State evaluation and closed-loop feedback regulation method and system
By fusing multimodal physiological data and using a deep learning evaluation model, combined with an edge computing architecture, real-time closed-loop control of multi-sensory feedback was achieved. This solved the problems of single device monitoring dimensions and lack of visual interaction, and improved the accuracy of status assessment and user compliance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENZHEN LIOZHI TECHNOLOGY CO LTD
- Filing Date
- 2026-03-25
- Publication Date
- 2026-07-14
AI Technical Summary
Existing portable brainwave or relaxation devices have limited monitoring dimensions, distorted state assessments, and lack immersive visual closed-loop interaction, resulting in poor user compliance.
By employing multimodal physiological data fusion, including full-band EEG, heart rate, respiration, and electromyography, combined with a deep learning evaluation model, multisensory feedback instructions are generated. The algorithm is then extrapolated to external devices through an edge computing architecture to achieve real-time closed-loop feedback of vision, hearing, and touch.
It improves the accuracy of state assessment and user compliance, significantly shortens the time to enter a mindfulness or relaxation state through visual closed-loop interaction, and enhances the device's lightweight design and battery life.
Smart Images
Figure CN122376954A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of smart wearable device technology, and in particular to a state assessment and closed-loop feedback control method and system. Background Technology
[0002] With the fast pace of modern life, psychological anxiety and excessive fatigue are common. Mindfulness meditation and psychological adjustment exercises have been proven to effectively improve these problems. However, traditional adjustment methods rely heavily on professional guidance or user self-realization, which are difficult to grasp and whose effects are hard to quantify.
[0003] Existing portable brainwave or relaxation devices (such as meditation headbands) mainly suffer from the following technical problems: Single monitoring dimension and distorted state assessment: Most devices rely only on single-channel EEG or simple optical heart rate, without combining electromyography, respiration and full-band EEG for multimodal fusion, resulting in low accuracy in recognizing the user's deep emotions and cognitive fatigue.
[0004] Lack of immersive visual closed-loop interaction: Modern people rely heavily on vision to acquire information, but existing devices mainly rely on auditory interventions such as playing "white noise," and mobile apps are only used as static data viewers. The lack of closed-loop regulation methods that can convert brainwave states into visual experiences in real time (such as AR / VR or screen animations) makes users easily distracted during training, resulting in poor adherence. Summary of the Invention
[0005] The technical problem to be solved by the embodiments of the present invention is to provide a state assessment and closed-loop feedback control method and system, which transforms the dry EEG data into real-time dynamic feedback that is visualized, audible, and tactile, thereby realizing closed-loop digital neuromodulation.
[0006] To address the aforementioned technical problems, this invention proposes a state assessment and closed-loop feedback control method, comprising: Step 1: Acquire the user's multimodal physiological data, which includes at least full-band EEG signals and at least one non-EEG physiological auxiliary feature; Step 2: Based on the preset deep learning evaluation model, feature extraction and attention allocation are performed on the multimodal physiological data to output the original predicted score; at the same time, the physiological baseline of the user in the preset initial time period is extracted, and the deviation between the current multimodal physiological data and the physiological baseline is calculated to generate a dynamic correction coefficient; the dynamic correction coefficient and the original predicted score are weighted and calculated to obtain a continuous instantaneous state score. Step 3: Based on the instantaneous state score, generate and execute feedback guidance instructions for the user's multi-sensory dimensions; wherein, the feedback guidance instructions are configured with a sensory priority scheduling strategy, and when the instantaneous state score reaches a preset target state threshold, a sensory adaptive extinction mechanism is triggered to gradually reduce the feedback intensity of some sensory dimensions and maintain at least one implicit physiological synchronous feedback to avoid overstimulation.
[0007] Accordingly, embodiments of the present invention also provide a state assessment and closed-loop feedback control system, including a wearable headband and an edge computing device, and further including: Data acquisition and transmission module: Located at the wearable headband, it is used to acquire the user's multimodal physiological data in real time and transmit it to the edge computing device. Adaptive correction evaluation module: set on the edge computing device, used to output the original predicted score based on the preset deep learning evaluation model, and generate dynamic correction coefficients by combining the deviation of the user's initial physiological baseline, and then calculate the continuous instantaneous state score through the two. The multi-sensory scheduling feedback module includes a visual display terminal, an auditory sound generation unit, and a tactile vibration unit. The multi-sensory scheduling feedback module is used to receive the instantaneous state score, generate and execute feedback guidance instructions. Furthermore, when it is determined that the user has entered the target state, the multi-sensory scheduling feedback module executes a sensory adaptive extinction mechanism to dynamically weaken sensory stimuli with high cognitive load and retain physiological anchoring feedback with low cognitive load.
