530 results about "Brain–computer interface" patented technology

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.

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 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.

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.

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 user's retina based on pupil position and size. The glasses comprise frame, lenses with directed pixel layers, sensors for tracking pupil movement. Each directed pixel generates multiple virtual pixels by rapidly changing its emission angle. Each directed pixel with pupil's real-time tracking, provides automatic adjustments to virtual contents, ensuring accurate alignment of images with field of view of users while identifying vergence-accommodation conflict. The glasses function as prescription lenses, virtual reality displays, augmented reality devices without traditional optical systems. Additional features include depth sensors, cameras, connectivity to peripheral devices. The glasses enable seamless blend of virtual and real-world experiences, creating immersive “Mixverse” environment. Various input methods, including gesture recognition and brain-computer interfaces, allow for intuitive control and interaction.

The invention is applicable to the technical field of medical instruments, and provides a subskull epidural micro-invasive flexible brain-computer interface electrode, which comprises a plurality of electrode branches, each electrode branch comprises a linear flexible main body, and the flexible main body comprises a micro power supply circuit and a signal circuit which are solidified. A plurality of electrode units are arranged on the micro power supply circuit and the signal circuit at intervals, and the electrode units are electrically connected with the micro power supply circuit and the signal circuit. The brain-computer interface electrode is in a flexible line type, can be implanted into the middle artery of the meninx, does not need craniotomy, is a micro-invasive brain-computer interface, has data monitoring accuracy close to that of an invasive brain-computer interface, does not need to worry about inflammatory response, immunostimulation, biocompatibility, brain tissue damage and other adverse factors after long-term implantation, and is suitable for popularization and application. And the thrombogenic risk of a vein stent brain-computer interface does not exist.

The invention discloses a brain heuristic multi-expert multi-mode emotion recognition method and system, equipment and a medium, and belongs to the technical field of artificial intelligence and biomedical signal processing. The method comprises the following steps: by simulating a brain function partitioning mechanism, dividing an electroencephalogram signal into a plurality of brain regions according to neuroanatomy prior, and designing a special expert network for each region; a global-local double-current encoder is adopted to cooperatively extract spatial-temporal characteristics of each brain region signal, and meanwhile, a multi-scale large-kernel convolution module is utilized to extract peripheral physiological signal characteristics; and finally, dynamically fusing multi-expert features through an adaptive routing network to realize sentiment classification. Expert load balancing and bifurcation regularization joint loss are introduced into the model in training, and effective cooperation and feature diversity of experts are ensured. According to the method, excellent recognition precision is obtained in practice, it is verified through interpretability analysis that the decision-making process conforms to neuroscience cognition, and a high-precision and high-reliability solution is provided for application of brain-computer interfaces, mental health monitoring and the like.

The invention discloses a brain-computer interface signal enhancement and evaluation method fusing physical information. The method comprises the following steps: firstly, based on an individual structure magnetic resonance image, constructing a personalized six-layer brain tissue anatomical model comprising a cortex, a white matter, cerebrospinal fluid, a dura mater, a skull and a scalp, and endowing differentiated conductivity parameters; secondly, neuroelectrophysiology priori knowledge such as neurodynamics and white matter anisotropic conduction is converted into a representation rule which can be learned by a neural network; then, the rules are systematically embedded into a physical information neural network in a differentiable physical constraint form, including a cortical neurodynamic equation residual error, a volume conduction equation residual error of each layer and an interlayer interface continuity constraint; and finally, training a network by using the scalp electroencephalogram signals, and jointly outputting high-fidelity intracranial electroencephalogram signals and multi-modal neurophysiological information by minimizing a loss function containing reconstruction and physical constraints. According to the method, the fidelity, the physical rationality and the clinical interpretability of signal enhancement are effectively improved.

The invention belongs to the technical field of brain-computer interfaces, and particularly relates to a post-stroke dysphagia rehabilitation recognition system, feedback stimulation equipment and a storage medium. The system comprises a signal acquisition module, an electroencephalogram signal preprocessing module, a neural network feature extraction module, a statistical feature extraction module and a swallowing intention recognition module, and can recognize whether a patient has a swallowing intention or not through a Mama deep learning model based on space-time statistical features. The invention also constructs brain-computer interface equipment for rehabilitation of dysphagia after stroke in combination with electroencephalogram signal acquisition equipment and electrical stimulation equipment. The technical scheme of the invention has the advantages of being accurate in recognition, good in rehabilitation treatment effect and capable of forming center-periphery closed loop feedback, and has a very good application prospect.

