Dynamic pain nerve decoding and intervention system based on prefrontal lobe time sequence characteristic window

Abstract

The invention relates to the technical field of medical health data information decoding analysis and processing, and particularly discloses a dynamic pain nerve decoding and intervention system based on a prefrontal lobe time sequence characteristic window, which comprises a signal acquisition module, a dynamic window control module, a phase prediction module, an intervention execution module and an event driving controller. The dynamic window control module intelligently adjusts the duration of a signal analysis window through real-time calculation of the energy ratio of a # imgabs 0 # frequency band to a # imgabs 1 # frequency band, and accurately locks a transient characteristic window in a pain outbreak period; the phase prediction module realizes millisecond-level synchronization of stimulation pulse and a rising edge of action potential of a neural cluster based on a two-factor mapping mechanism of an individualized conduction velocity level and an oscillation frequency; the event-driven controller reduces the power consumption to a wearable device level while maintaining 10 ms-level real-time response through a dynamic resource allocation strategy.

Description

Technical Field

[0001] The present invention relates to a dynamic pain nerve decoding and intervention system based on a prefrontal temporal feature window, belonging to the technical field of medical health data information decoding analysis and processing. Background Art

[0002] Data decoding and intervention of pain nerves is an important research direction in the field of medical health information technology. It analyzes the temporal characteristics of prefrontal EEG signals to achieve objective assessment and precise intervention of pain status. Traditional systems usually use a fixed time analysis window (such as 1 second) to intercept nerve signals and trigger electrical stimulation intervention based on a preset frequency threshold. However, this method has significant defects in practical applications: 1. Insufficient ability to capture transient pain features: The fixed window cannot adapt to the dynamic characteristics of the prefrontal θ-γ cross-band coupling events (the pain outbreak period usually lasts 0.3-0.8 seconds), resulting in key signals such as high-frequency oscillation phase transitions being submerged by smooth noise, and the feature loss rate exceeds 60%.

[0003] 2. Timing mismatch between intervention pulses and neural activity: Existing technologies rely on a unidirectional stimulus-response model, ignoring individual differences in nerve conduction velocity and fluctuations in oscillation frequency. This results in a 50-200ms delay between the stimulation pulse and the action potential of the target nerve cluster, missing the optimal time window for inducing synaptic plasticity and causing the intervention efficiency to decay by more than 50%.

[0004] 3. It is difficult to achieve both real-time performance and power consumption of the system: To improve accuracy, existing solutions often continuously run high-computing algorithms (such as real-time Fourier transform), resulting in end-to-end delays exceeding 50ms and power consumption > 5W, which cannot meet the needs of wearable devices and long-term monitoring scenarios. In response to the above problems, the industry has tried to improve them by optimizing signal processing algorithms (such as adaptive filtering) or increasing hardware computing power, but the former is still limited by a fixed window framework, and the latter increases power consumption and cost pressures.

[0005] See the invention patent with announcement number CN111951958A, which discloses a pain data evaluation method based on autoencoder and related components. This method uses a convolutional neural network (CNN) to extract features from laser evoked potential (LEP) EEG data, and uses a deep separable convolution layer to reduce the number of network parameters, thereby improving the efficiency of the system. Although this method predicts the pain level through a machine learning model and improves the accuracy of pain prediction, this technology relies on a static data feature extraction method and cannot dynamically respond to instantaneous changes in neural signals, resulting in the possibility of missing the precise intervention opportunity during dynamic pain intervention. In addition, the traditional neural network model does not fully consider individual differences, resulting in poor adaptability of the prediction model, especially when dealing with different nerve conduction velocities and oscillation frequency fluctuations, and accurate matching is not achieved. Summary of the invention

[0006] The present invention provides a dynamic pain neural decoding and intervention system based on the prefrontal temporal feature window, the main purpose of which is to solve the problems of incomplete pain feature capture, low intervention efficiency and difficulty in clinical implementation caused by fixed analysis windows, phase delay and high power consumption in the prior art.

[0007] To achieve the above-mentioned purpose, the present invention provides a dynamic pain neural decoding and intervention system based on the prefrontal temporal feature window, comprising: a signal acquisition module for acquiring prefrontal EEG signals in real time through an EEG device, and separating the prefrontal EEG signals using an analog filter bank. Frequency band signal and Frequency band signal, where The frequency range is 4 to 8 Hz. The frequency range is 30 to 80 Hz; A dynamic window control module is connected to the signal acquisition module and includes: an energy ratio calculation unit for calculating Frequency band signal and The frequency band signals are respectively integrated in a sliding time window to calculate the instantaneous energy ratio of the two; the window adjustment unit compresses the signal analysis window to a first preset time length when the instantaneous energy ratio exceeds a preset threshold, otherwise extends it to a second preset time length, wherein the preset threshold is 1.2 to 1.8 times the frequency band energy average, the first preset duration is 0.2-0.4 seconds, and the second preset duration is 0.8-1.6 seconds; A phase prediction module, connected to the dynamic window control module, includes: a frequency detection unit, used to extract the main frequency of the current prefrontal lobe oscillation signal; a delay mapping unit, which matches the corresponding phase compensation time parameter from a pre-stored frequency delay comparison table with different conduction velocity levels according to the main frequency and the individual nerve conduction velocity level, wherein the phase compensation time parameter is used to characterize the advance time amount of the stimulation pulse triggering moment relative to the rising edge of the action potential of the target neural cluster; An intervention execution module, connected to the phase prediction module, configured to trigger an electrical stimulation pulse in the dynamic window compression phase, and to adjust the triggering timing of the stimulation pulse in advance to the rising edge of the action potential of the target neural cluster based on the phase compensation time parameter; The event-driven controller connects the signal acquisition module, the dynamic window control module, the phase prediction module and the intervention execution module, and is configured as follows: when the energy ratio does not exceed the threshold, only the low-power operation of the signal acquisition module and the dynamic window control module is maintained; when the energy ratio exceeds the threshold, the phase prediction module and the intervention execution module are activated to the full-power mode, and the low-power state is restored after the window compression phase ends.