[0008] The beneficial effects of this invention are as follows: 1. This invention provides a highly immersive and precise "perception-evaluation-intervention" visual closed loop; 2. Existing products employ limited intervention methods. This invention uniquely introduces a dedicated interactive program that links with an external visual terminal (screen / AR / VR), mapping the user's EEG changes in real time to dynamic changes in the visual image, forming a visual loop of "what you see is what you think." This deep visual interaction can more effectively attract the user's attention and significantly shorten the time required to enter a state of mindfulness or relaxation.
[0009] 3. High-accuracy anti-interference assessment resulting from multimodal data fusion: Single EEG signals are highly susceptible to external interference and have limited dimensions. This invention synchronously fuses full-frequency (0.1-250Hz) EEG characteristics with heart rate, respiration, electromyography (EMG), and motor signals. This invention effectively eliminates artifact interference from single signals, resulting in more accurate and scientific assessments of emotional load and fatigue levels.
[0010] 4. This invention adopts an edge computing architecture, which balances the requirements of lightweight devices and high computing power: This invention innovatively separates computationally intensive algorithm derivation (such as real-time decoding and multimodal fusion calculation of 250Hz high sampling rate EEG signals) from the headband body and transfers it to edge computing devices such as smartphones for execution. This architecture significantly reduces the performance requirements of the headband chip, effectively reducing the headband's size, weight, power consumption, and heat generation, and significantly improving user wearing comfort and device battery life; at the same time, it fully utilizes the powerful idle computing power of modern smartphones, ensuring low latency and high accuracy of algorithm derivation.
[0011] 5. Strong neuromodulation effect through multi-sensory synergy: This invention simultaneously utilizes auditory (speaker sound waves), tactile (motor vibration), and visual (external screen / glasses) sensations to create a three-dimensional sensory experience. Compared to traditional devices that only provide sound guidance, this solution is multifunctional and can flexibly combine stimulation methods according to different scenarios (such as gentle audio-visual stimulation before falling asleep or strong visual feedback for mental exercise), making it more widely applicable. Attached Figure Description
[0012] Figure 1 This is a schematic flowchart of the state assessment and closed-loop feedback control method according to an embodiment of the present invention.
[0013] Figure 2 This is a schematic diagram of the system processing flow when a user performs meditation / psychological adjustment according to an embodiment of the present invention.
[0014] Figure 3 This is a user diagram illustrating the state assessment and closed-loop feedback control system according to an embodiment of the present invention.
[0015] Figure 4 This is a schematic diagram of the process when a user begins mindfulness training according to an embodiment of the present invention. Detailed Implementation
[0016] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0017] In this embodiment of the invention, directional indicators (such as up, down, left, right, front, back, etc.) are only used to explain the relative positional relationship and movement of each component in a specific posture (as shown in the figure). If the specific posture changes, the directional indicator will also change accordingly.
[0018] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features.
[0019] Please refer to Figure 1 The state assessment and closed-loop feedback control method of this invention includes steps 1 to 3.
[0020] In specific implementation, the execution entities of this invention are the microprocessor built into the headband (responsible for signal acquisition and transmission) and the dedicated interactive feedback program running on an external smart terminal (such as a mobile phone, PC, AR / VR device) (responsible for edge computing and instruction generation).
[0021] Step 1: Acquire the user's multimodal physiological data, which includes at least full-band EEG signals and at least one non-EEG physiological auxiliary feature. The auxiliary feature includes heart rate, respiration, head movement, and electromyography (EMG). For the full-band EEG signals from 0-125Hz, short-time Fourier transform or wavelet transform is used to construct a time-frequency-space three-dimensional feature matrix containing a time axis, a frequency axis, and a channel axis. Specifically, Step 1 includes: (1) Signal acquisition: The user's original physiological electrical signals are acquired in real time through the multimodal signal acquisition module built into the headband.
[0022] (2) Preprocessing and transmission: The physiological electrical signals are converted from analog to digital and packaged by the microprocessor built into the headband. The raw physiological data with a high sampling rate (e.g., 250Hz) is transmitted to the external smart terminal, which is an edge computing device, in real time with low latency through the built-in wireless communication module.
