67 results about "Neural oscillation" patented technology

Embodiments described herein relate to systems, devices, and methods for monitoring brain activity and delivering electrical brain stimulation to a patient. In some embodiments, a system can deliver responsive electrical stimulation in a closed-loop manner and can offer real-time or near real-time monitoring of induced neurophysiological effects. After detecting a targeted brain pattern (i.e., an epileptic seizure), the system may deliver high-intensity ultra-short electrical stimulation impulses non-invasively or minimal-invasively to diminish or stop the neural oscillations underlying the epileptic seizure. The stimuli are delivered in time and space, relative to the emerging seizure rhythm patterns, such that they diminish or terminate the seizure. The system may include an implantable device including one or more electrodes electrically coupled to one or more processors. The processor(s) may be operatively coupled to a memory, one or more communication modules, and may optionally be coupled to a battery and one or more additional sensor(s).

The invention discloses a system and method for classifying and recognizing epilepsy in electroencephalogram signals based on a time sequence. The system comprises a cross-domain mixed self-supervised learning module, a multi-scale electroencephalogram feature learning module, an epileptic seizure guided self-attention learning module and a classifier. The cross-domain hybrid self-supervised learning module comprises a time domain context prediction task unit, a channel information reconstruction task unit and a frequency spectrum masking identification task unit; the cross-domain hybrid self-supervised learning module reconstructs an input electroencephalogram signal according to a time domain, a space domain and a frequency domain to obtain an electroencephalogram representation model; the time domain context prediction task unit is used for capturing dynamic change characteristics before and after epileptic seizure by learning a time domain evolution rule of electroencephalogram signals; the channel information reconstruction task unit is used for reconstructing missing electroencephalogram signal channel data and enhancing spatial information representation of an electroencephalogram representation model; the frequency spectrum masking identification task unit trains the electroencephalogram characterization model to characterize epilepsy nerve oscillation through a random masking part electroencephalogram signal frequency component in a frequency domain; the multi-scale electroencephalogram feature learning module captures local and global level epilepsy features of an electroencephalogram representation model through different resolutions to obtain a first epilepsy electroencephalogram feature model; the epileptic seizure guided self-attention learning module optimizes the first epileptic electroencephalogram feature model by fusing time sequence and spatial features to obtain a second epileptic electroencephalogram feature model; and the classifier classifies and outputs the electroencephalogram features of the second epileptic electroencephalogram feature model based on the long short-term memory network to determine whether the epileptic seizure occurs. The invention provides an efficient, accurate and robust solution for automatic detection and clinical auxiliary diagnosis of the epileptic seizure.

The present invention relates to an electrical nerve stimulator configured to deliver an electrical signal to a user to entrain Mu-band neural oscillations and / or Beta-band neural oscillations. The electrical nerve stimulator may be used in the treatment of neurological conditions associated with desynchronization of neural oscillations, such as Tourette's syndrome. The electrical nerve stimulator may be wearable on a wrist of a user, and may be configured to deliver rhythmic stimulation at a frequency falling within the same frequency range of Mu-band or Beta-band neural oscillations. The invention provides a convenient means of treating conditions such as Tourette's syndrome without the need for specialist medical equipment or facilities. Also disclosed is a method of delivering an electrical signal to a user to entrain Mu-band neural oscillations and / or Beta-band neural oscillations.

The invention belongs to the field of electroencephalogram signal processing, and provides a time-frequency space electroencephalogram emotion recognition method based on three-dimensional space position embedding, which comprises the following steps of: firstly, constructing a three-dimensional electrode space position matrix based on an international 10-20 system standard, determining a space adjacency relation between electrodes, and calculating a phase locking value to obtain a functional connection matrix; then, deep feature fusion of an electrode spatial position matrix and a functional connection matrix is realized by adopting a hierarchical cross Transform architecture, the spatial position matrix represents spatial distribution features of a cerebral cortex region, and the functional connection matrix quantifies phase synchronization features of cross-brain region neural oscillation and simulates a brain spatial topological structure; and finally, extracting time, frequency and spatial features of the electroencephalogram signals through combination of a graph attention network and bidirectional long-short-term memory with an attention mechanism for emotion recognition. The method can effectively extract space structure information highly related to the emotional state, and significantly improves the accuracy of emotion recognition.