[0008] In a preferred embodiment, the energy ratio calculation unit is implemented by: Frequency band signal and The frequency band signals are full-wave rectified respectively; the rectified signals are smoothed by a first-order low-pass filter with a cutoff frequency of 2 Hz; the smoothed signals are calculated Frequency band signal energy and The ratio of signal energy in the frequency band.

[0009] In a preferred embodiment, the frequency delay comparison table is generated by the following rules: according to the big data of nerve conduction velocity of healthy people and chronic pain patients, the reference delay parameters corresponding to four different conduction velocity levels are preset, wherein the different conduction velocity levels include slow, medium, fast and ultrafast; the reference delay parameter is negatively correlated with the current oscillation frequency, and the delay decreases by 1.5ms for every 10Hz increase in frequency; the parameters are automatically calibrated after each intervention: if the actual phase deviation exceeds If the angle is radian, adjust the corresponding parameter in the table with a step size of ±2ms.

[0010] In a preferred embodiment, the power consumption control of the event-driven controller includes: in low power consumption mode, the signal acquisition module and the dynamic window control module occupy 5% of the chip resources, and the main frequency is reduced to 50MHz; in full power consumption mode, the phase prediction module and the intervention execution module occupy 95% of the chip resources, and the main frequency is increased to 200MHz.

[0011] In a preferred embodiment, the stimulation parameters of the intervention execution module satisfy: the stimulation frequency is consistent with the current prefrontal cortex. The deviation of the oscillation frequency did not exceed ±5%; the stimulation intensity was mapped to a current range of 50 to 500 μA based on the input value of the pain level assessment scale through fuzzy logic rules.

[0012] In a preferred embodiment, the dynamic window control module implements window adjustment through hardware logic, specifically including: using the shift register group embedded in the FPGA to store the signal data in the current window; dynamically controlling the length of the shift register through a programmable counter to achieve continuous adjustment of the window length from 0.1 to 2 seconds.

[0013] In a preferred embodiment, the dynamic pain nerve decoding and intervention system also includes a safety control module, which is used to perform the following: monitoring the number of continuous high-frequency stimulations, and automatically switching to a baseline intervention mode if the pain level does not decrease after three stimulations. The stimulation frequency of the baseline intervention mode is 40 Hz, and the stimulation intensity is 100 μA; when it is detected that the amplitude of the EEG signal exceeds 200 μV, the stimulation output is immediately cut off and an alarm is triggered.

[0014] In a preferred embodiment, the signal acquisition module is compatible with the digital interface of Neuroscan equipment and BioSemi equipment, including: an SPI protocol conversion unit or an I2C protocol conversion unit for receiving raw EEG data from an external device; a signal resampling unit for uniformly converting the input signal to a 250 Hz sampling rate.

[0015] In a preferred embodiment, the frequency delay comparison table contains the following mapping relationship: when the oscillation frequency is between 30 and 40 Hz and the conduction velocity level is medium, the corresponding compensation delay is 20 ms; when the oscillation frequency is between 40 and 60 Hz and the conduction velocity level is fast, the corresponding compensation delay is 15 ms; when the oscillation frequency is between 60 and 80 Hz and the conduction velocity level is ultra-fast, the corresponding compensation delay is 10 ms.

[0016] Compared with the problems described in the background technology, the beneficial effects of the present invention are: Frequency band and Real-time dynamic calculation of frequency band energy ratio (non-fixed threshold) realizes intelligent expansion and contraction of signal analysis window, directly responding to the unique pain coding of prefrontal cortex Cross-band coupling events can accurately lock the 0.2-0.4 second transient feature window during the pain outbreak period, overcome the smooth interference of traditional fixed windows on high-frequency oscillation phase transitions, and significantly improve the integrity of key signal capture; based on the dual-factor mapping mechanism of individualized conduction velocity level and oscillation frequency, combined with delay parameter self-learning calibration, the precise timing matching of the stimulation pulse and the rising edge of the action potential of the target neural cluster is achieved, breaking through the phase mismatch problem caused by nerve conduction delay in traditional interventions, and ensuring the optimal induction window for synaptic plasticity; the hardware-level dynamic resource allocation strategy is adopted to activate the full-power computing module only when pain characteristics appear, so that the system can maintain a 10ms terminal While reducing the end delay, the overall power consumption is reduced to the level of wearable devices, thus reconciling the technical contradiction between "high real-time" and "low power consumption" in medical devices; integrating the objective characteristics of neural signals with the subjective input of clinical pain scores, dynamically mapping stimulation parameters through fuzzy logic, and automatically switching to safe baseline mode when high-frequency intervention is ineffective, constructing a full-chain individualized adaptation logic from signal analysis to safe output, avoiding the risk of over-stimulation in parameter settings of traditional solutions; through standardized protocol conversion and signal resampling modules, directly connecting to the digital interface of existing medical equipment, without hardware modification, you can achieve functional upgrades, meeting the low-cost and high-efficiency system iteration needs of medical institutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a structural diagram of the dynamic pain neural decoding and intervention system based on the prefrontal temporal feature window of the present invention; Figure 2A comparison diagram of nerve cluster stimulation triggering between the conventional method of the present invention and the method of the present invention; Figure 3 This is a structural diagram of the dynamic window control and phase prediction module based on the Xilinx Zynq chip of the present invention.

