Neural regulation and control system based on alpha wave closed-loop flour noise stimulation

Abstract

The invention discloses a nerve regulation and control system for closed-loop pink noise stimulation based on alpha waves. The nerve regulation and control system comprises a real-time sleep monitoring module, a data quality detection module, an alpha wave intensity calculation module, an alpha wave rising edge detection module and a pink noise regulation and control module. The rising edge of the alpha waveform is monitored through a personalized morphological waveform monitoring algorithm, the Alpha wave activity of the brain is adjusted through closed-loop pink noise stimulation in the specific rising stage, meanwhile, the sound intensity of pink noise can be adjusted in a self-adaptive mode, accurate sound stimulation is provided, the sleeping experience of a user is improved, and the sleep experience of the user is improved. Compared with a traditional sleep aiding system, the sleep aiding system has the advantages that the sleep aiding system based on the Alpha waves not only can improve the speed of falling asleep, but also can improve the overall quality of sleep, and has wider applicability and practical significance.

Description

Technical Field

[0001] The present invention relates to the field of neural regulation technology, and in particular to a neural regulation system based on closed-loop pink noise stimulation of alpha waves. Background Art

[0002] In recent years, sleep problems have become a global public health challenge. According to statistics, about a quarter of the world's population suffers from sleep disorders. This phenomenon is particularly prominent in China, where about 38% of the population has sleep disorders. Among them, the sleep problems of office workers are more serious, accounting for as high as 65%. Sleep, especially the deep sleep stage at night, is crucial to human health and cognitive function. The duration and intensity of the slow-wave sleep (SWS) stage directly affects the body's self-repair and growth functions, and is also a critical period for memory consolidation. However, patients with sleep disorders usually have problems with short deep sleep time and poor sleep quality, which in turn affects their learning ability, concentration and emotional control ability.

[0003] There are many slow-wave sleep control systems on the market, but the effectiveness of these systems depends on whether the user can successfully enter deep sleep. If insomnia patients find it difficult to enter deep sleep, the control effects of these systems will be greatly reduced. Therefore, it is necessary to explore more effective sleep-aiding technologies to help these patients. To this end, this application proposes a neural control system based on closed-loop pink noise stimulation of alpha waves. Summary of the invention

[0004] Based on the technical problems existing in the background technology, the present invention proposes a neural regulation system based on closed-loop pink noise stimulation of alpha waves.

[0005] The present invention proposes a neural regulation system based on closed-loop pink noise stimulation of alpha waves, comprising a real-time sleeping monitoring module, a data quality detection module, an alpha wave intensity calculation module, an alpha wave rising edge detection module and a pink noise regulation module, wherein the real-time sleeping monitoring module, the data quality detection module, the alpha wave intensity calculation module, the alpha wave rising edge detection module and the pink noise regulation module are connected in sequence, and the real-time sleeping monitoring module, the data quality detection module, the alpha wave intensity calculation module, the alpha wave rising edge detection module and the pink noise regulation module are all built in an electroencephalogram monitoring device.

[0006] Preferably, the real-time sleep monitoring module analyzes and determines the collected EEG data at a cycle of 6 seconds, with the purpose of detecting whether the subject has entered a stable sleep stage. The detection at a cycle of 6 seconds can dynamically track the changes in the subject's sleep state;

[0007] The basis for determining the stable sleep stage is: the sleep state appears in the N2 stage for 15 consecutive times, which means it is in a stable sleep stage;

[0008] Use an EEG monitoring device to collect EEG data, and the EEG monitoring device includes but is not limited to a single-lead portable EEG monitoring device for the frontal lobe. The specific steps are as follows:

[0009] S101: First, the operator uses disinfectant wipes to clean the subject's forehead and remove the grease and makeup on the subject's head;

[0010] S102: Take out the brain patch device, correctly buckle the click patch on the EEG monitoring device, and stick it on the subject's forehead;

[0011] S103: Connect the adapter to the USB port of the computer to achieve Bluetooth connection between the brain patch device and the EEG monitoring device, so that EEG data can be collected through the brain patch device and the EEG monitoring device.

