794 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 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 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 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 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.

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 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.

The invention discloses a patient improvement effect analysis method for controlling spinal cord electrical stimulation through an implantable brain-computer interface, and relates to the technical field of medical rehabilitation, and the method comprises the steps: multi-dimensional collaborative data collection: implanting electrodes in a target brain region and below a spinal cord injury segment, installing a detection element at an exoskeleton key part, and carrying out multi-dimensional collaborative data collection; a sensor is attached to a lower limb preset muscle group, electroencephalogram signals, SCS stimulation parameters, EXS motion data and neuromuscular response data are synchronously collected, and time correlation marks are embedded; according to the method, the reliability of motion intention decoding is remarkably improved by adopting a mode of combining multi-source signal preprocessing and a multi-mode intention recognition model, and in the signal preprocessing stage, the self-adaptive filtering algorithm combining Kalman filtering and wavelet threshold denoising is applied, so that the motion intention decoding efficiency is improved. SCS electrical stimulation interference, EXS motor noise and physiological noise in the electroencephalogram signals are effectively removed.

The invention relates to a three-dimensional spiral high-density neural electrode and a preparation method and application thereof. The three-dimensional spiral high-density neural electrode comprises a probe structure and a plurality of electrode sites. The probe structure is formed by curling a planar flexible electrode precursor, and at least one end of the probe structure is provided with a spiral outer surface of a three-dimensional spiral line structure. Electrode sites are distributed along a spiral path, have a size of 5-1000 [mu] m, and are used for contacting biological tissues. On the planar flexible electrode precursor, electrode sites are arranged on one or more straight lines forming an inclined angle alpha with the axial direction of the probe, and the spatial distribution is matched with the edge. Compared with the prior art, the method has the advantages that the constraint of a traditional wiring mode is broken through, the integration of high-density three-dimensional channels is realized under a micro size, and a new generation of solution is provided for a high-precision brain-computer interface, deep brain stimulation and a three-dimensional electroencephalogram.

The invention relates to the technical field of medical data processing and human-computer interaction, and particularly discloses a rehabilitation training feedback adjusting system based on a brain-computer interface and myoelectricity sensing. The system comprises a multi-modal physiological signal acquisition module, a signal fusion and intention decoding module, a rehabilitation state dynamic evaluation module, a self-adaptive feedback strategy generation module and a multi-channel feedback execution module. By synchronously collecting and fusing the electroencephalogram signals and the electromyographic signals to decode the motion intention, dynamically evaluate the rehabilitation state and predict the trend, a multi-sensory collaborative feedback strategy is adaptively generated, personalized and dynamic precise adjustment of rehabilitation training is achieved, and the training effect and the patient participation degree are improved. The system can autonomously switch between a reinforcement learning strategy and an auxiliary guiding strategy according to the real-time performance and the long-term trend of a patient, and dynamically optimize various feedback parameters.

The invention provides a brain organism closed-loop rehabilitation system and a control method thereof, belongs to the field of brain-computer interfaces and rehabilitation medical treatment, and is used for solving the problems of insufficient brain state perception, poor adaptability and low robustness of a brain organism rehabilitation system in related technologies. The method comprises the following steps: synchronously acquiring multi-modal physiological signals by cooperating with physiological signal monitoring, task induction and regulation and control equipment modules, removing artifacts through preprocessing and redundancy check, extracting neuroplasticity characteristics, dynamically adjusting weight decoding brain activity intentions, regulating and controlling stimulation parameters in a closed loop, and training an optimization model in combination with a multi-center federation; accurate and highly-adaptive rehabilitation regulation and control are realized, and the rehabilitation effect and the model generalization are improved.