[0023] Step 2: Based on the preset deep learning evaluation model, feature extraction and attention allocation are performed on the multimodal physiological data to output the original predicted score; at the same time, the physiological baseline of the user in the preset initial time period is extracted, and the deviation between the current multimodal physiological data and the physiological baseline is calculated to generate a dynamic correction coefficient; the dynamic correction coefficient and the original predicted score are weighted and calculated to obtain a continuous instantaneous state score.
[0024] In practice, raw physiological data is received through a dedicated interactive feedback program on a smart terminal, and the following multi-level evaluation logic is executed: (1) Multimodal feature decoding and three-dimensional feature matrix construction: First, non-EEG auxiliary features such as heart rate, respiration, head movement and electromyography (EMG) are decoded from the original signal; at the same time, for the full-band EEG signal of 0-125Hz, unlike traditional devices that only extract a single frequency band, this invention uses short-time Fourier transform (STFT) or wavelet transform to construct a "time-frequency-space three-dimensional feature matrix" containing time axis, frequency band axis and channel axis, so as to completely preserve the phase synchronization information across frequency bands.
[0025] (2) Deep learning evaluation of multimodal feature fusion (raw score): The above three-dimensional feature matrix and auxiliary features are input into a preset deep learning evaluation architecture. This architecture includes a multi-head attention mechanism, which automatically assigns dynamic attention scores (i.e., dynamic weights) to different frequency bands in 0-125Hz (such as high gamma waves representing distraction, 100-125Hz) and auxiliary features. After processing by fully connected layers, the model outputs a raw predicted score that reflects the instantaneous physiological state.
[0026] (3) Dynamic weight calibration (corrected score) based on baseline drift: To eliminate individual baseline differences and environmental noise, this invention introduces a baseline adaptive correction mechanism. This invention collects multimodal data 30 seconds before each training session to lock the initial physiological baseline. In real-time evaluation, the deviation of the current multimodal features from the baseline is calculated, and a dynamic correction coefficient N is generated after normalization.
[0027] (4) State Index Output: The final continuous state score is calculated by multiplying the original predicted score by the dynamic correction coefficient N. This score is continuously mapped to the range of 0-100 and is used as the input threshold for the intensity of subsequent multi-sensory feedback, comprehensively reflecting the user's current cognitive state, emotional load and physical and mental relaxation level.
[0028] Step 3: Based on the instantaneous state score, generate and execute feedback guidance instructions (including auditory feedback instructions, visual feedback instructions, and tactile feedback instructions) for the user's multi-sensory dimensions; wherein, the feedback guidance instructions are configured with a sensory priority scheduling strategy, and when the instantaneous state score reaches a preset target state threshold, a sensory adaptive extinction mechanism is triggered to gradually reduce the feedback intensity of some sensory dimensions and maintain at least one implicit physiological synchronous feedback to avoid overstimulation.
[0029] Based on the final state score output in step 2, this invention uses a dedicated interactive feedback program to generate feedback guidance instructions, which, in conjunction with the headband hardware, provide multi-sensory closed-loop feedback to guide the user into a target cognitive state (such as mindfulness focus or deep relaxation). (1) Dynamic scheduling mechanism of multi-sensory weights (overload prevention design): The system has a built-in sensory priority scheduling algorithm. When it is determined that the user is in a "high cognitive load or high distraction state" in the early stage of training, visual and auditory feedback are given high weights to capture attention; when features (such as continuous alpha waves and theta waves) indicate that the user is about to or has entered a flow or mindfulness state, the system automatically triggers the "sensory adaptive extinction mechanism" to gradually reduce the intensity of visual and auditory feedback and smoothly transition to retaining only weak tactile anchoring to avoid overstimulation interrupting the state maintenance.
[0030] (2) Visual feedback instructions (real-time rendering pipeline control): The dedicated interactive feedback program uses real-time EEG characteristics as external input variables and directly intervenes in the graphics rendering engine of the external terminal display or AR / VR device. If a surge in high-frequency wave energy representing distraction is detected, the program automatically reduces the sharpness of the visual image edges (dynamic depth blur) or narrows the field of view (FOV) of the image, forcibly guiding the visual focus back to the center; at the same time, the multimodal fusion score is mapped to the velocity of particle flow or changes in light and shadow color temperature in the image, forming a visual closed loop.