[0012] Preferably, the data quality detection module is used to evaluate the quality of the collected EEG data to ensure the accuracy of subsequent analysis. The data quality detection module is also used to identify and filter out artifact signals caused by external interference or equipment problems, thereby improving the credibility of the data. The operating logic steps of the data quality detection module are as follows:

[0013] S201: Calculate the average amplitude of the signal. When the average amplitude is lower than 0.5, it is determined that there is no data.

[0014] S202: Detect disconnection and dropout events, and evaluate according to the event labels printed by the embedded software. If there is no event, mark (5.0, 3); if there is a device disconnection, mark (5.1, 3); if there is a device dropout, mark (5.2, 3);

[0015] S203: Remove power frequency interference, calculate the FFT spectrum, find the maximum amplitude in the range of 45-52Hz and the corresponding center frequency. If the amplitude is greater than 2.0, it is determined that power frequency interference occurs and marked on the software end;

[0016] S204: extracting the EEG frequency band range and filtering it using a band-stop filter;

[0017] S205: Detect packet loss and calculate the packet loss rate. If the packet loss rate of a 6-second signal is greater than 50%, it is marked as severe packet loss.

[0018] S206: When the lost packet can be recovered, the data in the lost packet is supplemented;

[0019] S207: Detect the jump fluctuation of EEG data caused by motion artifacts, use cubic interpolation to construct the signal envelope, take 120μV as the threshold, delineate the area and calculate the proportion of the area where the artifact occurs. If it is >50%, mark it as severe fluctuation;

[0020] S208: If the fluctuation detected in step S207 can be removed, remove it by filtering through the defined area;

[0021] S209: Detect non-correlated low-frequency oscillations in EEG acquisition, calculate STFT time-frequency diagram, and calculate instantaneous frequency and standard deviation. After demarcating the area, calculate the proportion of the occurrence area. If >50%, mark it as severe oscillation;

[0022] S210: If the low-frequency oscillation detected in step S209 can be restored, the time-frequency coefficients of the abnormal area are set to zero, and an inverse transform is used to restore the normal signal.

[0023] Preferably, the alpha wave intensity calculation module is used to perform quantitative evaluation of the alpha wave intensity of the processed EEG data. The quantitative evaluation is mainly performed by calculating the proportion of the average power of the alpha wave in the time window to the entire EEG, so as to provide basic data support for the next step of regulation. The stimulation volume of the pink noise can be obtained by multiplying the average power proportion of the alpha wave by the amplification factor.

[0024] Preferably, when the alpha wave rising edge detection module is started, it focuses on capturing the rising edge signal of the alpha wave. When the alpha wave rising edge that meets the preset conditions is detected, the pink noise control module will accurately apply pink noise stimulation near the top of the rising edge to promote the sleeping process. After the alpha wave rising edge is detected and the pink noise stimulation is applied, two consecutive stimulations are required, and a 2.5-second cooling cycle is entered after each stimulation to prevent excessive stimulation from interfering with the sleeping process.

[0025] Preferably, the specific logical steps of the cooperation between the alpha wave rising edge detection module and the pink noise control module are as follows:

[0026] S301: Selecting and setting parameters of sliding window: Selecting a suitable sliding window for signal analysis according to the time resolution and frequency characteristics of the signal, and determining the window function and window length range of the sliding window to ensure that the slight changes of the alpha wave can be accurately captured. The type and length of the window function directly affect the smoothness and edge effect of the signal, so the spectral characteristics and calculation efficiency of the signal need to be comprehensively considered when selecting;

[0027] S302: Step size selection: After setting the sliding window, determine the step size of the sliding window movement, that is, the time interval between each sliding window movement. The size of the step size determines the level of detail of the signal analysis. A smaller step size can provide a higher time resolution, thereby more precisely capturing the rising edge characteristics of the alpha wave, but it will also increase the complexity of the calculation;