[0018] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0020] The present application provides a dynamic pain neural decoding and intervention system based on the prefrontal temporal feature window. It includes: a signal acquisition module for acquiring prefrontal EEG signals in real time through an EEG device, and using an analog filter bank to separate the signals that have a greater impact on the decoding and intervention of the pain state. Frequency band signal and Frequency band signal, where The frequency range is 4 to 8 Hz. The frequency band is 30 to 80 Hz; a dynamic window control module, connected to the signal acquisition module, including: an energy ratio calculation unit for Frequency band signal and The frequency band signals are respectively integrated in a sliding time window to calculate the instantaneous energy ratio of the two; the window adjustment unit compresses the signal analysis window to a first preset time length when the instantaneous energy ratio exceeds a preset threshold, otherwise extends it to a second preset time length, wherein the preset threshold is The first preset duration is 1.2 to 1.8 times of the frequency band energy mean, the first preset duration is 0.2-0.4 seconds, and the second preset duration is 0.8-1.6 seconds; a phase prediction module, connected to the dynamic window control module, including: a frequency detection unit, used to extract the main frequency of the current prefrontal lobe oscillation signal; a delay mapping unit, according to the main frequency and the individual nerve conduction velocity level, matches the corresponding phase compensation time parameter from the pre-stored frequency delay comparison table with different conduction velocity levels, the phase compensation time parameter is used to characterize the advance time amount of the stimulation pulse triggering moment relative to the rising edge of the action potential of the target neural cluster; an intervention execution module , connected to the phase prediction module, used to trigger the electrical stimulation pulse in the dynamic window compression stage, and adjust the triggering timing of the stimulation pulse to the rising edge of the action potential of the target neural cluster based on the phase compensation time parameter; an event-driven controller, connected to the signal acquisition module, the dynamic window control module, the phase prediction module and the intervention execution module, configured as: when the energy ratio does not exceed the threshold, only the low power consumption operation of the signal acquisition module and the dynamic window control module is maintained; when the energy ratio exceeds the threshold, the phase prediction module and the intervention execution module are activated to the full power consumption mode, and the low power consumption state is restored after the window compression stage ends.

[0021] In a preferred embodiment, the energy ratio calculation unit is implemented by: Frequency band signal and The frequency band signals are full-wave rectified respectively; the rectified signals are smoothed by a first-order low-pass filter with a cutoff frequency of 2 Hz; the smoothed signals are calculated Frequency band signal energy and The frequency delay comparison table is generated by the following rules: according to the big data of nerve conduction velocity of healthy people and patients with chronic pain, the reference delay parameters corresponding to four different conduction velocity levels are preset, where the different conduction velocity levels include slow, medium, fast and ultra-fast; the reference delay parameter is negatively correlated with the current oscillation frequency, and the delay decreases by 1.5ms for every 10Hz increase in frequency; the parameters are automatically calibrated after each intervention: if the actual phase deviation exceeds If the radian is ±2ms, the corresponding parameters in the table are adjusted in steps of ±2ms; the power consumption control of the event-driven controller includes: in low power mode, the signal acquisition module and the dynamic window control module occupy 5% of the chip resources, and the main frequency is reduced to 50MHz; in full power mode, the phase prediction module and the intervention execution module occupy 95% of the chip resources, and the main frequency is increased to 200MHz; the stimulation parameters of the intervention execution module meet the following requirements: the stimulation frequency is consistent with the current prefrontal cortex. The deviation of the oscillation frequency does not exceed ±5%; the stimulation intensity is mapped to a current range of 50 to 500 μA according to the input value of the pain level assessment scale through fuzzy logic rules; the dynamic window control module implements window adjustment through hardware logic, specifically including: using the shift register group embedded in the FPGA to store the signal data in the current window; dynamically controlling the length of the shift register through a programmable counter to achieve continuous adjustment of the window length from 0.1 to 2 seconds.