[0031] (3) Auditory feedback instructions (neural rhythm induction): Based on the target compensation frequency band, a dedicated interactive feedback program controls the headband speaker or external headphones to generate binaural beats or isochronous sounds. For example, synthetic sound waves with a frequency difference of 4-8Hz are output to the left and right ears respectively, and the brain's "frequency response induction effect" physical resonance is used to pull the brain wave frequency closer to the Theta band.
[0032] (4) Tactile feedback command (physiological synchronous anchoring): control the vibration motor built into the headband, whose vibration frequency and intensity are phase-synchronized with the real-time respiratory rhythm or heart rate variability (HRV) collected in step 1, providing a subconscious sense of bodily anchoring.
[0033] Meanwhile, the system continuously executes steps 1-3 in a loop at a specific refresh rate (e.g., every 100 milliseconds), compares the error value between the current state and the target state in real time, dynamically fine-tunes the output parameters, and finally generates a quantified training report.
[0034] The state assessment and closed-loop feedback control system of this invention includes a wearable headband, an edge computing device, a data acquisition and transmission module, an adaptive correction and assessment module, and a multi-sensory scheduling feedback module. The wearable headband includes a headband body made of flexible wearable material, serving as the physical support for the entire system. For example, the user's process for meditation / psychological adjustment using the system of this invention is as follows: Figure 2 As shown.
[0035] Data Acquisition and Transmission Module: Located at the wearable headband, this module acquires the user's multimodal physiological data in real time and transmits it to the edge computing device. The multimodal physiological data acquired by the data acquisition and transmission module includes at least full-band EEG signals and at least one non-EEG physiological auxiliary feature; the auxiliary feature includes heart rate, respiration, head movement, and electromyography (EMG); the data acquisition and transmission module uses short-time Fourier transform or wavelet transform to construct a time-frequency-space three-dimensional feature matrix containing time axis, frequency axis, and channel axis for the full-band EEG signals from 0-125H.
[0036] In practice, the data acquisition and transmission module consists of a multimodal physiological signal acquisition module and a main control and wireless transmission module.
[0037] Multimodal physiological signal acquisition module: Located inside the headband body, it includes an EEG electrode array and physiological electrical / motor sensors. The multimodal physiological signal acquisition module is used to acquire brain waves, heart rate, respiration, electromyography, and head movement signals in real time.
[0038] Main control and wireless transmission module: Built into the headband, it is electrically connected to the multimodal physiological signal acquisition module. Due to the adoption of an edge computing architecture, this module is mainly responsible for hardware-level analog-to-digital conversion (ADC), data packetization, and establishing wireless connections (such as Bluetooth Low Energy (BLE) or Wi-Fi), transmitting high-frequency (e.g., 250Hz) sampled multidimensional physiological signals to the external edge computing device in real time.
[0039] In one implementation, heart rate and respiratory signals can be acquired using photoplethysmography (PPG) sensors instead of skin-tight physiological electrodes; head movement signals can also be acquired by capturing eye movements or facial micro-expressions through the front-facing camera of an external visual feedback terminal for auxiliary judgment.
[0040] Adaptive Correction Evaluation Module: Located on the edge computing device, it outputs the original predicted score based on a preset deep learning evaluation model, and generates a dynamic correction coefficient by combining the deviation of the user's initial physiological baseline, thereby calculating a continuous instantaneous state score through both.
[0041] Edge computing device: This refers to external smart devices with high independent computing power, such as smartphones, tablets, or PCs. In specific implementation, the adaptive correction evaluation module is implemented through a dedicated interactive feedback program built into the edge computing device. Dedicated interactive feedback program: Installed and running within the edge computing device. This program is responsible for receiving raw 250Hz EEG data, decoding and deducing the core algorithm, mapping it into a dynamic visual image on the terminal screen (visual closed loop), and simultaneously sending audio and vibration control signals back to the headband's main control module.
[0042] As one implementation method, if the headband uses a higher-performance built-in SoC chip, the adaptive correction evaluation module and the comprehensive evaluation calculation in "Step 2" can be completed locally on the headband, with the mobile phone only serving as a display terminal for the pure visual image; or, the mobile phone can only serve as a data relay station (router), uploading the raw data to the cloud server, where the cloud performs algorithm deduction and then sends the control commands back to the mobile phone and the headband.