[0028] S303: Signal monotonicity and zero point detection: within the sliding window range, the signal is analyzed to determine its monotonicity and zero point characteristics, whether the signal shows a monotonically increasing trend within the sliding window, and whether the signal has only one zero crossing point. The signal part that meets these two conditions will be determined as the rising edge of the alpha wave. In this signal frame, attention should be paid to the interference of noise;

[0029] S304: Detection and time recording of rising edge: Once a rising edge that meets the conditions is successfully detected within the sliding window, the system will output the detection result and accurately record the time point when the detected rising edge occurs;

[0030] S305: The pink noise control module accurately applies pink noise stimulation twice near the top of the rising edge to promote the sleeping process.

[0031] Preferably, in S303, the basis for judging the monotonicity and zero-point characteristics of the signal is as follows:

[0032] (1) For the signal sequence x[1], x[2], …, x[n], check whether for each i, x[i] ≤ x[i+1], if all conditions are met, then the signal is monotonically increasing;

[0033] (2) Traverse the signal sequence, find the first positive value position, and check whether there is a negative value before this position. When there is only one transition between a negative value and a positive value, the signal has only one zero crossing point.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention monitors the rising edge of the alpha waveform through a personalized morphological waveform monitoring algorithm, and regulates the brain's Alpha wave activity through closed-loop pink noise stimulation at a specific rising stage. At the same time, it can adaptively adjust the sound intensity of the pink noise to provide precise sound stimulation, thereby improving the user's sleeping experience, helping the user to gradually enter a calm state from a wakeful state, and laying the foundation for subsequent deep sleep. Compared with traditional sleep aid systems, the regulation technology based on Alpha waves can not only improve the speed of falling asleep, but also improve the overall quality of sleep, and has wider applicability and practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1A block diagram of a neural regulation system based on closed-loop pink noise stimulation of alpha waves proposed by the present invention;

[0037] Figure 2 A flowchart of a neural regulation system based on closed-loop pink noise stimulation of alpha waves proposed by the present invention;

[0038] Figure 3 This is an EEG waveform diagram after sound regulation of a neural regulation system based on closed-loop pink noise stimulation of alpha waves proposed by the present invention. DETAILED DESCRIPTION

[0039] The present invention will be further explained below in conjunction with specific embodiments.

[0040] Example

[0041] Reference Figure 1-3 , this embodiment proposes a neural regulation system based on closed-loop pink noise stimulation of alpha waves, including a real-time sleep monitoring module, a data quality detection module, an alpha wave intensity calculation module, an alpha wave rising edge detection module and a pink noise regulation module, the real-time sleep monitoring module, the data quality detection module, the alpha wave intensity calculation module, the alpha wave rising edge detection module and the pink noise regulation module are connected in sequence, the real-time sleep monitoring module, the data quality detection module, the alpha wave intensity calculation module, the alpha wave rising edge detection module and the pink noise regulation module are all built in an electroencephalogram monitoring device, wherein the electroencephalogram monitoring device includes but is not limited to a single-lead portable electroencephalogram monitoring device for the prefrontal lobe;

[0042] The real-time sleep monitoring module analyzes and determines the collected EEG data every 6 seconds to detect whether the subject has entered a stable sleep stage. The detection every 6 seconds can dynamically track the changes in the subject's sleep state.