[0022] In a preferred embodiment, the dynamic pain nerve decoding and intervention system also includes a safety control module for performing the following operations: monitoring the number of continuous high-frequency stimulations, and automatically switching to a baseline intervention mode if the pain level does not decrease after three stimulations, wherein the stimulation frequency of the baseline intervention mode is 40 Hz and the stimulation intensity is 100 μA; immediately cutting off the stimulation output and triggering an alarm when the amplitude of the EEG signal is detected to exceed 200 μV; the signal acquisition module is compatible with the digital interface of the Neuroscan device and the BioSemi device, including: an SPI protocol conversion unit or an I2C protocol conversion unit for receiving raw EEG data from an external device; a signal resampling unit for uniformly converting the input signal to a 250 Hz sampling rate; the frequency delay comparison table contains the following mapping relationships: when the oscillation frequency is between 30 and 40 Hz and the conduction velocity level is medium, the corresponding compensation delay is 20 ms; when the oscillation frequency is between 40 and 60 Hz and the conduction velocity level is fast, the corresponding compensation delay is 15 ms; when the oscillation frequency is between 60 and 80 Hz and the conduction velocity level is ultrafast, the corresponding compensation delay is 10 ms. That is, in order to ensure that the system can operate stably under the power consumption requirements of wearable devices, the intervention execution module is combined with the event-driven controller to adopt a dynamic power consumption control strategy. When the energy ratio does not exceed the threshold, the system will only enable the signal acquisition module and the dynamic window control module for low-power operation, and the main frequency will be reduced to 50MHz, consuming only 5% of the total power. When the energy ratio exceeds the set threshold, the event-driven controller will activate the phase prediction module and the intervention execution module, enter the full power consumption mode, and increase the main frequency to 200MHz to ensure the timely execution of pulse stimulation and the intervention accuracy reaches the optimal state. After the intervention is completed, the system will automatically restore the low-power state to ensure the optimization of power consumption in long-term use. And in order to improve the safety of the system, this system is designed with a safety control module that can monitor the number of continuous high-frequency stimulations in real time. If the patient's pain level does not decrease after three stimulations, the system will automatically switch to the baseline intervention mode to prevent side effects caused by excessive stimulation. In addition, when the amplitude of the EEG signal exceeds 200μV, the system will immediately cut off the electrical stimulation and trigger an alarm to ensure patient safety. The system uses an SPI or I2C protocol conversion unit and a signal resampling unit to ensure that data from different devices can be uniformly converted to a sampling rate of 250Hz, thereby achieving standardized operations and reducing modifications to existing equipment. These are all extended implementation methods that are known to ordinary technicians in this field.

[0023] Embodiment 1: The signal acquisition module of this embodiment obtains the frontal lobe EEG signal in real time through the EEG device, and divides the signal into Frequency band 4 to 8 Hz and There are two types of frequency bands: 30 to 80 Hz. In order to ensure high-quality signal acquisition, the system uses an analog filter bank to first separate the frequency bands and perform full-wave rectification on the separated signals to avoid signal distortion caused by noise interference. In the dynamic window control module, by calculating Frequency band and The instantaneous energy ratio of the frequency band signal is used to adjust the length of the analysis window. To ensure the accuracy of the analysis process, the system introduces an adaptive adjustment mechanism for the window length based on energy ratio calculation. and When the instantaneous energy ratio of the frequency band exceeds the preset threshold, the signal analysis window will be quickly compressed to a short-term window of 0.2 to 0.4 seconds, which helps to accurately capture transient features and avoid misjudgment caused by feature loss in traditional fixed windows.

[0024] Specifically, the energy ratio calculation is achieved through the following steps: Frequency band and The frequency band signals are full-wave rectified respectively, and the rectified signals are smoothed using a low-pass filter with a cutoff frequency of 2 Hz. The energy ratio of the smoothed signals is calculated: , in, and Respectively Frequency band and The instantaneous energy of the frequency band signal, energy ratio Directly affects the window duration adjustment strategy to ensure the responsiveness and accuracy of the signal analysis process to pain characteristics. Through this dynamic adjustment mechanism, the system can respond to sudden events during the pain outbreak in real time and avoid missing key neural signals in a fixed duration window. Specifically, and Respectively Frequency band 4 to 8 Hz and The instantaneous energy of the signal in the frequency band 30 to 80 Hz. Indicates that within the current time window The energy of the frequency band signal is calculated by The band signal is full-wave rectified and smoothed using a low-pass filter with a cutoff frequency of 2 Hz, and its energy is calculated. Indicates that within the current time window The energy of the frequency band signal, the processing process and The frequency band is the same. Through this formula, we can deduce that the θ frequency band is The instantaneous energy ratio of the frequency band signal is calculated, and the duration of the signal analysis window is dynamically adjusted based on this value. Specifically, when When the preset threshold is exceeded, the system compresses the window to 0.2 to 0.4 seconds to accurately capture the transient characteristics of pain.

[0025] At the same time, in order to ensure the efficiency and accuracy of the dynamic adjustment of the signal analysis window, the following implementation path can be adopted: First, the signals collected from the EEG device are Frequency band 4-8Hz and The frequency band 30-80Hz is separated. This process is achieved through an analog filter. The separated signal is full-wave rectified to obtain the instantaneous signal strength of each frequency band. Next, a first-order low-pass filter (cut-off frequency 2Hz) is used to smooth the rectified signal to remove high-frequency noise. Then, the following calculations are made: Frequency band and Energy of frequency band signal and The instantaneous energy ratio obtained through the above process is , as the basis for dynamically adjusting the signal analysis window length. If the value exceeds the preset threshold, the analysis window will be compressed to 0.2 to 0.4 seconds; if it is below the threshold, the window will be expanded to 0.8 to 1.6 seconds to accommodate different pain signal patterns.

