A transcranial micro-current stimulation regulation method based on electroencephalogram feedback

Abstract

This invention discloses a transcranial microcurrent stimulation modulation method based on EEG feedback. The method involves activating an audio output module to output pink noise while simultaneously sending a light modulation command to an optical output module. While maintaining the pink noise output from the audio output module, the transcranial microcurrent stimulation module is activated to output microcurrent stimulation. The user's EEG signals are collected, and the user's entry into a target sleep state is determined based on these signals. Once the user is determined to be in the target sleep state, the output intensity of both the audio output module and the transcranial microcurrent stimulation module is gradually reduced. By using pink noise and light modulation as the initial activation modality, and then sequentially integrating transcranial microcurrent stimulation while maintaining acoustic stimulation, this method achieves orderly synergy of multimodal stimulation in the temporal dimension. Furthermore, it enables adaptive withdrawal of stimulation intensity based on EEG feedback, enhancing the temporal synergy and closed-loop adaptive capability of multimodal sleep-aid modulation.

Description

Technical Field

[0001] This invention relates to the field of neuroelectric stimulation technology, and in particular to a transcranial microcurrent stimulation modulation method based on electroencephalographic feedback. Background Technology

[0002] Neuromodulation technology has received widespread attention and application in the field of sleep improvement in recent years. Cranial Electrotherapy Stimulation (CES), as a non-invasive neuromodulation method, modulates neural activity by applying microampere-level currents to the brain. Studies have shown that CES can effectively improve sleep quality and has promising applications in the field of sleep aids. Meanwhile, acoustic stimulation (such as pink noise and binaural beats) and light environment modulation have also been proven to have positive effects on sleep induction and maintenance. With the advancement of wearable devices and sensor technology, sleep state monitoring technology based on electroencephalogram (EEG) signals is becoming increasingly mature, providing a technological foundation for the intelligent control of sleep aid devices.

[0003] Several sleep aid solutions exist in the current technology. One type of solution uses single transcranial microcurrent stimulation as its core, applying stimulation to the user through a current output at a fixed frequency or in a preset pattern. While this type of solution is easy to operate, its stimulation parameters are usually factory-preset and cannot be dynamically adjusted according to the user's actual sleep state. Another type of solution uses acoustic stimulation to assist in falling asleep, playing specific types of sounds to mask environmental noise or guide brainwave rhythms. This type of solution can shorten the sleep latency to some extent, but acoustic stimulation itself lacks the ability to perceive the user's physiological state, and its output is usually open-loop and unchanging.

[0004] Another approach involves simply superimposing multiple stimulus modalities, such as playing music or pink noise while stimulating CES, or superimposing acoustic stimulation on top of light environment modulation. However, most existing approaches simply activate multiple stimuli simultaneously or control them independently, failing to stagger the timing and coordinate them according to the physiological characteristics of each stimulus. Therefore, how to achieve temporal coordination and functional cooperation among modalities within a multimodal stimulation framework, so that stimuli from different modalities form effective synergy in the temporal dimension rather than simple superposition or interference, has become a pressing technical problem to be solved in this field. Summary of the Invention

[0005] To address the shortcomings of multimodal sleep aid programs, which lack systematic temporal coordination and misalignment among different stimulation modalities, resulting in the inability of each modality to achieve functional synergy over time, this invention proposes a transcranial microcurrent stimulation modulation method based on electroencephalographic feedback.

[0006] The technical solution adopted in this invention is a transcranial microcurrent stimulation modulation method based on electroencephalographic feedback, comprising: S100: Start the audio output module to output pink noise, and at the same time send a light control command to the light output module; S200. While maintaining the output of pink noise from the audio output module, start the transcranial microcurrent stimulation module to output microcurrent stimulation. S300: Collects the user's brainwave signals and determines whether the user has entered the target sleep state based on the brainwave signals; S400: When it is determined that the user has entered the target sleep state, the audio output module and the transcranial microcurrent stimulation module are controlled to gradually reduce the output intensity.