[0043] The basis for determining the stable sleep stage is: the sleep state appears in the N2 stage for 15 consecutive times, which means it is in a stable sleep stage;

[0044] The specific steps for collecting EEG data using EEG monitoring equipment are as follows:

[0045] S101: First, the operator uses disinfectant wipes to clean the subject's forehead and remove the grease and makeup on the subject's head;

[0046] S102: Take out the brain patch device, correctly buckle the click patch on the EEG monitoring device, and stick it on the subject's forehead;

[0047] S103: Connect the adapter to the USB port of the computer to achieve Bluetooth connection between the brain patch device and the EEG monitoring device, so that EEG data can be collected through the brain patch device and the EEG monitoring device;

[0048] The data quality detection module is used to evaluate the quality of the collected EEG data to ensure the accuracy of subsequent analysis. The data quality detection module is also used to identify and filter out artifact signals caused by external interference or equipment problems, thereby improving the credibility of the data. The operating logic steps of the data quality detection module are as follows:

[0049] S201: Calculate the average amplitude of the signal. When the average amplitude is lower than 0.5, it is determined that there is no data.

[0050] S202: Detect disconnection and dropout events, and evaluate according to the event labels printed by the embedded software. If there is no event, mark (5.0, 3); if there is a device disconnection, mark (5.1, 3); if there is a device dropout, mark (5.2, 3);

[0051] S203: Remove power frequency interference, calculate the FFT spectrum, find the maximum amplitude in the range of 45-52Hz and the corresponding center frequency. If the amplitude is greater than 2.0, it is determined that power frequency interference occurs and marked on the software end;

[0052] S204: extracting the EEG frequency band range and filtering it using a band-stop filter;

[0053] S205: Detect packet loss and calculate the packet loss rate. If the packet loss rate of a 6-second signal is greater than 50%, it is marked as severe packet loss.

[0054] S206: When the lost packet can be recovered, the data in the lost packet is supplemented;

[0055] S207: Detect the jump fluctuation of EEG data caused by motion artifacts, use cubic interpolation to construct the signal envelope, take 120μV as the threshold, delineate the area and calculate the proportion of the area where the artifact occurs. If it is >50%, mark it as severe fluctuation;

[0056] S208: If the fluctuation detected in step S207 can be removed, remove it by filtering through the defined area;

[0057] S209: Detect non-correlated low-frequency oscillations in EEG acquisition, calculate STFT time-frequency diagram, and calculate instantaneous frequency and standard deviation. After demarcating the area, calculate the proportion of the occurrence area. If >50%, mark it as severe oscillation;

[0058] S210: If the low-frequency oscillation detected in step S209 can be restored, the time-frequency coefficients of the abnormal area are set to zero, and an inverse transform is used to restore the normal signal;

[0059] The alpha wave intensity calculation module is used to quantitatively evaluate the alpha wave intensity of the processed EEG data. The quantitative evaluation is mainly performed by calculating the average power of the alpha wave in the time window as a percentage of the entire EEG, providing basic data support for the next step of regulation. The stimulation volume of the pink noise is obtained by multiplying the average power percentage of the alpha wave by the amplification factor.

[0060] When the alpha wave rising edge detection module is started, it focuses on capturing the rising edge signal of the alpha wave. When the alpha wave rising edge that meets the preset conditions is detected, the pink noise control module will accurately apply pink noise stimulation near the top of the rising edge to promote the sleeping process. After the alpha wave rising edge is detected and the pink noise stimulation is applied, two consecutive stimulations are required. After each stimulation, a cooling cycle of 2.5 seconds is entered to prevent excessive stimulation from interfering with the sleeping process. The specific logical steps are as follows:

[0061] S301: Selecting and setting parameters of sliding window: Selecting a suitable sliding window for signal analysis according to the time resolution and frequency characteristics of the signal, and determining the window function and window length range of the sliding window to ensure that the slight changes of the alpha wave can be accurately captured. The type and length of the window function directly affect the smoothness and edge effect of the signal, so the spectral characteristics and calculation efficiency of the signal need to be comprehensively considered when selecting;

[0062] S302: Step size selection: After setting the sliding window, determine the step size of the sliding window movement, that is, the time interval between each sliding window movement. The size of the step size determines the level of detail of the signal analysis. A smaller step size can provide a higher time resolution, thereby more precisely capturing the rising edge characteristics of the alpha wave, but it will also increase the complexity of the calculation;