[0026] The phase compensation module is based on a dual-factor mapping mechanism of individualized nerve conduction velocity and oscillation frequency. Each time after obtaining the main frequency of the current prefrontal EEG signal from the frequency detection unit, the system calculates the corresponding phase compensation time based on the frequency and the individual's nerve conduction velocity using the frequency-delay comparison table. Specifically: Assuming the current oscillation frequency is Hz, the conduction velocity is "fast", then according to the frequency-delay comparison table, the system will find the corresponding compensation time, for example, 15ms. radians), the system will automatically adjust the phase compensation time, each adjustment step is 2ms, so as to ensure that each stimulation pulse matches the rising edge of the action potential of the target neural cluster. In order to solve the contradiction between real-time performance and low power consumption, a dynamic power consumption control mechanism can be adopted. The specific implementation steps are as follows: low power consumption mode: when the energy ratio is When the threshold is not exceeded, the system only starts the signal acquisition module and the dynamic window control module, maintains a low power state, and the main frequency is reduced to 50MHz, with resource usage of only 5%. In this mode, the system can continuously monitor signal changes to avoid unnecessary high power consumption. Full power mode: When the energy ratio exceeds the threshold, the system activates the phase prediction module and the intervention execution module, enters the full power mode, and the main frequency is increased to 200MHz, with resource usage of 95%. In this mode, the system can quickly respond to pain signals and accurately trigger intervention pulses to ensure the timeliness and accuracy of treatment. Through this dynamic power control, the system can mobilize sufficient computing resources at critical moments, and effectively reduce power consumption when signal analysis is not frequent, meeting the needs of wearable devices for low power consumption and long-term operation. The safety control module can monitor the number of continuous high-frequency stimulations in real time. When the pain level does not drop significantly after three consecutive stimulations, the system will automatically switch to a safe baseline intervention mode, with a stimulation frequency of 40Hz and a stimulation intensity of 100μA to avoid side effects caused by excessive stimulation. At the same time, when the amplitude of the EEG signal exceeds 200μV, the system will immediately cut off the electrical stimulation output and trigger an alarm to ensure the safety of the patient.

[0027] At the same time, in order to solve the phase mismatch problem caused by individual differences in nerve conduction velocity in the prior art, this system designs a personalized phase prediction mechanism. This mechanism is based on individualized nerve conduction velocity, combined with the dual-factor mechanism of oscillation frequency and delay mapping, to provide accurate phase compensation. For example, the phase prediction module extracts the main frequency of the current prefrontal oscillation signal through the frequency detection unit, and obtains the corresponding phase compensation time from the pre-stored frequency delay comparison table according to the oscillation frequency and nerve conduction velocity level. The phase compensation table is generated by a large amount of clinical data and an individualized conduction velocity model. After each intervention, if the actual phase deviation exceeds 𝜋 / 8 radians, the system will automatically adjust the corresponding compensation time. In the optimization process of this module, the phase adjustment process of the signal no longer relies solely on fixed rules, but combines the real-time changes of individualized nerve conduction velocity and oscillation frequency to ensure that the intervention pulse is highly synchronized with the action potential of the target neural cluster, thereby greatly improving the effect of the intervention, which are all extended implementation methods known to ordinary technicians in this field.

[0028] Example 2: See Figure 1 , Figure 2 and Figure 3 , Figure 1 This is the structural diagram of the dynamic pain neural decoding and intervention system based on the prefrontal temporal feature window of the present invention, showing the overall framework of the dynamic pain neural decoding and intervention system based on the prefrontal temporal feature window. The system consists of multiple functional modules. The first is the signal acquisition module, which obtains EEG signals in real time through the EEG device and separates signals of different frequency bands through the analog filter group. Specifically, the signal is divided into Frequency band signal (4-8Hz) and The output of the signal acquisition module is sent to the dynamic window control module, which includes Energy ratio calculation unit. This unit calculates Frequency band and The instantaneous energy ratio of the frequency band signal is calculated, and the signal analysis window is dynamically adjusted based on the energy ratio. When the energy ratio exceeds the preset threshold, the window adjustment unit will compress the analysis window to a shorter duration, usually 0.2 to 0.4 seconds, in order to accurately capture the transient characteristics of pain; otherwise, the analysis window will be extended to 0.8 to 1.6 seconds. After the dynamic window adjustment, the signal is passed to the phase prediction module. In this module, the main frequency of the prefrontal oscillation signal is first extracted by the frequency detection unit, and then the frequency is combined with the individualized nerve conduction velocity level to determine the appropriate phase compensation time through the delay mapping unit. This mechanism can accurately adjust the triggering timing of the stimulation pulse to ensure that the stimulation pulse is accurately synchronized with the rising edge of the action potential of the neural cluster. Then, the signal adjusted by the phase prediction module enters the intervention execution module, which is responsible for generating electrical stimulation pulses and adjusting the triggering timing of the stimulation pulse in advance according to the phase compensation parameters, so as to achieve precise neural intervention. Finally, the system manages and controls the entire process through an event-driven controller. When the energy ratio does not exceed the threshold, the signal acquisition and dynamic window control modules maintain operation in low power consumption mode; when the energy ratio exceeds the threshold, the controller activates the phase prediction module and the intervention execution module, enters the full power consumption mode, and restores the low power consumption state after the intervention. This not only improves the accuracy of capturing pain signals, but also maintains high real-time performance under low power consumption conditions, providing an efficient and low-power consumption solution for wearable devices.