The invention discloses a scoliosis neuromuscular rehabilitation system based on a brain-computer interface and digital twinning, and the system comprises a multi-mode sensing module which is used for synchronously collecting a neurophysiological signal, a muscle electrical activity signal and a motion posture signal of a user; the intelligent processing and simulation module is used for carrying out fusion processing on the multi-modal signals, decoding the motion intention of the user and carrying out real-time simulation prediction on a biomechanical effect generated by the motion intention based on a personalized digital twinborn model of the user; and the decision and feedback module is used for generating and executing a personalized feedback instruction according to the decoding result and the simulation prediction result of the motion intention. The invention belongs to the technical field of medical rehabilitation engineering and biomedical engineering, and particularly provides a neuromuscular function remodeling method which is capable of directly providing a scoliosis patient with biomechanical accuracy, real-time interactivity and individual adaptability from a neural control source by constructing an integrated rehabilitation system.

The invention belongs to the technical field of artificial intelligence and brain-computer interfaces, and discloses a multi-task self-supervision graph neural network emotion recognition method and system based on time-space-frequency fusion, and the method comprises the steps: firstly carrying out the graph modeling of an EEG signal from a time domain, a space domain and a frequency domain, then designing three types of self-supervision tasks, namely, a time sequence puzzle, a space puzzle and a frequency puzzle, the EEG signals are partitioned and rearranged according to time, space and frequency, and the model is guided to autonomously learn key structural features and potential modes in the EEG signals by predicting the original sequence. And meanwhile, a comparative learning task is introduced to realize semantic consistency constraint under different feature perspectives, so that the discrimination and robustness of feature expression are further improved. And the network adopts a dynamic weight distribution mechanism to carry out joint optimization on multi-task loss. The method can effectively improve the capturing capability of the spatial-temporal dynamic characteristics of the electroencephalogram signals, enhances the generalization of the model and the robustness of noise labels, and shows high accuracy in emotion recognition.

The invention provides an electroencephalogram signal processing method and device based on a non-invasive brain-computer interface, and belongs to the technical field of medical equipment. According to the change risk of the amplitude data of the electrode of the analysis processing target, the similarity degree of other analysis processing targets and the signal stabilization electrode, the identification processing scheme of the electroencephalogram staging model of the analysis processing target is determined, and based on the deviation data of the electroencephalogram staging result between different identification processing schemes, the electroencephalogram staging model of the analysis processing target is obtained. Determining the interference risk type of the analysis processing target and the position of the interference electrode, and determining the electroencephalogram signal processing method of the position of the interference electrode according to the similarity degree of the identification processing scheme with the identification deviation of the position of the interference electrode in each analysis processing target and the interference risk type of the analysis processing target. And the accuracy of the sleep staging result is improved.

The invention discloses a brain-computer interface interaction control device and method based on multi-mode brain signal fusion. The device comprises a perception acquisition layer, an edge processing layer, a fusion decoding layer, a control application layer and a closed-loop optimization cloud platform. The sensing acquisition layer synchronously acquires multi-modal brain signals; the edge processing layer adopts a condition alignment time sequence diffusion model to carry out signal enhancement and completion; the fusion decoding layer realizes multi-modal feature fusion and intention decoding through a lightweight graph neural network; the control application layer provides adaptive control mapping and multi-mode feedback; and the closed-loop optimization cloud platform realizes continuous performance optimization through federated learning and incremental learning. According to the method, the problems of single signal, insufficient precision and poor practicability of a traditional brain-computer interface are effectively solved, the decoding precision, the real-time performance and the individuation degree of the system are remarkably improved, and the method can be widely applied to the fields of intelligent home control, medical rehabilitation training, industrial control and the like.

The invention discloses a combined preprocessing and fusion decoding method based on electroencephalogram signals and functional near-infrared signals, which comprises the following steps of: synchronously acquiring the electroencephalogram signals and the functional near-infrared signals, taking a task trigger event as a unified time reference, and carrying out basic preprocessing of time alignment and modal self-adaption on two modal signals to obtain a functional near-infrared signal; a pre-trained cross-modal noise joint modeling module is utilized to perform joint modeling on cross-modal joint noise features caused by a common noise source, collaborative denoising processing is performed on multi-modal signals based on the cross-modal joint noise features, multi-modal denoising representation is obtained, a cross-modal feature interactive modeling mode based on an attention mechanism is obtained, and the multi-modal noise is obtained. And dynamically modeling the correlation between different modal features, realizing deep fusion of multi-modal complementary information, carrying out decoding processing based on the fused features, and outputting a corresponding brain-computer interface control instruction or task identification result. According to the method, the decoding accuracy and robustness of the brain-computer interface system in a complex task scene are enhanced.

