteristics are obtained, and an interactive interface after multi-interface fusion is dynamically partitioned; the method comprises the following steps: acquiring Z sub-regions and brain control instructions included in each sub-region, distributing frequency-phase information of U-class brain electrical stimulation to each brain control instruction, acquiring brain electrical stimulation classification based on a first corresponding relation on the basis of brain electrical signal characteristics induced by a flicker rule of brain electrical stimulation corresponding to a certain brain control instruction when a user watches the brain control instruction, and determining a sub-region based on the user sight drop point coordinate, and outputting a brain control instruction in combination with the brain electrical stimulation classification and the determined sub-region. According to the invention, the brain-computer interaction work efficiency can be improved.

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

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 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 a decoding method, and provides a multi-modal brain-computer interface decoding method and a related device for solving the technical problems that an existing non-intrusive brain language decoding method is insufficient in adaptability in global context and weak in generalization performance in a cross-subject scene, and multi-modal neural feature collaborative enhancement is difficult to achieve. And determining the called execution module. The execution module comprises a feature extraction module, a cross-subject standardization module, a multi-mode semantic fusion module, a language recognition module and a semantic consistency module. By obtaining the unified semantic representation and combining the beam search algorithm, the fairness and universality in different language groups are remarkably improved, multiple modes can be supported, and the brain signal decoding precision and robustness are improved. In addition, cross-subject semantic representation generalization can be realized, and individual specificity is effectively reduced.

The invention belongs to the field of intelligent rehabilitation medical instruments, and particularly relates to a brain-computer interface-based multi-modal child sensory integration training device, which comprises a brain-computer interface head ring, an intelligent touch floor and AR interactive glasses, the brain-computer interface head ring is connected with a biological signal acquisition module, the intelligent touch floor is connected with a motion trail analysis unit, the AR interactive glasses are connected with a universe scene engine, and the biological signal acquisition module, the motion trail analysis unit and the universe scene engine are connected with a digital twin generator. The digital twin generator is connected with a dynamic mode switching decision tree, and the dynamic mode switching decision tree is connected with a multi-mode feedback actuator; the brain-computer interface head ring is located on the head of the child and collects electroencephalogram, myoelectricity and electrocardiosignals through a biological signal collection module; and the biological signal acquisition module sends a signal to the digital twin generator through wireless transmission. According to the invention, the training efficiency can be improved, the evaluation dimension can be expanded, potential safety hazard early warning can be realized, and the compliance can be enhanced.

The invention discloses an intelligent cabin interaction control method and system and a vehicle, and the method comprises the steps: obtaining the physiological and behavior data of a driver, and the physiological and behavior data comprise electroencephalogram signal data, eye image data, sound data and gesture image data; preprocessing the physiological and behavior data to obtain the intention tendency of the driver; and based on the intention tendency of the driver, adopting a multi-mode fusion decision to generate a vehicle control instruction. The intelligent cabin interaction control system comprises a brain-computer interface subsystem, a multi-mode interaction subsystem, a central fusion and control unit and an equipment execution subsystem. According to the invention, a more accurate vehicle control instruction conforming to the expectation of a driver can be obtained, and the situation that the vehicle cannot be executed due to the conflict of the vehicle control instructions output by different modules is prevented.

The invention discloses a cross-subject electroencephalogram emotion recognition method and device based on domain self-adaption and adversarial fusion, and belongs to the field of electroencephalogram signal processing and emotion calculation. The invention provides a cross-subject electroencephalogram emotion recognition method based on domain self-adaption and adversarial fusion, and aims to solve the problems that in the prior art, electroencephalogram signal preprocessing is insufficient in emotional feature retention capacity and cross-subject model generalization is poor, and the method specifically comprises the steps that an electroencephalogram signal data set is acquired, and electroencephalogram signals are preprocessed; after fractional order Fourier transform is carried out on the preprocessed electroencephalogram signals, differential entropy features are extracted; constructing a pre-training model; constructing a classification model; the classification model adopts an encoder in a pre-trained model after pre-training, and then a classifier is added; and adopting the classification model to realize target domain electroencephalogram signal emotion classification. Experiments show that the average accuracy of cross-subject emotion recognition on an SEED data set reaches 88.29%, and the method is suitable for scenes such as brain-computer interfaces and mental health monitoring.

The invention discloses a VR brain-computer interface system and method for olfaction-induced electroencephalogram, and the system employs a control module to control an olfaction generation module to generate smell, and control an image and video resource library to generate images or videos; the electroencephalogram signal acquisition module is used for acquiring electroencephalogram signals and sending the electroencephalogram signals to the signal processing module; a near-infrared signal acquisition module is used for acquiring a near-infrared signal and sending the near-infrared signal to a signal processing module; processing the electroencephalogram signal and the near-infrared signal by using a signal processing module to obtain a signal processing result, and sending the signal processing result to a control module and a communication module; the control module is used for controlling the smell generation module to generate smell and controlling the image and video resource library to generate images or videos according to the signal processing result; and sending a signal processing result to the mobile terminal by using the communication module. According to the method, virtual reality and olfactory stimulation are fused, multi-sense synergy is achieved, and immersive experience is provided for the user.

The embodiment of the invention relates to the technical field of brain-computer interfaces, in particular to a brain-computer interface module, a digital skull implant and an integration method and related application of the digital skull implant. The digital skull implant comprises a skull prosthesis and a brain-computer interface module, wherein the brain-computer interface module and the skull prosthesis are integrated; the brain-computer interface module comprises an ultrasonic transducer used for sending ultrasonic signals to the brain and collecting brain echo signals. The brain-computer interface module comprises an ultrasonic transducer used for sending ultrasonic signals to the brain and collecting brain echo signals. The brain-computer interface module is configured to be integrated with a skull or a skull prosthesis. In this way, the implantation structure integrating the brain-computer interface module in the skull form can have good mechanical strength and biocompatibility, complete implantation and wireless design can be achieved, and ultrasonic waves penetrate through the skull to conduct precise stimulation on the brain area.

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.