The invention discloses a multi-target transcranial electrical stimulation device based on cross-regional phase-locked stimulation, and the device comprises a light electroencephalogram cap which is integrated with at least one pair of electroencephalogram signal collection electrodes located in a first brain region and used for monitoring specific neural oscillation, and is provided with electrode holes used for placing stimulation electrodes in a second brain region; a plurality of stimulating electrodes; a controller, a multi-channel stimulation module, an electroencephalogram signal processing and feedback regulation and control module and a communication module are arranged in the control box; the electroencephalogram signal module is configured to detect the phase of specific neural oscillation of a first brain region in real time; the controller is configured to accurately control the time or phase of stimulation (such as transcranial alternating current stimulation or pulse stimulation) output by the multi-channel stimulation module in the second brain region based on the detected real-time phase information, and cross-region phase-locked stimulation is achieved. The device is suitable for personalized nerve regulation and control application in a family environment while remarkably simplifying user operation, reducing cost and improving comfort and portability.

The invention discloses an original script-oriented AI autonomous plot structure adaptive generation system, and relates to the technical field of creation assistance, and the system specifically comprises the following modules: a structure extraction analysis module, an emotional role analysis module, a plot inference module, a scene generation optimization module, a conservation target generation module, an adaptive control module, and a constraint punishment module. According to the method, a multi-level narrative structure and a causal relationship graph are constructed to form an emotion vector and a trajectory curve, an optimal causal path is generated based on a graph neural network, a graph convolution / attention mechanism and a graph generation algorithm, and emotion toning and conservation target driven text generation are performed on scene and dialogue levels. Structural consistency and emotional arcs are optimized in real time in combination with self-adaptive control, plot path weighting and rewriting triggering are performed by utilizing a multi-dimensional emotional space and a neural oscillator network, intelligent structured management of a script is realized, and script creation efficiency and quality are improved.

The present invention relates to an electrical nerve stimulator configured to deliver an electrical signal to a user to entrain Mu-band neural oscillations and / or Beta-band neural oscillations. The electrical nerve stimulator may be used in the treatment of neurological conditions associated with desynchronization of neural oscillations, such as Tourette's syndrome. The electrical nerve stimulator may be wearable on a wrist of a user, and may be configured to deliver rhythmic stimulation at a frequency falling within the same frequency range of Mu-band or Beta-band neural oscillations. The invention provides a convenient means of treating conditions such as Tourette's syndrome without the need for specialist medical equipment or facilities. Also disclosed is a method of delivering an electrical signal to a user to entrain Mu-band neural oscillations and / or Beta-band neural oscillations.

The invention discloses a personalized prediction system and device for temporal lobe epilepsy based on multi-site frequency-crossing coupling, and the system comprises a signal collection module which is used for synchronously collecting electroencephalogram signals at a plurality of sites of the brain; the signal preprocessing module is used for preprocessing the collected electroencephalogram signals, drawing a cross-frequency coupling common modulation graph and segmenting frequency bands; the feature extraction module is used for extracting phase amplitude coupling features including average coupling center phase frequency, amplitude frequency and average coupling center intensity; the prediction module is used for inputting the phase amplitude coupling characteristics into a pre-trained machine learning model and outputting a prediction result of the temporal lobe epilepsy; and the early warning module is used for generating an early warning signal according to the prediction result. According to the method, a traditional fixed frequency band analysis normal form is broken through, and phase / amplitude migration characteristics are captured through energy distribution weighting; constructing a personalized adaptation mechanism to identify epilepsy atypical cross-frequency coupling features; and the interpretability of a neural oscillation desynchronization mechanism is higher.

The invention relates to the technical field of artificial intelligence, and provides a network intrusion detection method based on multidimensional feature collaborative analysis, and the method comprises the steps: calculating the correlation degree eta ij of any two feature columns in an original traffic feature matrix, and when eta ij is greater than eta thr, generating a new feature through Qij = Xi * Xj + (1-eta ij) * (Xi + Xj), outputting a dimension reduction feature matrix to perform nonlinear spatial transformation on the dimension reduction feature matrix X ', dynamically retrieving associated knowledge through a learnable memory library, fusing current input features and memory enhancement features, and outputting refined features; decomposing the refined features to a biological neural oscillation frequency band, applying carrier modulation, and performing feature reconstruction to generate a space-time correlation tensor; the refined features and the space-time correlation tensor are fused, and the intrusion behavior classification probability is output through multi-stage abstract compression and regularization processing; screening difficult samples based on classification confidence to construct a training library, and obtaining a conventional detection model and a special and precise detection model; and preferentially calling the special and precise model during real-time detection, and switching to the conventional model when the confidence coefficient is smaller than a confidence coefficient threshold value.