[0029] Figure 2The comparison diagram of neural cluster stimulation triggering between the traditional method and the method of the present invention shows the comparison between the traditional method and the method of the present invention in neural cluster stimulation. In the traditional method, the stimulation signal is usually triggered by a fixed delay, and the delay time is relatively long, usually a pulse delay of 50ms. This fixed delay triggering method has a major defect and cannot accurately synchronize stimulation with neural activity, resulting in poor stimulation effect, especially in the accuracy and real-time performance of neural signal processing. In the technical solution of the present invention, a more precise and flexible control method is adopted for the stimulation signal triggering. Specifically, the present invention uses an advance compensation triggering technology, that is, through the real-time analysis of neural signals, the stimulation signal is compensated in advance. This method can accurately trigger electrical stimulation pulses when the deviation is less than ±π / 16 radians, ensuring that the stimulation pulse is completely synchronized with the action potential of the target neural cluster, thereby effectively improving the accuracy and efficiency of the intervention. And Figure 2 The neural cluster in the figure shows the application of the present invention at the neural group level. By comparing with the traditional method, it can be seen that the technical solution of the present invention has significantly improved the accuracy, timeliness and synchronization compared with the traditional method. This allows the present invention to not only solve the delay problem in the traditional method, but also significantly enhance the personalization and accuracy of neural regulation, ensuring the best effect of pain intervention.

[0030] Figure 3 The figure is a structural diagram of the dynamic window control and phase prediction module based on the Xilinx Zynq chip, which shows the structure and operation process of the dynamic window control module and phase prediction module based on the Xilinx Zynq chip. First, the clinical EEG device inputs the signal through the SPI / I2C interface and communicates with the SPI / I2C protocol. After the signal is input, it is passed to the dynamic window control module, which has a 5% resource configuration and an operating frequency of 50MHz. In this module, after the signal is processed, the corresponding event triggering mechanism is triggered according to the dynamic window control algorithm to achieve accurate capture and analysis of pain signals. After the signal is processed, it is output to the phase prediction module, which occupies 95% of the resources and runs at a working frequency of 200MHz. Through this module, the system can predict the activity phase of the neural cluster based on the real-time changes of the prefrontal signal, and perform phase compensation according to the individualized conduction velocity, thereby improving the synchronization and accuracy of the intervention. The figure also shows two working modes of the system: low power mode (voltage is 0.8V) and full power mode (voltage is 1.2V). In low power mode, the system optimizes the use of computing resources to ensure low power operation when signal processing is infrequent. In full power mode, when higher-precision signal processing is required, the system can ensure accurate signal intervention and processing by increasing voltage and resource allocation.

[0031] Embodiment 3: In this embodiment, the prefrontal EEG signal acquired by the signal acquisition module is separated into Frequency band 4-8Hz and After the frequency band 30-80Hz signal, the dynamic window control module Frequency band and The energy ratio of the frequency band is used to adjust the duration of the analysis window. In the optimization scheme, the energy ratio calculation unit Frequency band and The frequency band signal is integrated in a sliding time window to calculate the instantaneous energy ratio. Specifically, we define the energy ratio as: ,in, and Respectively Frequency band and The instantaneous energy of the frequency band signal in the current window. Each time the energy ratio is calculated, the size of the window is dynamically adjusted according to the change of the energy ratio: when the energy ratio exceeds the preset upper threshold (for example, If the energy ratio is lower than the set threshold, the window will be expanded to a longer duration (for example, 0.8 to 1.6 seconds) to accommodate longer neural signal analysis.

[0032] The purpose of the phase prediction module is to eliminate the timing mismatch problem caused by differences in nerve conduction velocity. To this end, this embodiment adopts a dual-factor mapping mechanism combining individualized nerve conduction velocity and oscillation frequency. Unlike the simple fixed delay compensation in traditional technology, this system can flexibly adjust the phase compensation time according to the current oscillation frequency and the individual nerve conduction velocity, ensuring that the stimulation pulse and the rising edge of the action potential of the target neural cluster are synchronized at the millisecond level. Specifically, the phase prediction module extracts the main frequency of the prefrontal oscillation signal through the frequency detection unit, and searches for the corresponding phase compensation time according to the following rules: frequency-delay comparison table: according to the nerve conduction velocity level (slow, medium, fast, ultrafast) and frequency, search for the corresponding phase compensation value. For example, when the oscillation frequency is 40Hz, the conduction velocity is "fast", and the phase compensation is 10ms; when the oscillation frequency is 60Hz, the phase compensation is 5ms. Through this mapping mechanism, the phase compensation can be adjusted according to the feedback after each intervention. In addition, after each electrical stimulation, the system will self-calibrate the phase deviation. If the actual deviation exceeds the set threshold (e.g., ±𝜋 / 8 radians), the system automatically adjusts the compensation time to ensure the accuracy of the next intervention.