[0063] S303: Signal monotonicity and zero point detection: within the sliding window range, the signal is analyzed to determine its monotonicity and zero point characteristics, whether the signal shows a monotonically increasing trend within the sliding window, and whether the signal has only one zero crossing point. The signal part that meets these two conditions will be determined as the rising edge of the alpha wave. In this signal frame, attention should be paid to the interference of noise;

[0064] The basis for judging the monotonicity and zero-point characteristics of the signal is as follows:

[0065] (1) For the signal sequence x[1], x[2], …, x[n], check whether for each i, x[i] ≤ x[i+1], if all conditions are met, then the signal is monotonically increasing;

[0066] (2) Traverse the signal sequence, find the first positive value position, and check whether there is a negative value before this position. When there is only one transition between a negative value and a positive value, the signal has only one zero crossing point;

[0067] S304: Detection and time recording of rising edge: Once a rising edge that meets the conditions is successfully detected within the sliding window, the system will output the detection result and accurately record the time point when the detected rising edge occurs;

[0068] S305: the pink noise control module accurately applies pink noise stimulation twice near the top of the rising edge to promote the sleeping process;

[0069] The pink noise stimulation strategy uses high-frequency noise similar to spindle waves, which lasts for 0.5 seconds. When the regulation starts, once a significant alpha wave rising edge is detected, the system will immediately trigger a pink noise stimulation. In each regulation, the system applies two pink noise stimulations in succession, and enters a 2.5-second cooling period after two stimulations, and then repeats the stimulation process. The interval between two consecutive stimulations is automatically adjusted according to the frequency of the alpha wave (usually 8-13Hz) to ensure that the noise stimulation is highly synchronized with the natural rhythm of the alpha wave, thereby effectively enhancing the effect of neural regulation. This stimulation strategy not only ensures the regulation effect, but also takes into account the user's comfort and the system's operating efficiency;

[0070] In addition, Alpha waves are a type of brain wave that is closely related to relaxation and inner peace. When you close your eyes to rest, meditate, or relax slightly, the brain will produce significant Alpha wave activity. The increase in Alpha waves usually means that the individual gradually transitions from a highly alert state to a relaxed state. Therefore, it is regarded as a bridge from wakefulness to sleep. The benefit of regulating Alpha waves is that it can help individuals enter a calm state of mind and reduce anxiety, thereby creating favorable conditions for falling asleep.

[0071] This embodiment monitors the rising edge of the alpha waveform through a personalized morphological waveform monitoring algorithm, and regulates the brain's Alpha wave activity through closed-loop pink noise stimulation at a specific rising stage. At the same time, it can adaptively adjust the sound intensity of the pink noise to provide precise sound stimulation, thereby improving the user's sleeping experience, helping the user to gradually enter a calm state from a wakeful state, and laying the foundation for subsequent deep sleep. Compared with traditional sleep aid systems, the regulation technology based on Alpha waves can not only improve the speed of falling asleep, but also improve the overall quality of sleep, and has wider applicability and practical significance.

[0072] It should be noted that: Figure 3The middle dotted line represents the rising edge moment of the record, the solid line B represents the alpha waveform after regulation, the solid line A represents the alpha waveform before regulation, and the gray area at the bottom represents the alpha wave intensity reaching significance at this time.

