A neural modulation system based on closed-loop alpha wave-based pink noise stimulation

Abstract

The application discloses a kind of neuroregulation systems based on alpha wave closed loop pink noise stimulation, including real-time sleep monitoring module, data quality detection module, alpha wave intensity calculation module, alpha wave rising edge detection module and pink noise control module.The application monitors the rising edge of alpha waveform by personalized morphological waveform monitoring algorithm, adjusts the Alpha wave activity of brain by closed loop pink noise stimulation in specific rising stage, can also adaptively adjust the sound intensity of pink noise, provides accurate sound stimulation, to improve the sleep experience of user, help user gradually lead into calm state from wakefulness, lay the foundation for subsequent deep sleep, compared with traditional sleep aid system, based on Alpha wave control technology can not only improve sleep speed, but also improve the overall quality of sleep, with more extensive applicability and practical significance.

Description

Technical Field

[0001] This invention relates to the field of neuromodulation technology, and in particular to a neuromodulation system based on closed-loop powder noise stimulation using alpha waves. Background Technology

[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. In China, this phenomenon is particularly prominent, with about 38% of the population experiencing sleep disorders. Among them, the sleep problems of office workers are even more severe, with a proportion 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 affect the body's self-repair and growth functions, and it 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.

[0003] There are already various control systems for slow-wave sleep on the market, but the effectiveness of these systems depends on whether the user can successfully enter deep sleep. If insomnia patients have difficulty entering deep sleep, the control effect of these systems will be greatly reduced. Therefore, it is necessary to explore more effective sleep aid technologies to help these patients. To this end, this application proposes a neuromodulation system based on closed-loop powder 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 modulation system based on closed-loop powder noise stimulation of alpha waves.

[0005] This invention proposes a neuromodulation system based on closed-loop pink noise stimulation of alpha waves, comprising 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 modulation module. The real-time sleep monitoring module, data quality detection module, alpha wave intensity calculation module, alpha wave rising edge detection module, and pink noise modulation module are connected in sequence and are all built into an EEG monitoring device.

[0006] Preferably, the real-time sleep monitoring module analyzes and judges 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 changes in the subject's sleep state.

[0007] The criteria for determining a stable sleep stage are: a sleep state consisting of 15 consecutive N2 stages, which indicates a stable sleep stage.

[0008] EEG data is collected using EEG monitoring devices, including but not limited to portable prefrontal cortex single-lead EEG monitoring devices. The specific steps are as follows:

[0009] S101: First, the operator uses disinfectant wipes to clean the subject's forehead to remove oil and cosmetics from the subject's head;

[0010] S102: Remove the brain patch device, correctly attach the click patch to the EEG monitoring device, and place it on the subject's forehead;

[0011] S103: Connect the adapter to the computer's USB port to enable Bluetooth connection between the brain patch and the EEG monitoring device, allowing the brain patch to work with the EEG monitoring device to collect EEG data.

[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 reliability 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. If the average amplitude is less than 0.5, it is determined that there is no data.

[0014] S202: Detect disconnection and detachment events, and evaluate them based on the event labels printed by the embedded software. If there is no event, label it (5.0, 3); if the device disconnects, label it (5.1, 3); if the device detaches, label it (5.2, 3).

[0015] S203: Remove power frequency interference. Calculate the FFT spectrum, find the maximum amplitude in the 45-52Hz range, and the corresponding center frequency. If the amplitude is >2.0, it is determined that power frequency interference has occurred and it is marked in the software.

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

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

[0018] S206: If packet loss can be recovered, the data in the lost packet will be supplemented;

[0019] S207: To detect the jump fluctuations in EEG data caused by motion artifacts, a cubic interpolation method is used to construct the signal envelope. After delineating the region with a threshold of 120μV, the proportion of the region where the fluctuation occurs is calculated. If it is >50%, it is marked as a severe fluctuation.

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

[0021] S209: Detect unrelated low-frequency oscillations in EEG acquisition, calculate the STFT time-frequency plot, and calculate the instantaneous frequency and standard deviation. After delineating the region, calculate the proportion of the region where the oscillation occurs. If it is >50%, it is marked as a severe oscillation.