[0043] The multi-sensory scheduling feedback module includes a visual display terminal, an auditory sound generation unit, and a tactile vibration unit. The multi-sensory scheduling feedback module is used to receive the instantaneous state score, generate and execute feedback guidance instructions. Furthermore, when it is determined that the user has entered the target state, the multi-sensory scheduling feedback module executes a sensory adaptive extinction mechanism to dynamically weaken sensory stimuli with high cognitive load and retain physiological anchoring feedback with low cognitive load.
[0044] The feedback guidance instructions include visual feedback instructions, auditory feedback instructions, and tactile feedback instructions. Visual feedback instructions: The multi-sensory scheduling feedback module uses real-time EEG signals as external input variables. If a surge in high-frequency wave energy, representing distraction, is detected, the clarity of the visual display edge is reduced, or the field of view is narrowed, forcibly guiding the visual focus back to the center. Simultaneously, the score is mapped to the velocity of particle flow or changes in light and shadow color temperature in the image, forming a visual closed loop. Auditory feedback instructions: Based on the instantaneous state score, the multi-sensory scheduling feedback module generates binaural beats or isochronous sounds through an auditory sound generation unit. Tactile feedback instructions: Based on the instantaneous state score, the multi-sensory scheduling feedback module generates vibrations through a tactile vibration unit. The vibration frequency and intensity are phase-synchronized with the real-time respiratory rhythm or heart rate variability acquired by the data acquisition and transmission module.
[0045] In practice, the multi-sensory scheduling feedback module is controlled by a dedicated interactive feedback program on the edge computing device to emit physical stimuli.
[0046] Hearing unit: Built-in amplifier for releasing music and sound waves of specific frequencies for auditory control.
[0047] Tactile vibration unit: Built-in vibration motor to provide tactile vibration feedback.
[0048] Visual display terminal: This includes a display independent of the headband, a mobile phone screen, AR glasses, or VR glasses. The visual display terminal establishes a data connection with the headband system via a "wireless communication module."
[0049] The visual display terminal has a built-in dedicated feedback program: this program receives state evaluation data sent by the microprocessor, maps it into dynamic visual images (visual stimuli presented on the screen), thereby constructing a real-time closed-loop system for the user's visual senses.
[0050] As one implementation method, the visual display terminal can be replaced by an intelligent ambient lighting system (such as a smart home full-color ambient light) or a holographic projection device. A temperature feedback module (such as a semiconductor cooling / heating plate) or a microcurrent stimulation module (tDCS / tACS) can be added to the feedback mechanism to further enhance the intervention effects of psychological adjustment and mental training through physical temperature or non-invasive microcurrents.
[0051] This invention collects users' electroencephalograms (EEGs) and multimodal physiological signals, uses a dedicated interactive feedback program for comprehensive evaluation, and forms a closed-loop feedback loop of multisensory stimulation including visual, auditory, and tactile senses. In particular, it introduces a visual interaction system that links with smartphones, displays, and AR / VR glasses to achieve real-time guidance, evaluation, and adjustment of the user's cognitive state and relaxation level. Please refer to [link / reference]. Figure 3 Compared to other solutions, this invention places greater emphasis on real-time feedback, real-time evaluation, real-time guidance, and closed-loop feedback.
[0052] In the system of this invention, please refer to Figure 4 When a user begins mindfulness, the headband collects EEG information, the phone processes the data to evaluate the effect, and vibration, visual, and musical multi-sensory feedback guides the user to practice mindfulness correctly. Once the user enters a mindfulness state, the multi-sensory feedback is reduced or stopped. When the user ends the mindfulness activity, the phone records the mindfulness score and the mindfulness time to evaluate the effectiveness of the user's mindfulness activity.
[0053] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A state assessment and closed-loop feedback control method, characterized in that, include: Step 1: Acquire the user's multimodal physiological data, which includes at least full-band EEG signals and at least one non-EEG physiological auxiliary feature; Step 2: Based on the preset deep learning evaluation model, feature extraction and attention allocation are performed on the multimodal physiological data to output the original predicted score; at the same time, the physiological baseline of the user in the preset initial time period is extracted, and the deviation between the current multimodal physiological data and the physiological baseline is calculated to generate a dynamic correction coefficient; the dynamic correction coefficient and the original predicted score are weighted and calculated to obtain a continuous instantaneous state score. Step 3: Based on the instantaneous state score, generate and execute feedback guidance instructions for the user's multi-sensory dimensions; wherein, the feedback guidance instructions are configured with a sensory priority scheduling strategy, and when the instantaneous state score reaches a preset target state threshold, a sensory adaptive extinction mechanism is triggered to gradually reduce the feedback intensity of some sensory dimensions and maintain at least one implicit physiological synchronous feedback to avoid overstimulation.