The invention discloses a flexible hydrogel neural electrode and a preparation method and application thereof. The flexible hydrogel neural electrode comprises a flexible substrate layer, an electrode layer and a packaging layer which are sequentially arranged in a stacked mode, the flexible substrate layer and the packaging layer are each of a hydrogel fiber composite film structure formed by compounding a fiber film and hydrogel, and the fiber film is embedded in the hydrogel; the fiber film is a polycaprolactam fiber film, and the hydrogel is polyvinyl alcohol hydrogel; the electrode layer comprises a plurality of metal electrodes, and each metal electrode is provided with an electrode point and a bonding pad; the packaging layer is provided with a plurality of hole structures, and the holes are arranged corresponding to the electrode points and the bonding pads and enable the electrode points and the bonding pads to be exposed. The flexible hydrogel neural electrode provided by the invention has good biocompatibility, swelling resistance and process compatibility; meanwhile, the flexible hydrogel neural electrode can be stably applied to an implantable brain-computer interface and is used for realizing long-term stable acquisition and transmission of neural signals.

A device and system for real-time visualization of the electrophysiologic activity of a brain, particularly at the cortical surface. The neural device can acquire, process, and display high-spatiotemporal-resolution electrophysiologic data in real-time across entire electrode arrays spanning many thousands of electrodes over identified anatomic regions. The system is compatible with thin-film cortical surface electrodes that record from neural tissues without damaging those tissues. The system can be used to guide diagnostic and therapeutic actions with high precision, and also provides the basis for a brain-computer interface.

A brain control interface system is disclosed. The brain control interface comprises: a brain control interface configured to detect brain signals indicative of brain activity of a user in an environment, an input configured to obtain data indicative of a current light scene of one or more lighting devices in the environment, a lighting controller configured to control the one or more lighting devices, and one or more processors configured to analyze the brain signals to identify a level of noise in the brain signals when the current light scene is active, and, if the level of noise exceeds a threshold, adjust the light scene while monitoring the level of noise until a target level of noise in the brain signals has been established.

The invention provides a flexible electrode implantation system and method based on spiral winding, and relates to the technical field of brain-computer interfaces, the system comprises an implantation tool (1), the implantation tool (1) comprises a sleeve (12) and a sliding rod (11) slidably arranged in the sleeve (12) in a penetrating mode, and the sliding rod (11) is provided with an extension part extending out of the first end of the sleeve (12); the baffle structure is arranged at the first end of the sleeve (12), and a clamping groove is formed between the baffle structure and the extending part of the sliding rod (11); the near end of the flexible electrode (2) is provided with a fixing part, and the fixing part is matched with the extending part of the sliding rod (11) and the clamping groove so that the near end of the flexible electrode (2) can be locked to the implantation tool (1) before implantation; the sliding rod (11) is configured to slide towards the second end of the sleeve (12) relative to the sleeve (12) and retreat from the clamping groove so as to unlock the near end of the flexible electrode (2).

The invention relates to the technical field of brain-computer interfaces and emotion calculation, in particular to an electroencephalogram emotion recognition method based on adaptive multi-stream graph fusion and gated space-time Transform, which comprises the following steps: constructing and training an electroencephalogram emotion recognition model, and inputting to-be-detected signal data into the trained electroencephalogram emotion recognition model to obtain a detection result; the electroencephalogram emotion recognition model comprises a self-adaptive multi-stream graph fusion network, a gating enhanced time sequence block and an emotion classification head; the average accuracy and the F1 score of the method are superior to those of the existing mainstream model, and the superiority of the method in the aspect of improving the personalized emotion recognition performance is proved.

The application discloses a non-invasive brain-computer interface rehabilitation robot and relates to the technical field of intelligent rehabilitation equipment, and solves the technical problem that the existing rehabilitation robot mainly adopts a set program to perform repetitive rehabilitation training, and patients are not actively involved, the passive training mode limits the autonomous control and participation of the patients, and the rehabilitation effect is limited, especially in the aspect of neural injury repair, and the effect is poor; the application obtains motion data of the patient; an artificial intelligence model is trained based on historical motion data to obtain a motion recognition model; the motion data is recognized based on the motion recognition model to obtain a recognition result; the patient is subjected to rehabilitation training through the rehabilitation robot based on the recognition result; a rehabilitation effect evaluation coefficient of the patient is calculated based on the electromyographic characteristic data; and the recovery progress of the patient is judged based on the rehabilitation effect evaluation coefficient and a preset rehabilitation effect evaluation threshold value, so that the above technical problem is solved.