[0022] S210: If the low-frequency oscillation detected in step S209 can be recovered, set the time-frequency coefficient of the abnormal region to zero and use inverse transformation to recover the normal signal.

[0023] Preferably, the alpha wave intensity calculation module is used to quantitatively evaluate the alpha wave intensity of the processed EEG data. The quantitative evaluation mainly involves calculating the proportion of the average power of the alpha wave in the entire EEG within the time window, providing basic data support for the next step of regulation. The stimulation volume of the pink noise is obtained by multiplying the proportion of the average power of the alpha wave by the amplification factor.

[0024] Preferably, when the alpha wave rising edge detection module is activated, it focuses on capturing the rising edge signal of the alpha wave. When an alpha wave rising edge that meets the preset conditions is detected, the pink noise control module will precisely apply pink noise stimulation near the top of the rising edge to promote the sleep process. After detecting the alpha wave rising edge and applying pink noise stimulation, two stimulations are required in succession. After each stimulation, a 2.5-second cooling cycle is entered to prevent overstimulation from interfering with the sleep process.

[0025] Preferably, the specific logical steps for the coordinated operation of the alpha wave rising edge detection module and the powder noise control module are as follows:

[0026] S301: Select and set parameters for the sliding window: Select a suitable sliding window for signal analysis based on the signal's time resolution and frequency characteristics, and determine the window function and window length range of the sliding window to ensure that the subtle changes of the alpha wave can be accurately captured. The type and length of the window function directly affect the smoothness and edge effects of the signal, so the spectral characteristics and computational efficiency of the signal must be considered when selecting it.

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

[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. The signal is detected to show a monotonically increasing trend within the sliding window, and it is also confirmed whether the signal has only one zero-crossing point. The signal portion that meets these two conditions will be determined as the rising edge of the alpha wave. Noise interference needs to be noted within this signal frame.

[0029] S304: Rising Edge Detection and Time Recording: 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 occurred.

[0030] S305: The pink noise control module precisely applies two pink noise stimuli near the tip of the rising edge to promote the sleep process.

[0031] Preferably, in step S303, the criteria for determining the signal monotonicity and zero-point characteristics are 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, check if there is a negative value before this position. If there is only one transition between a negative value and a positive value, then the signal has only one zero crossing point.

[0034] Compared with existing technologies, the beneficial effects of this invention are:

[0035] This invention monitors the rising edge of the alpha waveform using a personalized morphological waveform monitoring algorithm. During a specific rising phase, it modulates the brain's alpha wave activity through closed-loop pink noise stimulation. Simultaneously, it adaptively adjusts the sound intensity of the pink noise to provide precise sound stimulation, thereby enhancing the user's sleep experience and helping them gradually transition from a waking state to a calm state, laying the foundation for subsequent deep sleep. Compared with traditional sleep aid systems, the alpha wave-based modulation technology not only improves the speed of falling asleep but also enhances the overall quality of sleep, making it more widely applicable and practically significant. Attached Figure Description

[0036] Figure 1This is a block diagram of a neuromodulation system based on closed-loop powder noise stimulation using alpha waves, as proposed in this invention.

[0037] Figure 2 The flowchart is a diagram of a neuromodulation system based on closed-loop powder noise stimulation using alpha waves, as proposed in this invention.

[0038] Figure 3 This is an electroencephalogram (EEG) of a closed-loop powder noise stimulation neuromodulation system based on alpha waves proposed in this invention, after sound modulation. Detailed Implementation

[0039] The present invention will be further explained below with reference to specific embodiments.

[0040] Example

[0041] Reference Figure 1-3 This embodiment proposes a neuromodulation 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 modulation module. The real-time sleep monitoring module, data quality detection module, alpha wave intensity calculation module, alpha wave rising edge detection module, and pink noise modulation module are connected in sequence. The real-time sleep monitoring module, data quality detection module, alpha wave intensity calculation module, alpha wave rising edge detection module, and pink noise modulation module are all built into an EEG monitoring device, wherein the EEG monitoring device includes, but is not limited to, a prefrontal single-lead portable EEG monitoring device.