[0033] In order to enable the system to achieve long-term stable operation in wearable devices, this embodiment is optimized in terms of power consumption control. Through the event-driven controller, when the energy ratio does not exceed the threshold, the system only starts the signal acquisition module and the dynamic window control module to maintain a low power consumption mode (for example, 5% resource occupancy, 50MHz main frequency). When the energy ratio exceeds the set threshold, the phase prediction module and the intervention execution module will be activated and switched to full power consumption mode (for example, 95% resource occupancy, 200MHz main frequency) to ensure the precise timing of the electrical stimulation pulse. During the window adjustment process, the system also uses a hardware-level dynamic resource allocation strategy to ensure real-time resource allocation during the signal analysis process, thereby minimizing power consumption. After the electrical stimulation is completed, the system will return to a low power consumption mode to achieve long-term monitoring and low-power operation, ensuring the comfort of patients in daily life and the wearability of the device. And considering patient safety, this embodiment introduces a safety control module for monitoring the number of continuous high-frequency stimulations. If the pain level does not decrease significantly after three consecutive stimulations, the system will automatically switch to a safe baseline intervention mode, in which the stimulation frequency is set to 40Hz and the intensity is 100μA. This measure can avoid overstimulation and prevent potential side effects. At the same time, the design of the signal acquisition module is compatible with existing medical equipment (such as Neuroscan, BioSemi, etc.), can be seamlessly connected, and receive raw EEG data from external devices through SPI or I2C protocol conversion units. Through this design, the system can quickly connect to existing medical facilities without the need for additional hardware modification, facilitating rapid deployment and application.

[0034] Embodiment 4: In this embodiment, the energy ratio It is through the frontal lobe EEG signals Frequency band 4-8Hz and The specific calculation process is as follows: Signal processing: The original EEG signal is separated into frequency bands by analog filter groups to obtain Frequency band signal and Frequency band signal ; Energy calculation: Full-wave rectification is performed on the separated signals respectively, and a first-order low-pass filter with a cutoff frequency of 2 Hz is used to smooth the rectified signals to obtain the energy of the smoothed signals; Energy ratio definition: The energy ratio is defined as: ,in, and Respectively Frequency band and The instantaneous energy of the frequency band signal in the current time window, in units of power (for example, in μW). It is used to dynamically adjust the duration of the signal analysis window. By calculating the energy ratio, the system can respond to the transient characteristics of the pain outbreak in real time. When the preset threshold is exceeded, the system will shorten the signal analysis window to ensure that the key features of the pain outbreak period are captured and avoid misjudgment of the traditional fixed window method.

[0035] The present invention solves the problem that the traditional system cannot cope with the transient changes of pain characteristics by dynamically adjusting the signal analysis window length. Specifically, after the signal acquisition module obtains the prefrontal EEG signal, a sliding time window is used to Frequency band and The energy ratio of the frequency band signal is calculated. When the set threshold is exceeded, the system will compress the signal analysis window from the standard 0.8 to 1.6 seconds to 0.2 to 0.4 seconds to accurately capture transient pain characteristics. When the value is below the threshold, the window will expand to accommodate longer neural signal analysis. Through this mechanism, the system can flexibly respond to pain outbreaks of different intensities and durations to ensure that no critical pain signals are missed. At the same time, this adjustment strategy eliminates the smoothing interference of high-frequency oscillation phase transitions in the fixed-duration window method, thereby effectively improving the capture rate of neural signals and significantly reducing the feature loss rate.

[0036] In the phase prediction module of the present invention, a dual-factor mapping mechanism of individualized nerve conduction velocity and oscillation frequency can be used to accurately synchronize the stimulation pulse with the action potential of the target neural cluster. This mechanism extracts the main frequency of the prefrontal EEG signal through the frequency detection unit, and combines the individual nerve conduction velocity level to find the corresponding phase compensation time from the pre-stored frequency-delay comparison table. Assuming the current oscillation frequency is Hz, according to the different conduction velocities (such as slow, medium, fast, and ultrafast), find the corresponding phase compensation time (Unit: milliseconds), where frequency is negatively correlated with delay time. The specific mapping rule is: when the oscillation frequency is Hz, and the conduction velocity grade is "fast", the compensation delay , when the oscillation frequency is Hz, and the conduction velocity grade is "slow", the compensation delay After each electrical stimulation, the system will automatically calibrate the phase compensation parameters to ensure the accurate triggering of the next stimulation pulse, thereby ensuring the synchronization and accuracy of neural intervention. radians, the system will set the step size according to the Adjust the compensation parameters.

[0037] The core advantage of the system of the present invention lies in its dual optimization of real-time performance and low power consumption. When the signal acquisition module and the dynamic window control module are working, the system runs in low-power mode, with a main frequency of only 50 MHz, consuming 5% of the total power. When the energy ratio exceeds the set threshold, the system will activate the phase prediction module and the intervention execution module, enter the full power mode, and increase the main frequency to 200 MHz to ensure the precise execution of pulse stimulation. Through this dynamic power consumption control mechanism, the system can minimize power consumption without affecting the intervention effect, meeting the requirements of wearable devices. In addition, the system manages the working status of each module through an event-driven controller to ensure that high-power modules are activated only at critical moments to avoid waste of resources caused by long-term high-power operation.