The invention belongs to the technical field of brain-computer interfaces, multi-modal feature alignment and information retrieval, and discloses a visual content retrieval method based on electroencephalogram signals. Cross-subject standardized electroencephalogram samples are obtained and input into an electroencephalogram encoder to extract low-dimensional electroencephalogram signal feature vectors; an image encoder is adopted to process the corresponding retrieval images to extract visual feature vectors; the low-dimensional electroencephalogram signal feature vector and the visual feature vector are jointly input into a prototype attention enhancement module to form a dynamic prototype pool, and prototype enhanced electroencephalogram signal representation is obtained through processing; the method comprises the following steps: performing classification training according to existing prototype enhanced electroencephalogram signal representation and image data pairs; for a plurality of time slices of the to-be-queried electroencephalogram signal sample, calculating a reconstruction error or a signal-to-noise ratio of each time slice to obtain a confidence coefficient; and adopting a weighted average or voting mechanism to fuse a plurality of time slice results, and outputting a stable and robust final visual retrieval classification result.

An implantable medical device to restore brain-controlled movement and sensation after neural injury. The device comprises a brain-computer interface capable of acquiring electrocorticogram (ECoG) signals recorded directly from the surface of the brain and uses the signals to enable direct control of paralyzed muscles, limbs or extremities while simultaneously receiving signals from external sensors and converting them into electrical stimulation patterns for the brain's sensory areas.

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.

The invention relates to the technical field of brain-computer interface and man-machine interaction safety, in particular to a portable multi-modal brain-computer interface intelligent recognition system, which comprises a multi-modal data acquisition and preprocessing unit, a multi-modal data processing unit and a multi-modal data processing unit, the dynamic cognitive state feature extraction unit is used for generating a dimensionless dynamic cognitive state vector; the cognition-situation fusion and risk modeling unit is used for calculating a continuous and quantitative cognition safety risk index; the risk index generation and decision suggestion unit is used for generating hierarchical and adaptive intervention or auxiliary instructions according to the cognitive security risk index and the standardized situation data stream; according to the method, the problem of frequent occurrence of invalid alarms caused by excessive sensitivity of a traditional method is solved, and the accuracy of risk assessment is remarkably improved.

The invention relates to an intelligent hypnosis method and system based on a brain-computer interface and a storage medium, and belongs to the technical field of human-computer interaction and artificial intelligence. The method comprises the following steps: acquiring an EEG signal of a user in real time through EEG acquisition equipment, and extracting a brain wave segment power value as state input through preprocessing; a deep reinforcement learning model (such as DRQN) selects music type actions (such as classical music and white noise) based on an epsilon-greedy strategy; calculating a reward value (maximizing delta wave increment and inhibiting beta wave) according to the electroencephalogram state change after playing, and optimizing model parameters by adopting Q-Learning; and dynamically adjusting the strategy through iterative interaction until the user reaches a preset sleep target. The system comprises an electroencephalogram acquisition module, a preprocessing module, a reinforcement learning module and a music control module, and realizes closed-loop regulation and control. The method has the advantages of high personalization, high hypnosis efficiency (induction to sleep in 1-7 minutes), flexible adaptation to different users and self-evolution optimization capability, and effectively improves the sleep induction effect.

The invention relates to the technical field of rehabilitation medical treatment and neural engineering, and particularly discloses an epidural electrical stimulation lower limb rehabilitation system and method based on a brain-computer interface, the epidural electrical stimulation lower limb rehabilitation system comprises a weight reduction suspension device, an epidural electrical stimulation module and a brain-computer interface module, the epidural electrical stimulation module controls left and right electrical stimulation through a program controller, and the brain-computer interface module comprises a wireless electroencephalogram acquisition device and an interaction terminal and is used for acquiring electroencephalogram signals of a movement area of a patient and presenting a virtual gait scene and voice prompt at a computer end. The method comprises the steps of electroencephalogram signal collection and feature extraction, motion intention judgment, stimulation control and feedback backtracking, and by combining a motion intention decoding result with electrical stimulation output, activation of a lower limb target muscle group is achieved, a training closed loop is constructed, and the method is used for postoperative rehabilitation of a spinal cord injury patient.