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

[0043] The criteria for determining a stable sleep stage are: a sleep state consisting of 15 consecutive N2 stages, which indicates 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 to remove oil and cosmetics from the subject's head;

[0046] S102: Remove the brain patch device, correctly attach the click patch to the EEG monitoring device, and place it on the subject's forehead;

[0047] S103: Connect the adapter to the computer's USB port to enable Bluetooth connection between the brain patch and the EEG monitoring device, allowing the brain patch to collect EEG data in conjunction with the EEG monitoring device.

[0048] The data quality inspection module is used to evaluate the quality of the collected EEG data to ensure the accuracy of subsequent analysis. It also identifies and filters out artifacts caused by external interference or equipment malfunctions, thereby improving data reliability. The operational logic steps of the data quality inspection module are as follows:

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

[0050] S202: Detect disconnection and detachment events, and evaluate them based on the event labels printed by the embedded software. If there is no event, label it (5.0, 3); if the device disconnects, label it (5.1, 3); if the device detaches, label it (5.2, 3).

[0051] S203: Remove power frequency interference. Calculate the FFT spectrum, find the maximum amplitude in the 45-52Hz range, and the corresponding center frequency. If the amplitude is >2.0, it is determined that power frequency interference has occurred and it is marked in the software.

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

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

[0054] S206: If packet loss can be recovered, the data in the lost packet will be supplemented;

[0055] S207: To detect the jump fluctuations in EEG data caused by motion artifacts, a cubic interpolation method is used to construct the signal envelope. After delineating the region with a threshold of 120μV, the proportion of the region where the fluctuation occurs is calculated. If it is >50%, it is marked as a severe fluctuation.

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

[0057] S209: Detect unrelated low-frequency oscillations in EEG acquisition, calculate the STFT time-frequency plot, and calculate the instantaneous frequency and standard deviation. After delineating the region, calculate the proportion of the region where the oscillation occurs. If it is >50%, it is marked as a severe oscillation.

[0058] S210: If the low-frequency oscillation detected in step S209 can be recovered, set the time-frequency coefficient of the abnormal region to zero and use inverse transformation to recover 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 mainly involves calculating the proportion of the average power of the alpha wave in the entire EEG within the time window, providing basic data support for the next step of modulation. The stimulation volume of the pink noise is obtained by multiplying the proportion of the average power of the alpha wave by the amplification factor.

[0060] When the alpha wave rising edge detection module is activated, it focuses on capturing the rising edge signal of the alpha wave. When an alpha wave rising edge that meets preset conditions is detected, the pink noise modulation module precisely applies pink noise stimulation near the tip of the rising edge to promote the sleep process. After detecting the alpha wave rising edge and applying pink noise stimulation, two consecutive stimulations are required, followed by a 2.5-second cooling cycle after each stimulation to prevent overstimulation from interfering with the sleep process. The specific logic steps are as follows:

[0061] S301: Select and set parameters for the sliding window: Select a suitable sliding window for signal analysis based on the signal's time resolution and frequency characteristics, and determine the window function and window length range of the sliding window to ensure that the subtle changes of the alpha wave can be accurately captured. The type and length of the window function directly affect the smoothness and edge effects of the signal, so the spectral characteristics and computational efficiency of the signal must be considered when selecting it.

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

[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. The signal is detected to show a monotonically increasing trend within the sliding window, and it is also confirmed whether the signal has only one zero-crossing point. The signal portion that meets these two conditions will be determined as the rising edge of the alpha wave. Noise interference needs to be noted within this signal frame.

[0064] The criteria for determining the monotonicity and zero-point characteristics of a signal are 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, check if there is a negative value before this position. If there is only one transition between a negative value and a positive value, then the signal has only one zero crossing point.

[0067] S304: Rising Edge Detection and Time Recording: 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 occurred.

[0068] S305: The pink noise control module precisely applies two pink noise stimuli near the tip of the rising edge to promote the sleep process;

[0069] The pink noise stimulation strategy uses high-frequency noise similar to a spindle wave for 0.5 seconds. When modulation begins, the system immediately triggers a pink noise stimulus once a significant rising edge of the alpha wave is detected. During each modulation, the system applies two pink noise stimuli consecutively, followed by a 2.5-second cool-down period. This stimulation process is then repeated. The interval between two consecutive stimuli is automatically adjusted according to the frequency of the alpha wave (usually 8-13Hz) to ensure that the noise stimulus is highly synchronized with the natural rhythm of the alpha wave, thereby effectively enhancing the effect of neural modulation. This stimulation strategy ensures the modulation effect while taking into account user comfort and system operating efficiency.