[0007] Preferably, step S200 further includes: S210, Control the transcranial microcurrent stimulation module to output microcurrent stimulation with the first frequency parameter; S220. While maintaining the output of pink noise from the audio output module, the output frequency parameter of the transcranial microcurrent stimulation module is gradually transitioned from the first frequency parameter to the second frequency parameter, where the second frequency parameter is lower than the first frequency parameter.

[0008] Preferably, step S300 includes: S310. Collect the user's EEG signal and extract at least one EEG feature parameter from the EEG signal; S320. Compare the EEG characteristic parameters with the preset sleep state determination threshold, and determine whether the user has entered the target sleep state based on the comparison result.

[0009] Preferably, step S400 includes: S410. Obtain the current output intensity values ​​of the transcranial microcurrent stimulation module and the audio output module; S420. According to the preset load reduction strategy, control the output intensity of the transcranial microcurrent stimulation module and the audio output module to gradually reduce from the current output intensity value to the target intensity value.

[0010] Preferably, step S200 further includes: S230, Real-time extraction of phase information from EEG signals; S240. Based on the phase information, control the phase of the microcurrent stimulation output by the transcranial microcurrent stimulation module to synchronize with the target rhythm phase of the EEG signal.

[0011] Preferably, the method further includes the following steps before step S100: S10. Acquire the user's baseline EEG signal in a resting state and extract at least one baseline feature parameter from the baseline EEG signal; S20. Determine the initial output parameters of the transcranial microcurrent stimulation module based on the baseline characteristic parameters.

[0012] Preferably, step S100 includes: S110. Collect the user's EEG signals and extract real-time EEG feature parameters from the EEG signals; S120. Determine the output amplitude modulation value of pink noise based on real-time EEG characteristic parameters; S130. According to the output amplitude modulation value, control the audio output module to output amplitude-modulated pink noise, and at the same time send an optical control command to the optical output module.

[0013] Preferably, the step between step S300 and step S400 further includes: S350: Determine the user's sleep stage based on EEG signals; S360. Based on sleep stages, determine the deviation between the current output parameters of the transcranial microcurrent stimulation module and the audio output module and the target output parameters corresponding to the sleep stages. S370. Adjust the output parameters of the transcranial microcurrent stimulation module and / or audio output module according to the deviation.

[0014] Preferably, the process further includes the following after step S400: S500: Real-time monitoring of the user's brainwave signals, and determination of whether the user has exited the target sleep state based on the brainwave signals; S510. When it is determined that the user has exited the target sleep state, obtain the reduced intensity values ​​of the transcranial microcurrent stimulation module and the audio output module at the time of exit. S520. Based on the reduced intensity value, restore the output intensity of the transcranial microcurrent stimulation module and the audio output module.

[0015] Preferably, step S520 includes: S521. Obtain the reduced intensity values ​​of the transcranial microcurrent stimulation module and the audio output module at the exit time, and use them as the initial intensity values ​​for recovery. S522. Starting from the initial recovery intensity value, according to the preset recovery strategy, control the output intensity of the transcranial microcurrent stimulation module and the audio output module to gradually recover to the output intensity value before step S400.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This application discloses a transcranial microcurrent stimulation modulation method based on EEG feedback, which specifically includes: firstly, starting the audio output module to output pink noise and sending light modulation instructions; then, while maintaining the output of pink noise, starting the transcranial microcurrent stimulation module to output microcurrent stimulation; then, collecting EEG signals and determining whether the user has entered the target sleep state based on the EEG signals; finally, after determining that the user has entered the target sleep state, controlling the audio output module and the transcranial microcurrent stimulation module to gradually reduce the output intensity.