A method and a system for rhythmic motion control of a robot based on a neural oscillator, including: acquiring a current state of the robot, and a phase and a frequency generated by the neural oscillator; and obtaining a control instruction according to the acquired current state, phase and frequency and a preset reinforcement learning network so as to control the robot. The preset reinforcement learning network includes an action space, a pattern formation network and the neural oscillator. A control structure designed by the present disclosure, which is composed of the neural oscillator and the pattern formation network, can ensure formation of an expected rhythmic motion behavior; and meanwhile, a designed action space for joint position increment can effectively accelerate the training process of rhythmic motion reinforcement learning, and solve a problem that design of the reward function is time-consuming and difficult in learning with existing model-free reinforcement learning.

The invention relates to the technical field of neuroacoustics, in particular to a personalized brain wave induced audio generation method and system, and the method comprises the steps: S1, extracting multi-dimensional features of an input audio signal, the extracted dimensions at least comprising a time domain feature, a frequency domain feature, a pitch feature, an emotion feature and a complex feature; s2, on the basis of the multi-dimensional features extracted in the step S1, parameters of four neuroacoustic induction mechanisms are calculated in parallel; and S3, according to the features obtained in the step S1 and the quadruple mechanism parameters obtained in the step S2, signals corresponding to the four mechanisms are fused through a weight distribution strategy and a signal synthesis formula to generate a final brain wave induction audio. The method has the advantages that four mechanisms of rhythm synchronization, harmonic resonance, neural oscillation coupling and binaural beat frequency effect are fused, parameters of all the mechanisms are dynamically adjusted by analyzing music characteristics, and efficient personalized brain wave induction is achieved.

The invention provides an Alzheimer's disease intervention rehabilitation system based on photoacoustic magnetic vibration wave resonance, and relates to the technical field of medical health. The system comprises a data acquisition unit, a correction unit, a stimulation unit and an evaluation feedback unit. The data acquisition unit is used for acquiring multi-dimensional data such as electroencephalogram signals and pathology associated data of a user and extracting target neural oscillation features from the multi-dimensional data; a correction unit constructs a brain structure-pathology association model, and performs pathology association correction on the target neural oscillation features; and the stimulation unit generates co-stimulation containing at least two modes of light, sound, magnetism and vibration based on the corrected features, configures stimulation parameters according to a predetermined time domain or frequency domain relationship, and intervenes the coupling target. And the evaluation feedback unit evaluates the intervention effect by collecting the stimulated multi-dimensional data. According to the method, the multi-mode stimulation is bound with the pathological-neural oscillation coupling characteristics, so that accurate and collaborative intervention aiming at the pathophysiological mechanism of the Alzheimer's disease is realized.

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 discloses a system for analyzing the influence of photostimulation on amygdala function regulation and control, and relates to the field of brain nerve regulation and control, the system comprises a photostimulation module, an intracranial electroencephalogram recording module and an amygdala physiological activity analysis module, the photostimulation module issues intermittent photostimulation to a subject; the intracranial electroencephalogram recording module is used for recording continuous electroencephalogram signal data of the amygdala before and during photostimulation of the subject; the amygdala physiological activity analysis module preprocesses the electroencephalogram signal data, judges the amygdala function change of a specific frequency band according to the neural oscillation energy change rate of the specific frequency band, and forms an optical frequency-amygdala function change database, thereby obtaining the optical stimulation frequency influencing the amygdala function. The invention can overcome the problem that the scalp electroencephalogram is difficult to capture the nerve activity of the amygdala nucleus; in addition, the photostimulation frequency capable of influencing the functions of the amygdala kernel is obtained, and clinical reference is provided for accurately intervening the functions of the amygdala kernel.