The invention discloses a DIKWP-driven individualized brain-map interactive feedback system, which is used for neural rehabilitation and brain-computer interface training. The system obtains brain activity data of a patient through electroencephalogram acquisition equipment, constructs a personal brain-semantic map in combination with cognitive evaluation, and establishes a mapping relation between semantic units and brain region responses. In the training process, the DIKWP semantic analysis module performs multi-layer analysis on indexes such as reaction time, accuracy and intention achievement, generates multi-mode instant feedback such as visual sense, auditory sense or tactile sense, and performs directional reinforcement on a weak semantic domain. The system has a dynamic target optimization capability, and the difficulty can be automatically adjusted according to training performance; when attention distraction, emotion abnormity or semantic deviation is detected, a safety intervention mechanism is automatically triggered, and training effectiveness and safety are guaranteed. According to the method, closed-loop individualized rehabilitation interaction is realized, the adaptation degree and efficiency of brain-computer interface training are remarkably improved, and the method is suitable for various rehabilitation scenes such as languages, movement and cognition and has a good industrial application prospect.

The invention discloses an upper limb rehabilitation control method and system based on a multimode coordination adaptive brain-computer interface, and belongs to the technical field of neural rehabilitation engineering and brain-computer interfaces. The system comprises a brain signal acquisition module, a self-adaptive residual filtering module, a feature extraction module, a predictive control module, an upper limb rehabilitation actuator and a self-adaptive updating module. The core is that a self-adaptive updating module constructs a composite error cost function by fusing kinematics errors of a physical layer and error correlation potentials of a neurocognitive layer, and on the basis of the composite error cost function, a front-end filtering weight and a network weight of a rear-end control model are synchronously adjusted, so that full-link parameter collaborative optimization from signal preprocessing to motion control is realized. According to the method, the framework limitation of single error feedback and independent parameter optimization of a traditional system is broken through, a closed-loop brain-computer interface system which can sense internal and external states and has system-level self-optimization capability is constructed, and the decoding robustness, the control precision and the practicability under long-term use are effectively improved.

The invention provides an intrusive electroencephalogram signal classification system based on SHAP interpretable feature selection and Transform, and relates to the technical field of brain-computer interfaces and intelligent neural information processing. The system comprises a candidate feature extraction module, an SHAP feature optimization module, a space-time Transform classification model and an interpretability and visualization module. The candidate feature extraction module is used for extracting time domain, frequency domain and nonlinear candidate features from the preprocessed interventional electroencephalogram signals; the SHAP feature optimization module performs importance scoring and recursive screening on the candidate features based on a random forest and a Shapley value method to generate an optimal feature subset; according to the space-time Transform classification model, a space-time feature matrix is constructed through feature embedding and a time position coding mechanism, and high-precision classification is achieved through a multi-head self-attention structure; and the interpretability and visualization module is combined with the SHAP heat map and the attention weight map to provide physiological interpretation of a model discrimination basis. The system is suitable for various interventional brain-computer interface scenes such as neural rehabilitation and motion intention recognition.

The invention discloses a method for detecting sleep apnea and related events based on a brain-computer interface. The method comprises the following steps: collecting physiological signals during sleep; performing signal preprocessing operation on the physiological signal to obtain a preprocessed physiological signal; performing automatic sleep staging on electroencephalogram signals in the preprocessed physiological signals by adopting a deep learning model to obtain a sleep staging result; extracting features corresponding to the sleep staging results to obtain sleep feature data; positioning an apnea related event through the sleep feature data to obtain an event positioning result; calculating the event positioning result and the preprocessed physiological signal by adopting a statistical method to obtain event statistical data; calculating an apnea hypopnea index according to the sleep staging result, the event statistical data and the total sleep duration; and generating a sleep monitoring report according to the sleep staging result, the event statistical data and the apnea hypopnea index.