[0017] Compared with existing technologies, the sleep-aid regulation method disclosed in this application can achieve orderly arrangement and coordinated cooperation of multimodal stimulation in the temporal dimension. This application uses pink noise and light modulation commands as the first initiation sequence, and then connects transcranial microcurrent stimulation on the basis of the established external environment of sound and light stimulation. This allows stimulation modalities with different onset times to intervene in an orderly manner according to their respective physiological characteristics, thereby avoiding sensory competition caused by simultaneous multimodal activation and enabling each modality to function at its appropriate time, forming a relay coordination in the temporal dimension. Meanwhile, most existing multimodal solutions are open-loop controls, with the maintenance and withdrawal of stimulation relying on preset time periods or manual operation, lacking perception and response to the user's actual sleep state. This application collects the user's EEG signals and determines whether the user has entered the target sleep state based on the EEG signals. After determining that the user has entered the sleep state, the stimulation modalities are gradually deloaded and withdrawn, so that the duration of the entire stimulation process matches the actual sleep process. This avoids stopping stimulation too early before the user has fallen asleep, and also avoids continuing unnecessary stimulation output after the user has fallen asleep, thereby improving the timing control accuracy and overall regulation efficiency of multimodal stimulation. Attached Figure Description

[0018] The present invention will now be described in detail with reference to the embodiments and accompanying drawings, wherein: Figure 1 A schematic flowchart of a transcranial microcurrent stimulation modulation method based on electroencephalographic feedback provided in an embodiment of the present invention is shown. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Examples of embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar components or components having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0020] Most existing multimodal sleep aid solutions activate multiple stimuli simultaneously or control them independently, lacking temporal differentiation between the different modalities. This leads to two problems: First, when acoustic stimulation, light environment modulation, and transcranial microcurrent stimulation are activated simultaneously, multiple sensory inputs are concentrated on the user at the same time, potentially causing sensory competition or overload effects. Second, these three types of stimulation have different physiological targets and onset times—acoustic and light stimulation works through the external environmental perception channel, and its effects are relatively rapid; while transcranial microcurrent stimulation acts directly on the central nervous system, and its neuromodulation effect requires a certain physiological time to establish. Without temporal differentiation, the stimulation of each modality cannot form functional coordination in the temporal dimension, and may even lead to low synergistic efficiency due to misalignment of action times.

[0021] This invention discloses a transcranial microcurrent stimulation modulation method based on electroencephalographic feedback. Please refer to [link / reference]. Figure 1 ,include: S100: Start the audio output module to output pink noise, and at the same time send a light control command to the light output module; S200. While maintaining the output of pink noise from the audio output module, start the transcranial microcurrent stimulation module to output microcurrent stimulation. S300: Collects the user's brainwave signals and determines whether the user has entered the target sleep state based on the brainwave signals; S400: When it is determined that the user has entered the target sleep state, the audio output module and the transcranial microcurrent stimulation module are controlled to gradually reduce the output intensity.

[0022] Specifically, acoustic stimulation, light environment modulation, and transcranial microcurrent stimulation are staggered along the timeline, allowing stimulation modalities with different onset times and target points to intervene in an orderly manner according to their respective physiological characteristics, and to gradually withdraw after the user enters the target sleep state. Unlike existing technologies that involve simultaneous or independent operation of multiple modalities, this approach constructs a four-stage temporal framework of "acoustic and light preparation, CES access, EEG assessment, and gradual withdrawal." This ensures that the entire process of each modal stimulation, from initiation and maintenance to withdrawal, dynamically matches the user's real-time sleep state, thereby improving the overall synergistic efficiency of multimodal stimulation while avoiding multimodal sensory competition.

[0023] In step S100, the control module sends a start command to the audio output module, causing the audio output module to begin outputting pink noise; simultaneously, the control module sends a light control command to the light output module. "Simultaneously" in S100 means that the two actions are initiated in parallel time, not sequentially; that is, the audio start command and the light control command are issued within the same control cycle or under the same triggering event. The control module can be a microcontroller, a digital signal processor, or an application-specific integrated circuit (ASIC).

[0024] The audio output module can be a built-in or external speaker, bone conduction headphones, bone conduction pillow speakers, in-ear headphones, or other devices with sound playback capabilities. Pink noise is a sound signal whose power spectral density is inversely proportional to its frequency, meaning that the energy is equal per octave. Its characteristic is that the low-frequency components have higher energy than the high-frequency components, making it sound softer and more natural than white noise. The control module can generate pink noise signals in real time using a built-in pink noise generation algorithm, or it can read pre-generated pink noise audio files from storage media, convert them from digital to analog, and then play them through the audio output module.