[0073] In this embodiment, when in use, first open the monitoring software in the EEG monitoring device, the operator uses a disinfectant wipe to help the subject clean the forehead, remove the grease and cosmetics on the subject's head, take out the brain patch device, correctly buckle the click patch on the EEG monitoring device, and stick it on the forehead of the subject, connect the adapter to the computer USB interface, and realize the Bluetooth connection between the brain patch device and the EEG monitoring device. Then, the EEG data can be collected through the brain patch device in cooperation with the EEG monitoring device. The real-time sleep monitoring module analyzes and determines the collected EEG data in a cycle of every 6 seconds to detect whether the subject has entered a stable sleep stage. If so, it ends directly. When the real-time sleep monitoring module determines that the subject is not in a stable sleep state, the data quality detection module is immediately started to evaluate the quality of the current EEG data to ensure the accuracy of subsequent analysis. After passing the data quality detection, the system will enter the alpha wave intensity calculation link. The alpha wave intensity calculation module calculates the alpha wave intensity within the time window. The average power of the ha wave accounts for the proportion of the entire EEG, and the processed EEG data is used to quantitatively evaluate the intensity of the alpha wave. According to the average power proportion of the alpha wave multiplied by the amplification factor, the system will obtain the stimulation volume of the pink noise, and at the same time, the alpha wave rising edge detection module is started, focusing on capturing the rising edge signal of the alpha wave. When the alpha wave rising edge that meets the preset conditions is detected, the pink noise control module will accurately apply pink noise stimulation near the top of the rising edge to promote the sleeping process. After applying the pink noise stimulation, it will perform 2 consecutive stimulations. After each stimulation, the system enters a 2.5-second cooling cycle to prevent excessive stimulation from interfering with the sleeping process. The purpose of neural regulation of the alpha waveform is achieved through pink noise stimulation, helping users to gradually enter a calm state from an awake state, and through alpha wave intensity calculation and real-time monitoring, the sound intensity of the pink noise can be adaptively adjusted to provide precise sound stimulation, thereby enhancing the user's sleeping experience.

[0074] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A neural control system based on closed-loop pink noise stimulation of alpha waves, characterized in that: It includes a real-time sleep monitoring module, a data quality detection module, an alpha wave intensity calculation module, an alpha wave rising edge detection module and a pink noise control module, which are connected in sequence, and the real-time sleep monitoring module, the data quality detection module, the alpha wave intensity calculation module, the alpha wave rising edge detection module and the pink noise control module are all built into the EEG monitoring device.

2. A neural regulation system based on closed-loop pink noise stimulation of alpha waves according to claim 1, characterized in that: The real-time sleep monitoring module analyzes and determines the collected EEG data every 6 seconds, with the purpose of detecting whether the subject has entered a stable sleep stage. The detection every 6 seconds can dynamically track the changes in the subject's sleep state; The basis for determining the stable sleep stage is: the sleep state appears in the N2 stage for 15 consecutive times, which means it is in a stable sleep stage; Use an EEG monitoring device to collect EEG data, and the EEG monitoring device includes but is not limited to a single-lead portable EEG monitoring device for the frontal lobe. The specific steps are as follows: S101: First, the operator uses disinfectant wipes to clean the subject's forehead and remove the grease and makeup on the subject's head; S102: Take out the brain patch device, correctly buckle the click patch on the EEG monitoring device, and stick it on the subject's forehead; S103: Connect the adapter to the USB port of the computer to achieve Bluetooth connection between the brain patch device and the EEG monitoring device, so that EEG data can be collected through the brain patch device and the EEG monitoring device.