[0070] In addition, alpha waves are a type of brainwave closely associated with relaxation and inner peace. When resting with eyes closed, meditating, or in a state of mild relaxation, the brain produces significant alpha wave activity. An increase in alpha waves usually means that an individual is gradually transitioning from a state of high alertness to a state of relaxation. 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, reduce anxiety, and thus create favorable conditions for falling asleep.

[0071] This embodiment monitors the rising edge of the alpha waveform using a personalized morphological waveform monitoring algorithm. During a specific rising phase, it modulates the brain's alpha wave activity through closed-loop pink noise stimulation. Simultaneously, it can adaptively adjust the sound intensity of the pink noise to provide precise sound stimulation, thereby enhancing the user's sleep experience and helping the user gradually transition from a waking state to a calm state, laying the foundation for subsequent deep sleep. Compared with traditional sleep aid systems, alpha wave-based modulation technology can not only improve the speed of falling asleep but also enhance the overall quality of sleep, making it more widely applicable and practically significant.

[0072] It should be noted that: Figure 3The dashed line represents the rising edge of the recorded time, solid line B represents the modulated alpha waveform, solid line A represents the unmodulated alpha waveform, and the gray area at the bottom represents the significant alpha wave intensity at this time.

[0073] In this embodiment, during use, the monitoring software in the EEG monitoring device is first opened. The operator uses disinfectant wipes to clean the subject's forehead, removing oil and cosmetics from the subject's head. The brain patch device is then taken out, and the correct placement of the patch is made on the EEG monitoring device and the subject's forehead. The adapter is connected to the computer's USB port to establish a Bluetooth connection between the brain patch device and the EEG monitoring device. EEG data can then be collected through the brain patch device in conjunction with the EEG monitoring device. The real-time sleep monitoring module analyzes and judges the collected EEG data every 6 seconds to detect whether the subject has entered a stable sleep stage. If so, the process 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 activated to evaluate the quality of the current EEG data to ensure the accuracy of subsequent analysis. After passing the data quality detection, the system enters the alpha wave intensity calculation stage. The alpha wave intensity calculation module calculates the alpha wave intensity within the specified time window. The average power of alpha waves accounts for a significant portion of the overall EEG output. The processed EEG data is then quantitatively assessed for alpha wave intensity. Based on the average alpha wave power percentage multiplied by an amplification factor, the system obtains the volume of pink noise stimulation. Simultaneously, the alpha wave rising edge detection module is activated, focusing on capturing the rising edge signal of the alpha wave. When an alpha wave rising edge meeting preset conditions is detected, the pink noise modulation module precisely applies pink noise stimulation near the tip of the rising edge to promote sleep onset. After applying pink noise stimulation, two consecutive stimulations are performed, followed by a 2.5-second cooling cycle to prevent overstimulation from interfering with the sleep process. Pink noise stimulation achieves the goal of neuromodulation of the alpha waveform, helping users gradually transition from a waking state to a calm state. Furthermore, through alpha wave intensity calculation and real-time monitoring, the system adaptively adjusts the pink noise intensity to provide precise sound stimulation, thereby enhancing the user's sleep experience.

[0074] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A neural modulation system based on closed-loop powder noise stimulation using alpha waves, characterized in that, The system 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 modulation module. These modules are sequentially connected and are all built into the EEG monitoring device. The alpha wave intensity calculation module is used to quantitatively evaluate the alpha wave intensity of the processed EEG data. This quantitative evaluation is primarily achieved through calculation of the time... The average power of alpha waves within the time window accounts for a proportion of the total EEG, providing basic data support for the next step of regulation. The stimulation volume of pink noise is obtained by multiplying the average power proportion of alpha waves by the amplification factor. When the alpha wave rising edge detection module is activated, it focuses on capturing the rising edge signal of alpha waves. When an alpha wave rising edge that meets the preset conditions is detected, the pink noise regulation module will precisely apply pink noise stimulation near the top of the rising edge to promote the sleep process. After detecting the alpha wave rising edge and applying pink noise stimulation, two consecutive stimulations are required, followed by a 2.5-second cooling cycle after each stimulation to prevent overstimulation from interfering with the sleep process.