[0025] The light output module can be a smart lamp, LED light strip, bedside lamp, wearable phototherapy device, or other device with light output functionality. The content of the light control commands depends on the specific control objective; it can be a dimming command (reducing light intensity), a color temperature switching command (e.g., switching from cool white light to warm yellow light), a spectrum adjustment command (e.g., increasing red light component and decreasing blue light component), a brightness gradient command (simulating sunset light effects), or a combination of these control methods. The control module sends these commands to the light output module via wired or wireless communication methods (e.g., Bluetooth, Wi-Fi, ZigBee, infrared, etc.).

[0026] Step S100 is the initial step in the entire multimodal sleep-aiding modulation process, its role being to first establish a favorable external environment for falling asleep. The continuous playback of pink noise can mask sudden noises in the environment, such as vehicle sounds and conversations, preventing sudden noises from interfering with the user's sleep process. Simultaneously, its stable and monotonous acoustic characteristics can slow down and regulate brain waves. The light modulation command, by adjusting the physiological rhythm signals of melatonin secretion, especially by dimming or switching to a warmer spectrum, conveys an environmental cue to the user's biological clock to enter nighttime mode. The intervention of both sound and light modalities in the initiation phase creates a transitional state conducive to physical and mental relaxation for the user through the external environmental channel without directly applying neural electrical stimulation, providing a good physiological and psychological foundation for the subsequent access of transcranial microcurrent stimulation.

[0027] In a preferred embodiment, the output intensity of the pink noise can be set to 30-50 dB, a range that effectively masks ambient noise without causing auditory discomfort to the user. The preferred light control command is to gradually reduce the ambient light intensity to below 5 lux and switch the color temperature to a warm tone below 2700K. In alternative embodiments, the light control command can be a single on / off control, continuous dimming of brightness, gradual color temperature switching, or any combination of the above. The pink noise can also be replaced with other types of steady-state acoustic stimulation, such as a mixture of pink noise and white noise, or pure tones of specific frequencies. However, pink noise, due to its spectral characteristics being similar to natural sounds like wind and rain, has advantages in terms of auditory comfort and brainwave guidance effects.

[0028] In step S200, while maintaining the continuous output of pink noise from the audio output module, the control module sends a start command to the transcranial microcurrent stimulation module, causing the module to begin outputting microcurrent stimulation to the user. The transcranial microcurrent stimulation module typically includes a stimulation signal generation circuit (including a waveform generator and a constant current source circuit) and a pair of electrodes. The electrodes are attached to the user's earlobe, mastoid process, forehead, or other areas, and apply a microampere-level current to the brain through the electrodes.

[0029] The initial output parameters of microcurrent stimulation, such as waveform, frequency, pulse width, and current intensity, can be determined based on preset default values ​​or customized according to the user's individual baseline characteristics. Step S200 emphasizes that the CES mode is initiated after the acousto-optic mode has already started running, rather than starting simultaneously with the acousto-optic mode.

[0030] The purpose of step S200 is to timely integrate the core neuromodulation modality of transcranial microcurrent stimulation (CES) on the basis of acoustic and optical stimulation. If CES is activated simultaneously with acoustic and optical stimulation, the user will be subjected to concentrated input from three modalities at the same time, which may cause sensory overload or mutual interference between stimuli. However, by delaying the CES until the acoustic and optical stimulation has been running for a period of time, the user can first establish a relaxed state at the level of the external environment before receiving the regulation of neural electrical stimulation, thereby reducing the abruptness and competitive effect caused by the simultaneous activation of multiple modalities.