3. The neural regulation system based on closed-loop pink noise stimulation of alpha waves according to claim 1, characterized in that: The data quality detection module is used to evaluate the quality of the collected EEG data to ensure the accuracy of subsequent analysis. The data quality detection module is also used to identify and filter out artifact signals caused by external interference or equipment problems, thereby improving the credibility of the data. The operating logic steps of the data quality detection module are as follows: S201: Calculate the average amplitude of the signal. When the average amplitude is lower than 0.5, it is determined that there is no data. S202: Detect disconnection and dropout events, and evaluate according to the event labels printed by the embedded software. If there is no event, mark (5.0, 3); if there is a device disconnection, mark (5.1, 3); if there is a device dropout, mark (5.2, 3); S203: Remove power frequency interference, calculate the FFT spectrum, find the maximum amplitude in the range of 45-52Hz and the corresponding center frequency. If the amplitude is greater than 2.0, it is determined that power frequency interference occurs and marked on the software end; S204: extracting the EEG frequency band range and filtering it using a band-stop filter; S205: Detect packet loss and calculate the packet loss rate. If the packet loss rate of a 6-second signal is greater than 50%, it is marked as severe packet loss. S206: When the lost packet can be recovered, the data in the lost packet is supplemented; S207: Detect the jump fluctuation of EEG data caused by motion artifacts, use cubic interpolation to construct the signal envelope, take 120μV as the threshold, delineate the area and calculate the proportion of the area where the artifact occurs. If it is >50%, mark it as severe fluctuation; S208: If the fluctuation detected in step S207 can be removed, remove it by filtering through the defined area; S209: Detect non-correlated low-frequency oscillations in EEG acquisition, calculate STFT time-frequency diagram, and calculate instantaneous frequency and standard deviation. After demarcating the area, calculate the proportion of the occurrence area. If >50%, mark it as severe oscillation; S210: If the low-frequency oscillation detected in step S209 can be restored, the time-frequency coefficients of the abnormal area are set to zero, and an inverse transform is used to restore the normal signal.

4. The neural regulation system based on closed-loop pink noise stimulation of alpha waves according to claim 1, characterized in that: The alpha wave intensity calculation module is used to perform quantitative evaluation of the alpha wave intensity of the processed EEG data. The quantitative evaluation is mainly performed by calculating the proportion of the average power of the alpha wave in the time window to the entire EEG, providing basic data support for the next step of regulation. The stimulation volume of the pink noise is obtained by multiplying the average power proportion of the alpha wave by the amplification factor.

5. The neural regulation system based on closed-loop pink noise stimulation of alpha waves according to claim 1, characterized in that: When the alpha wave rising edge detection module is started, it focuses on capturing the rising edge signal of the alpha wave. When the alpha wave rising edge that meets the preset conditions is detected, the pink noise control module accurately applies pink noise stimulation near the top of the rising edge to promote the sleeping process. After the alpha wave rising edge is detected and the pink noise stimulation is applied, two consecutive stimulations are required. After each stimulation, a cooling cycle of 2.5 seconds is entered to prevent excessive stimulation from interfering with the sleeping process.

6. A neural regulation system based on closed-loop pink noise stimulation of alpha waves according to claim 5, characterized in that: The specific logical steps of the cooperation between the alpha wave rising edge detection module and the pink noise control module are as follows: S301: Selecting a sliding window and setting parameters: selecting a suitable sliding window for signal analysis according to the time resolution and frequency characteristics of the signal, and determining the window function and window length range of the sliding window to ensure that the slight changes of the alpha wave can be accurately captured; S302: Step size selection: after setting the sliding window, determine the step size of the sliding window movement, that is, the time interval between each sliding window movement; S303: Signal monotonicity and zero point detection: within the sliding window range, the signal is analyzed to determine its monotonicity and zero point characteristics, and whether the signal presents a monotonically increasing trend within the sliding window is detected, and whether the signal has only one zero crossing point. The signal part that meets these two conditions will be determined as the rising edge of the alpha wave; S304: Detection and time recording of rising edge: Once a rising edge that meets the conditions is successfully detected within the sliding window, the system will output the detection result and accurately record the time point when the detected rising edge occurs; S305: The pink noise control module accurately applies pink noise stimulation twice near the top of the rising edge to promote the sleeping process.

7. A neural regulation system based on closed-loop pink noise stimulation of alpha waves according to claim 6, characterized in that: In S303, the basis for judging the monotonicity and zero-point characteristics of the signal is as follows: (1) For the signal sequence x[1], x[2], …, x[n], check whether for each i, x[i] ≤ x[i+1], if all conditions are met, then the signal is monotonically increasing; (2) Traverse the signal sequence, find the first positive value position, and check whether there is a negative value before this position. When there is only one transition between a negative value and a positive value, the signal has only one zero crossing point.

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