2. The neural modulation system based on closed-loop powder noise stimulation using alpha waves according to claim 1, characterized in that, The real-time sleep monitoring module analyzes and judges 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. The criteria for determining a stable sleep stage are: a sleep state consisting of 15 consecutive N2 stages, which indicates a stable sleep stage. EEG data is collected using EEG monitoring devices, including but not limited to portable prefrontal cortex single-lead EEG monitoring devices. The specific steps are as follows: S101: First, the operator uses disinfectant wipes to clean the subject's forehead to remove oil and cosmetics from the subject's head; S102: Remove the brain patch device, correctly attach the click patch to the EEG monitoring device, and place it on the subject's forehead; S103: Connect the adapter to the computer's USB port to enable Bluetooth connection between the brain patch and the EEG monitoring device, allowing the brain patch to work with the EEG monitoring device to collect EEG data.

3. The neural modulation system based on closed-loop powder noise stimulation using 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. It also identifies and filters out artifacts caused by external interference or equipment problems, thereby improving the reliability of the data. The operational logic steps of the data quality detection module are as follows: S201: Calculate the average amplitude of the signal. If the average amplitude is less than 0.5, it is determined that there is no data. S202: Detect disconnection and detachment events, and evaluate them based on the event labels printed by the embedded software. If there is no event, label it (5.0, 3); if the device disconnects, label it (5.1, 3); if the device detaches, label it (5.2, 3). S203: Remove power frequency interference. Calculate the FFT spectrum, find the maximum amplitude in the 45-52Hz range, and the corresponding center frequency. If the amplitude is >2.0, it is determined that power frequency interference has occurred and it is marked in the software. S204: Extract the EEG frequency band range and filter it using a band-stop filter; S205: Detect packet loss and calculate the packet loss rate. Calculate a 6-second segment of signal. If the packet loss rate is >50%, it is marked as severe packet loss. S206: If packet loss can be recovered, the data in the lost packet will be supplemented; S207: To detect the jump fluctuations in EEG data caused by motion artifacts, a cubic interpolation method is used to construct the signal envelope. After delineating the region with a threshold of 120μV, the proportion of the region where the fluctuation occurs is calculated. If it is >50%, it is marked as a severe fluctuation. S208: If the fluctuation detected in step S207 can be removed, filter it through the defined area; S209: Detect unrelated low-frequency oscillations in EEG acquisition, calculate the STFT time-frequency plot, and calculate the instantaneous frequency and standard deviation. After delineating the region, calculate the proportion of the region where the oscillation occurs. If it is >50%, it is marked as a severe oscillation. S210: If the low-frequency oscillation detected in step S209 can be recovered, set the time-frequency coefficient of the abnormal region to zero and use inverse transformation to recover the normal signal.

4. The neural modulation system based on closed-loop powder noise stimulation using alpha waves according to claim 1, characterized in that, The specific logical steps for the coordinated operation of the alpha wave rising edge detection module and the powder noise control module are as follows: S301: Select and set parameters for the sliding window: Select a suitable sliding window for signal analysis based on the signal's time resolution and frequency characteristics, and determine the window function and window length range of the sliding window to ensure that the subtle changes in 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. The signal is detected to show a monotonically increasing trend within the sliding window, and it is also confirmed whether the signal has only one zero-crossing point. The part of the signal that meets these two conditions will be determined as the rising edge of the alpha wave. S304: Rising Edge Detection and Time Recording: 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 occurred. S305: The pink noise control module precisely applies two pink noise stimuli near the tip of the rising edge to promote the sleep process.

5. A neural modulation system based on closed-loop powder noise stimulation using alpha waves according to claim 4, characterized in that, In step S303, the criteria for determining the monotonicity and zero-point characteristics of the signal are 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, check if there is a negative value before this position. If there is only one transition between a negative value and a positive value, then the signal has only one zero crossing point.

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