[0031] The activation timing of CES can be set 3 to 10 minutes after the onset of audio-visual stimulation. This time window is sufficient for the initial establishment of the preparatory effects of audio-visual stimulation without reducing the overall efficiency of the process due to excessive waiting. The initial current intensity of microcurrent stimulation is preferably 100 to 300 microamps, and the initial frequency can be determined according to the frequency transition scheme. The stimulation waveform can be a square wave, sine wave, triangle wave, or biphasic pulse wave, etc. Electrodes can be in various forms, such as ear clip electrodes, patch electrodes, or headband electrodes. CES can also be activated using a gradual approach, where the current intensity gradually increases from zero to the target value over several seconds to tens of seconds to further reduce the abruptness of the activation.

[0032] In step S300, the user's brainwave signals are acquired in real time using an EEG acquisition module (e.g., a dry electrode EEG headband, a wet electrode EEG cap, an in-ear EEG sensor, etc.). After preprocessing such as amplification, filtering, and analog-to-digital conversion, the EEG signals are analyzed by a control module or a dedicated signal processing unit to extract EEG characteristic parameters that reflect the sleep state (such as power spectral density of a specific frequency band, band energy ratio, complexity index, etc.). The extracted characteristic parameters are then compared with a preset sleep state determination threshold. Based on the comparison result, it is determined whether the user has entered the target sleep state. The determination can be an instantaneous determination at a single point in time or a continuous determination over a time window, for example, only when the entry conditions are met for N consecutive time windows is it determined that the user has entered the sleep state.

[0033] During sleep, electroencephalogram (EEG) signals exhibit regular rhythmic changes. In a state of wakefulness and relaxation, EEG is dominated by alpha waves; in the early stages of sleep, alpha waves gradually weaken and theta waves begin to appear; during light sleep, sleep spindles and K-complexes appear; and during deep sleep, high-amplitude slow delta waves dominate. Therefore, by analyzing the energy distribution and trends of different frequency bands in EEG signals, the user's sleep state can be determined relatively reliably. Specifically, by calculating the power spectral density of each frequency band, including delta waves, theta waves, alpha waves, σ waves (the frequency band containing sleep spindles), and beta waves, characteristic parameters such as the δ / β ratio, the θ / α ratio, and the total power proportion of the δ+θ band can be extracted. Comparing these parameters with pre-calibrated sleep state determination thresholds allows for the determination of whether the user has entered a target sleep state, such as light sleep, deep sleep, or a specific sleep stage.

[0034] Step S300 establishes a closed-loop feedback decision node for the entire multimodal stimulation process. In S100 and S200, both auditory-visual stimulation and CES stimulation are output in an open loop according to preset timing and parameters, independent of the user's real-time physiological state. The introduction of step S300 ensures that subsequent stimulation withdrawal is no longer based on a fixed time period or manual operation, but rather on the user's actual sleep state. This closed-loop feedback mechanism ensures that the duration of the entire sleep-aiding regulation process matches the user's actual sleep progress: if the user has not yet fallen asleep, the process continues to output stimulation; if the user has fallen asleep, the process proceeds to the next withdrawal stage. This avoids the problem of fixed stimulation duration and disconnection from the user's actual state in traditional solutions.

[0035] In a preferred embodiment, the sampling frequency of the EEG signal is not less than 128Hz to fully capture the signal characteristics of each sleep-related frequency band. EEG characteristic parameters can be one or more combinations of the following: delta band (0.5-4Hz) power percentage, theta band (4-8Hz) power percentage, delta / beta power ratio, theta / alpha power ratio, and EEG complexity index. The judgment threshold can be preset after statistical analysis of sleep EEG data from a large number of users, or it can be personalized for specific users before use. The target sleep state can be a combination of light sleep, deep sleep, or a specific stage. In an alternative embodiment, in addition to EEG signals, multiple physiological signals such as electrocardiogram (ECG), electromyography (EMG), electrooculography (EOG), and body movement signals can be collected for comprehensive judgment to improve the accuracy and robustness of sleep state determination.

[0036] In step S400, after step S300 determines that the user has entered the target sleep state, the control module initiates the exit process, controlling the output intensity of the audio output module and the transcranial microcurrent stimulation module to gradually decrease from the current value to a lower target value. Here, "gradually decrease" means that the output intensity decreases gradually according to a certain time function (such as linear decrease, step decrease, exponential decrease, etc.), rather than being turned off instantaneously or decreasing abruptly.