The invention provides a brain-computer interface interaction method and system based on a neural oscillation mode, and relates to the technical field of brain-computer interface interaction. Clean data streams are obtained through multi-lead electroencephalogram collection and filtering artifact suppression, multi-frequency oscillation features are extracted and fused, an end-to-end time delay model is established to achieve phase compensation in combination with individualized dominant frequency and incubation period dynamic calibration, candidate actions are judged and generated according to signal quality, and the accuracy of phase compensation is improved. The external equipment is controlled in combination with state mapping and trigger execution, the problems of single feature dimension, uncompensated individual difference, uncorrected time delay error, insufficient signal quality control and the like in the existing method are solved, and the stability, accuracy and adaptability of interaction are improved.

The invention relates to the technical field of transcranial alternating current waveform control, and discloses a current waveform control method and system based on transcranial alternating current stimulation, and the method comprises the steps: carrying out the time-frequency analysis of an electroencephalogram signal through short-time Fourier transform; generating an initialized current waveform according to individual neural oscillation characteristics; different stimulation modes are selected according to task requirements to adjust the current waveform; and frequency deviation and phase deviation are calculated through real-time feedback, and the current waveform is dynamically compensated and adjusted. The technical problems that in the prior art, a stimulation method with fixed frequency and amplitude is usually adopted, and particularly under the condition of large individual difference, accurate individualized current waveform control is difficult to achieve, so that the stimulation effect is unstable and poor are solved. Due to the fact that the frequency and the phase of the current waveform can be accurately adjusted according to the actual neural oscillation state of the brain through real-time feedback and the dynamic frequency, the synchronism of stimulation and brain neural oscillation is improved.

Embodiments described herein relate to systems, devices, and methods for monitoring brain activity and delivering electrical brain stimulation to a patient. In some embodiments, a system can deliver responsive electrical stimulation in a closed-loop manner and can offer real-time or near real-time monitoring of induced neurophysiological effects. After detecting a targeted brain pattern (i.e., an epileptic seizure), the system may deliver high-intensity ultra-short electrical stimulation impulses non-invasively or minimal-invasively to diminish or stop the neural oscillations underlying the epileptic seizure. The stimuli are delivered in time and space, relative to the emerging seizure rhythm patterns, such that they diminish or terminate the seizure. The system may include an implantable device including one or more electrodes electrically coupled to one or more processors. The processor(s) may be operatively coupled to a memory, one or more communication modules, and may optionally be coupled to a battery and one or more additional sensor(s).

This invention discloses a neurophysiological behavior-driven multimodal intelligent learning guidance system and method. The system achieves closed-loop control by constructing a cognitive-emotional-task adaptive adjustment space: 1) In the cognitive dimension, it integrates EEG neural oscillations and frontoparietal network connections, fNIRS cerebral oxygen metabolism indicators, and eye-tracking data to construct a multimodal cognitive load assessment model; 2) In the emotional dimension, it combines the nonlinear characteristics of skin conductance signals with facial expression recognition to quantify learners' motivation levels and emotional valence in real time; 3) In the task dimension, it parameterizes task difficulty and interaction form as moderating variables. During system operation, the system locates the learner's position on the cognitive-emotional state plane in real time and dynamically adjusts task dimension parameters through a nonlinear mapping strategy. This invention effectively utilizes the decoupling ability of multimodal signals and the interaction of intention to achieve precise adaptive delivery of teaching content.

Systems and methods of the present disclosure are directed to neural stimulation via audio and visual stimulations. The combination and / or sequence of audio and visual brain stimulations can adjust, control or otherwise manage the frequency of the neural oscillations to provide beneficial effects to one or more cognitive states or cognitive functions of the brain, while mitigating or preventing adverse consequences on a cognitive state or cognitive function that stems from sleep deprivation. In doing so, the present systems and methods can reduce sleep fragmentation, improve sleep quality, and slow the progression of cognitive decline in a subject with Alzheimer's disease and MCI.

The inventors discovered that neural oscillatory activities in the sensorimotor cortex region of the brain and the basal ganglia system of the brain are indicative of physiological gait events. The invention utilizes this discovery, along with recent advances in neural interfaces and machine learning techniques, to provide new and useful methods for identifying gait events directly from the neural activity of an individual. In particular, methods and systems are provided that produce trained classification models capable of accurately identifying left and right leg events during walking. Trained classification models exhibiting above a threshold level of accuracy may then be used to generate control signals for real time adaptive deep brain stimulation in order to improve gait function in patients with Parkinson's disease.