2. The state assessment and closed-loop feedback control method as described in claim 1, characterized in that, In step 1, the auxiliary features include heart rate, respiration, head movement, and electromyography; for the full-band EEG signal of 0-125H, short-time Fourier transform or wavelet transform is used to construct a time-frequency-space three-dimensional feature matrix containing time axis, frequency axis and channel axis.
3. The state assessment and closed-loop feedback control method as described in claim 2, characterized in that, In step 3, the feedback guidance instructions include visual feedback instructions: Using real-time EEG signals as external input variables, if a surge in high-frequency wave energy representing distraction is detected, the sharpness of the visual image edges is reduced, or the field of view of the image is narrowed, forcibly guiding the visual focus back to the center; at the same time, the score is mapped to the velocity of particle flow or changes in light and shadow color temperature in the image, forming a visual closed loop.
4. The state assessment and closed-loop feedback control method as described in claim 2, characterized in that, In step 3, the feedback guidance instructions include auditory feedback instructions: Based on the instantaneous state score, a binaural beat or isochronous tone is generated.
5. The state assessment and closed-loop feedback control method as described in claim 2, characterized in that, In step 3, the feedback guidance instructions include haptic feedback instructions: Based on the instantaneous state score, vibration is generated by a vibration motor, and the vibration frequency and intensity are synchronized with the real-time respiratory rhythm or heart rate variability obtained in step 1.
6. A state assessment and closed-loop feedback control system, comprising a wearable headband and an edge computing device, characterized in that, Also includes: Data acquisition and transmission module: Located at the wearable headband, it is used to acquire the user's multimodal physiological data in real time and transmit it to the edge computing device. Adaptive correction evaluation module: set on the edge computing device, used to output the original predicted score based on the preset deep learning evaluation model, and generate dynamic correction coefficients by combining the deviation of the user's initial physiological baseline, and then calculate the continuous instantaneous state score through the two. The multi-sensory scheduling feedback module includes a visual display terminal, an auditory sound generation unit, and a tactile vibration unit. The multi-sensory scheduling feedback module is used to receive the instantaneous state score, generate and execute feedback guidance instructions. Furthermore, when it is determined that the user has entered the target state, the multi-sensory scheduling feedback module executes a sensory adaptive extinction mechanism to dynamically weaken sensory stimuli with high cognitive load and retain physiological anchoring feedback with low cognitive load.
7. The state assessment and closed-loop feedback control system as described in claim 6, characterized in that, The multimodal physiological data acquired by the data acquisition and transmission module includes at least full-band EEG signals and at least one non-EEG physiological auxiliary feature; the auxiliary feature includes heart rate, respiration, head movement and electromyography; the data acquisition and transmission module uses short-time Fourier transform or wavelet transform to construct a time-frequency-space three-dimensional feature matrix containing time axis, frequency axis and channel axis for the full-band EEG signals of 0-125H.
8. The state assessment and closed-loop feedback control system as described in claim 7, characterized in that, The feedback guidance instructions include visual feedback instructions. The multi-sensory scheduling feedback module uses real-time EEG signals as external input variables. If a surge in high-frequency wave energy representing distraction is detected, the clarity of the visual image edges on the visual display terminal is reduced, or the field of view of the image is narrowed to force the visual focus to return to the center. At the same time, the score is mapped to the speed of particle flow or changes in light and shadow color temperature in the image to form a visual closed loop.
9. The state assessment and closed-loop feedback control system as described in claim 7, characterized in that, The feedback guidance instructions include auditory feedback instructions. The multi-sensory scheduling feedback module generates binaural beats or isochronous tones through the auditory sound generation unit based on the instantaneous state score.
10. The state assessment and closed-loop feedback control system as described in claim 7, characterized in that, The feedback guidance instructions include tactile feedback instructions. The multi-sensory scheduling feedback module generates vibration through the tactile vibration unit based on the instantaneous state score. The vibration frequency and intensity are phase-synchronized with the real-time respiratory rhythm or heart rate variability obtained by the data acquisition and transmission module.