[0038] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0039] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A dynamic pain neural decoding and intervention system based on prefrontal temporal feature window, characterized in that: include: The signal acquisition module is used to obtain the frontal lobe EEG signals in real time through the EEG device and separate them using an analog filter bank. Frequency band signal and Frequency band signal, where The frequency band is 4 to 8 Hz, The frequency range is 30 to 80 Hz; A dynamic window control module is connected to the signal acquisition module and includes: an energy ratio calculation unit for calculating Frequency band signal and The frequency band signals are respectively integrated in a sliding time window to calculate the instantaneous energy ratio of the two; the window adjustment unit compresses the signal analysis window to a first preset time length when the instantaneous energy ratio exceeds a preset threshold, otherwise extends it to a second preset time length, wherein the preset threshold is 1.2 to 1.8 times the frequency band energy average, the first preset duration is 0.2-0.4 seconds, and the second preset duration is 0.8-1.6 seconds; A phase prediction module, connected to the dynamic window control module, includes: a frequency detection unit, used to extract the main frequency of the current prefrontal lobe oscillation signal; a delay mapping unit, which matches the corresponding phase compensation time parameter from a pre-stored frequency delay comparison table with different conduction velocity levels according to the main frequency and the individual nerve conduction velocity level, wherein the phase compensation time parameter is used to characterize the advance time amount of the stimulation pulse triggering moment relative to the rising edge of the action potential of the target neural cluster; An intervention execution module, connected to the phase prediction module, configured to trigger an electrical stimulation pulse in the dynamic window compression phase, and to adjust the triggering timing of the stimulation pulse in advance to the rising edge of the action potential of the target neural cluster based on the phase compensation time parameter; The event-driven controller connects the signal acquisition module, the dynamic window control module, the phase prediction module and the intervention execution module, and is configured as follows: when the energy ratio does not exceed the threshold, only the low-power operation of the signal acquisition module and the dynamic window control module is maintained; when the energy ratio exceeds the threshold, the phase prediction module and the intervention execution module are activated to the full-power mode, and the low-power state is restored after the window compression phase ends.

2. The dynamic pain neural decoding and intervention system based on the prefrontal temporal feature window according to claim 1, characterized in that: The energy ratio calculation unit is implemented in the following manner: Frequency band signal and The frequency band signals are full-wave rectified respectively; the rectified signals are smoothed by a first-order low-pass filter with a cutoff frequency of 2 Hz; the smoothed signals are calculated Frequency band signal energy and The ratio of signal energy in the frequency band.

3. The dynamic pain neural decoding and intervention system based on the prefrontal temporal feature window according to claim 1, characterized in that: The frequency delay comparison table is generated by the following rules: according to the big data of nerve conduction velocity of healthy people and chronic pain patients, the reference delay parameters corresponding to four different conduction velocity levels are preset, wherein the different conduction velocity levels include slow, medium, fast and ultra-fast; the reference delay parameter is negatively correlated with the current oscillation frequency, and the delay decreases by 1.5ms for every 10Hz increase in frequency; the parameters are automatically calibrated after each intervention: if the actual phase deviation exceeds If the angle is radian, adjust the corresponding parameter in the table with a step size of ±2ms.

4. The dynamic pain neural decoding and intervention system based on the prefrontal temporal feature window according to claim 1, characterized in that: The power consumption control of the event-driven controller includes: in low power consumption mode, the signal acquisition module and the dynamic window control module occupy 5% of the chip resources, and the main frequency is reduced to 50MHz; in full power consumption mode, the phase prediction module and the intervention execution module occupy 95% of the chip resources, and the main frequency is increased to 200MHz.

5. The dynamic pain neural decoding and intervention system based on the prefrontal temporal feature window according to claim 1, characterized in that: The stimulation parameters of the intervention execution module meet the following requirements: the stimulation frequency is consistent with the current prefrontal cortex. The deviation of the oscillation frequency did not exceed ±5%; the stimulation intensity was mapped to a current range of 50 to 500 μA according to the input value of the pain level assessment scale through fuzzy logic rules.

6. The dynamic pain neural decoding and intervention system based on the prefrontal temporal feature window according to claim 1, characterized in that: The dynamic window control module implements window adjustment through hardware logic, including: using the shift register group embedded in the FPGA to store the signal data in the current window; dynamically controlling the length of the shift register through a programmable counter to achieve continuous adjustment of the window length from 0.1 to 2 seconds.

7. The dynamic pain neural decoding and intervention system based on the prefrontal temporal feature window according to claim 1, characterized in that: The dynamic pain nerve decoding and intervention system also includes a safety control module, which is used to perform the following: monitoring the number of continuous high-frequency stimulations, and automatically switching to a baseline intervention mode if the pain level does not decrease after three stimulations. The stimulation frequency of the baseline intervention mode is 40 Hz and the stimulation intensity is 100 μA; when the amplitude of the EEG signal is detected to exceed 200 μV, the stimulation output is immediately cut off and an alarm is triggered.

8. The dynamic pain neural decoding and intervention system based on the prefrontal temporal feature window according to claim 1, characterized in that: The signal acquisition module is compatible with the digital interface of Neuroscan equipment and BioSemi equipment, including: an SPI protocol conversion unit or an I2C protocol conversion unit for receiving raw EEG data from external devices; a signal resampling unit for uniformly converting input signals into a 250Hz sampling rate.

9. The dynamic pain neural decoding and intervention system based on the prefrontal temporal feature window according to claim 1, characterized in that: The frequency delay comparison table contains the following mapping relationship: when the oscillation frequency is between 30 and 40 Hz and the conduction velocity level is medium, the corresponding compensation delay is 20 ms; when the oscillation frequency is between 40 and 60 Hz and the conduction velocity level is fast, the corresponding compensation delay is 15 ms; when the oscillation frequency is between 60 and 80 Hz and the conduction velocity level is ultra-fast, the corresponding compensation delay is 10 ms.

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