The invention relates to a transcranial electrical stimulation system and a storage medium, and the system comprises a neural signal sensing module which is configured to be used for collecting neural activity signals of the brain of a subject in real time; a transcranial alternating current stimulation module configured to apply transcranial alternating current stimulation to a target area of the brain of the subject; the real-time signal processing and control module is configured to be in communication connection with the neural signal sensing module and the transcranial alternating current stimulation module at the same time; the real-time signal processing and control module is configured to: receive a neural activity signal; extracting features of neural oscillation in a preset target frequency band from the neural activity signal in real time, wherein the features at least comprise an instantaneous phase; predicting a future phase of the neural oscillation based on the instantaneous phase, and generating a stimulation parameter in an anti-phase relationship with the future phase; and based on the stimulation parameters, controlling the transcranial alternating current stimulation module to apply adaptive transcranial alternating current stimulation to the target area of the brain of the patient.

Systems and methods for neural modeling are disclosed. A perfusable microfluidic platform with human hippocampal organoid integration and functional vasculature is disclosed. Vascularized human brain organoids can be developed on a microphysiological platform. The system can include a membrane configured to enable perfusion throughout the membrane. The membrane can couple with a carrier. The system can include the carrier including channels. The channel can be configured to deliver a first set of cells to the membrane through a lumen of the membrane; and deliver a second set of cells to the membrane through an exterior of the membrane. The system can include monitoring components to continuously and non-invasively measure the onset and development of neural oscillations.

The application provides an analgesia system based on brain nerve oscillation regulation, relates to the technical field of pain regulation, and first establishes a brain signal standardization information base of healthy people; for each pain patient, the brain nerve signal index of the pain patient is compared with a standard normal range obtained from the brain signal standardization information base of healthy people to determine abnormal electroencephalogram nerve indexes of each pain patient; then, according to the pain specificity of each pain patient, the correlation between the behavior indexes and the abnormal electroencephalogram nerve indexes of the pain patient is analyzed to determine potential regulation target points; the regulation target points to be intervened and regulation modes are selected from the potential regulation target points; different regulation modes are selected according to actual requirements to regulate the pain of the pain patient; and then, the pain regulation effect is evaluated, so that the effect of relieving pain is achieved, the analgesia system has the characteristics of safety, economy and easy operation, and is expected to be applied to the treatment of clinical pain.

The invention discloses a chronic insomnia accurate detection method fusing EMD and multi-channel E-PCNN, and relates to the technical field of chronic insomnia disorder detection. The invention provides an enhanced pulse coupled neural network (E-PCNN), and aims to improve the automatic feature extraction capability of EEG signals and improve the time sequence modeling precision. The E-PCNN combines a multi-scale feature fusion MSFF and a dynamic channel weighted DIC mechanism, and can capture and analyze the rhythm difference of a chronic insomnia disorder group and a normal group in resting state electroencephalogram signals in real time under the conditions of high precision and high efficiency. Meanwhile, the E-PCNN introduces an adaptive time step length, so that the E-PCNN has higher dynamic flexibility when modeling EEG time sequence information. Besides, the E-PCNN adopts a multi-channel convolution structure, EEG signals from different key brain regions can be processed at the same time, the adaptive capacity of the model to neural oscillation changes is improved in combination with a dynamic spiking neuron mechanism, and EEG frequency domain feature modes of chronic insomnia patients and normal individuals can be distinguished more effectively.

The invention discloses a portable frequency-adjustable transcranial magnetic stimulation system and a stimulation method thereof. The system comprises an application program, a central control system and an intervention module, wherein the application program, the central control system and the intervention module are arranged in portable insomnia therapeutic equipment; the application program and the intervention module are both connected with the central control system. According to the invention, accurate matching with human body natural slow wave rhythm (Delta wave 1-4Hz and Theta wave 4-8Hz) is realized by releasing 1-8Hz Gamma low-frequency magnetic pulse waves capable of inhibiting excessive activity through control, slow wave synchronism is enhanced through frequency-phase resonance of magnetic field-neural oscillation, sleep is improved, and an instantaneous alternating magnetic field is generated to penetrate through skull to act on cerebral cortex, so that sleep quality is improved. Compared with a traditional medicine therapy, an invasive therapy and a non-invasive therapy, the side effect is smaller, the risk is lower, sleep can be further improved by playing soothing music, and the sleep neural circuit can be conveniently regulated and controlled at home to conveniently improve insomnia.