The invention discloses a hand motion FNIRS signal classification method based on t distribution optimization feature extraction, and the method specifically comprises the steps: employing a portable functional near-infrared collection device to collect an optical density signal of a forehead cortex brain region during the motion execution period during the operation of the device; after pretreatment, the concentration of oxyhemoglobin HBO and the concentration of deoxidized hemoglobin HBR are calculated according to the corrected Beer-Lambert law; according to motion execution and resting state segmentation data, time domain features and time-frequency domain features of oxyhemoglobin concentration data signals of each channel of each experiment test are extracted, and a first feature matrix is formed; performing kernel density estimation and kurtosis and skewness test on the first feature matrix to obtain a second feature matrix, performing t distribution on each corresponding feature satisfying normal distribution in the second feature matrix of the motion execution and resting states, and optimizing feature selection on the basis to obtain an optimized third feature matrix; the optimized third feature matrix is used as classifier input, a brain-computer interface system model training result is obtained, and effective feature selection and extraction are achieved; according to the method, the brain-computer interface system feature extraction of the hand motion FNIRS signals can be optimized, so that the classification performance is improved.

The invention discloses a task state brain-computer interface training system for closed-loop transcranial focused ultrasound, and relates to the technical field of medical simulation training. According to the task state brain-computer interface training system for closed-loop transcranial focused ultrasound, the states of software, hardware and participants are verified and protected in sequence through a safety and equipment initialization module; the baseline acquisition and feature calibration module extracts individualized EEG time-frequency features with high signal-to-noise ratio in resting and task states and screens key channels, the offline decoding training module aligns feature mapping and task labels and then generates a high-precision discriminator through cross validation optimization, and the online closed-loop modulation module embeds the discriminator into a real-time EEG assembly line. The self-adaptive optimization and advanced task module realizes'reading-judging-writing-evaluating 'closed-loop intervention, guarantees safety through physiological monitoring, and finally constructs a safe, accurate and sustainable self-perfect closed-loop transcranial focused ultrasound brain-computer interface training platform by continuously iterating a model and stimulation parameters based on real-time feedback and historical data through the self-adaptive optimization and advanced task module.

The invention provides an electroencephalogram image reconstruction method based on frequency steering and bidirectional diffusion, and belongs to the technical field of brain-computer interfaces and computer vision. The method comprises the steps that EEG data and corresponding image data are acquired and preprocessed; constructing a reconstruction model comprising a frequency domain-space-time dynamic encoder and a bidirectional submerged space diffusion generator; wherein the frequency domain-space-time dynamics encoder adopts a frequency-oriented Mama architecture, explicitly models neural oscillation dynamics by constructing a block diagonal state matrix, and extracts features in combination with graph convolution and space-time convolution; the bidirectional submerged space diffusion generator comprises a symmetric EEG-to-image submerged space diffusion model and an image-to-EEG submerged space diffusion model, and training is carried out through generative cyclic consistency constraint; and finally, mapping the collected EEG data into image semantic features by using the trained model, and driving a pre-training generation model to reconstruct an image. According to the method, the problems that in the prior art, the electroencephalogram frequency domain specificity dynamic state is ignored, and cross-modal semantic alignment is weak are solved, and the semantic consistency of electroencephalogram decoding and the fidelity of image reconstruction are remarkably improved.

The invention provides a sleep regulation and control method and device based on a non-invasive brain-computer interface, and belongs to the technical field of sleep regulation and control. The method specifically comprises the steps that the coincidence condition of a reliable user and a user with the environment temperature changing is determined and recognized, and verification data of a sleep stage recognition model for recognizing the reliable user are combined; determining an update processing strategy of the sleep staging identification model, performing update processing on the sleep staging identification model according to the update processing strategy of the sleep staging identification model, and determining the sleep staging identification model according to an update processing result of the sleep staging identification model and historical regulation and control data of the sleep environment temperature in the user with the environment temperature changing. According to the regulation and control method for determining the sleep environment temperature of the user with the environment temperature changing, determination of differentiated temperature regulation strategies of the sleep environment temperature under different staging results is achieved, and then the user experience is improved.