The invention discloses a neurovascular coupling dynamic modeling method based on CEEMDAN and GLM fusion, and relates to the technical field of biological signal processing. Adaptive non-parametric frequency band segmentation is carried out on EEG signals by introducing a white noise enhanced ensemble empirical mode decomposition strategy; the individual specific neural oscillation mode (such as beta-gamma mixed rhythm after stroke or abnormal enhancement of gamma frequency band) can be captured without presetting a frequency band boundary. Meanwhile, the intrinsic mode function time-varying frequency spectrum features generated by the CEEMDAN are embedded into the hemodynamic response function reconstruction process of the GLM model, individualized neural oscillation features are dynamically extracted through an energy entropy weighting mechanism, and the space-time heterogeneity of neural blood flow dynamic coupling in the pathological state is quantitatively analyzed. According to the method, the physiological limitation of traditional fixed frequency band division is overcome, the analysis precision of non-stationary signals in a pathological noise environment is remarkably improved through an anti-modal aliasing adaptive decomposition technology, and a dynamic modeling framework is provided for research of a neurovascular coupling mechanism.

This invention relates to the field of transcranial alternating current waveform control technology, and discloses a method and system for controlling current waveforms based on transcranial alternating current stimulation. The method includes: performing time-frequency analysis of electroencephalogram (EEG) signals using short-time Fourier transform; generating an initial current waveform based on individual neural oscillation characteristics; selecting different stimulation modes to adjust the current waveform according to task requirements; and calculating and dynamically compensating for frequency and phase shifts through real-time feedback. Compared to existing stimulation methods that typically use fixed frequencies and amplitudes, especially under conditions of significant individual differences, it is difficult to achieve precise individualized current waveform control, leading to unstable and poor stimulation effects. This application, through real-time feedback and dynamic frequency, can precisely adjust the frequency and phase of the current waveform according to the actual neural oscillation state of the brain, improving the synchronization between stimulation and brain neural oscillations.

A system for closed-loop modulation of audio data includes a neural oscillation modulator. The neural oscillation modulator receives initial audio data selected by a user. The neural oscillation modulator receives current neural oscillation data for a current time period. The neural oscillation modulator obtains historical neural oscillation data for historical time periods. The neural oscillation modulator determines modulated audio data based on the initial audio data, the current neural oscillation data, the historical data, and a pre-configured input-output relationship. The current neural oscillation data is phase shifted based on the historical data and the pre-configured input-output relationship to determine the modulated audio data. The neural oscillation modulator delivers the modulated audio data to a user device. The user device generates audio waves based on the modulated audio data and delivers the audio waves to the user to adjust neural oscillation activity of the user.

The invention discloses a cerebral apoplexy exercise rehabilitation system and method based on cortical hippocampus network neural oscillation regulation and control, electronic equipment and a storage medium, and the system comprises a cerebral apoplexy patient comprehensive evaluation module which is used for carrying out the comprehensive and systematic evaluation of a cerebral apoplexy patient; the individualized model building module is used for customizing an individualized model for a set patient; the transcranial alternating current stimulation accurate intervention module is used for accurately intervening transcranial alternating current stimulation; and the multi-modal joint evaluation and dynamic monitoring module is used for carrying out multi-modal joint evaluation and dynamic monitoring. According to the cerebral apoplexy exercise rehabilitation system and method based on cortical hippocampus network neural oscillation regulation and control, the electronic equipment and the storage medium, exercise function recovery can be promoted.

A light-emitting diode (LED) luminaire with dual-frequency visual stimulation flickers, comprising: two sets of LED arrays disposed on a printed circuit board in an interlaced configuration; a pulse-width modulation (PWM) pulse generator; and an LED driving circuit, is configured to replace a conventional luminaire. The PWM pulse generator is configured to generate two modulating signals at respective temporal modulation frequencies. The LED driving circuit is configured to receive the two modulating signals and generate corresponding driving currents to drive the two sets of LED arrays, thereby producing visual stimulation flickers at the two temporal modulation frequencies. The resulting dual-frequency visual stimulation flickers induce a symmetric harmonic frequency perceptible by the human brain, thereby enhancing a benign brain oscillation and endogenously triggering and entraining a gamma-band neural oscillation.