[0037] Specifically, the control module first acquires the current output intensity values ​​(such as current volume and current intensity) of the audio output module and the transcranial microcurrent stimulation module. Then, according to a preset de-load strategy, such as reducing the current value by 10% every 30 seconds or by a fixed step every 60 seconds, it periodically updates the output intensity commands of each module until a preset target intensity value is reached. This target value can be zero (completely off) or a non-zero, low-level maintenance value. During the entire de-load process, the de-load of the audio output module and the transcranial microcurrent stimulation module can be performed synchronously or separately at different rates.

[0038] Step S400 aims to achieve a smooth exit of multimodal stimulation after the user falls asleep. Once the user has entered the target sleep state, continuing to maintain high-intensity multimodal stimulation is not only unnecessary but may also interfere with sleep stability due to continuous sensory input. However, if all stimulation is suddenly turned off, the user may be awakened by abrupt changes in environmental perception (such as sudden silence or the sudden loss of weak perception of electrical stimulation), a phenomenon known as the "rebound effect" or "awakening response." Step S400 exits by "gradually reducing" rather than "instantly shutting down," making the change in the user's sensory environment gradual and adaptive, thereby minimizing interference with sleep continuity while stopping stimulation. Simultaneously, the audio output module and the transcranial microcurrent stimulation module reduce their load synchronously, ensuring the coordination and consistency of the two modalities during the exit process and avoiding the uncoordinated state where one modality has been shut down while the other is still outputting at high intensity.

[0039] The de-entrainment strategy can employ a linear decreasing approach: the total de-entrainment duration is set to 3 to 10 minutes, during which the output intensity of each module decreases uniformly from its current value to the target value. Alternatively, an exponential decreasing approach can be used, with faster de-entrainment initially and slower de-entrainment later. This method rapidly reduces the stimulation intensity while avoiding a sudden drop in the final stage. The de-entrainment rate can also be dynamically adjusted based on the user's real-time EEG status: if the EEG signal indicates the user is in deep sleep, the de-entrainment rate can be appropriately increased; if the user is in light sleep, the de-entrainment rate can be appropriately decreased. The target intensity value can be set to zero or a low non-zero maintenance value, retaining extremely low-volume pink noise as continuous ambient background sound, or retaining extremely low-intensity CES stimulation as a weak maintenance input. The latter can more quickly recover to an effective stimulation level if the user may briefly awaken from deep sleep.

[0040] Preferably, step S200 further includes: S210, Control the transcranial microcurrent stimulation module to output microcurrent stimulation with the first frequency parameter; S220. While maintaining the output of pink noise from the audio output module, the output frequency parameter of the transcranial microcurrent stimulation module is gradually transitioned from the first frequency parameter to the second frequency parameter, where the second frequency parameter is lower than the first frequency parameter.

[0041] Specifically, in step S210, after the transcranial microcurrent stimulation module is activated, it first outputs microcurrent stimulation at a first frequency parameter. This first frequency parameter can be determined according to a default setting or preset according to the user's individual baseline EEG characteristics. In step S220, while the audio output module continuously outputs pink noise, the output frequency parameter of the transcranial microcurrent stimulation module gradually transitions from the first frequency parameter to a second frequency parameter, wherein the value of the second frequency parameter is lower than the value of the first frequency parameter. This "gradual transition" means that within a preset time window, the frequency parameter evolves continuously or segmentally from the initial value to the target value according to a preset change curve, rather than a sudden change or jump in frequency at a certain moment. The first frequency parameter and the second frequency parameter are both superordinate expressions, used to refer to the starting value and the ending value of the frequency transition, respectively, without limiting the specific frequency value. In actual implementation, the rate of frequency transition can be a constant rate or dynamically change with time.

[0042] This approach optimizes the temporal modulation of multimodal stimulation by introducing a dynamic transition in transcranial microcurrent stimulation frequency from higher to lower frequencies. It is known that microcurrent stimulation at different frequencies has different guiding effects on brain rhythms; higher-frequency stimulation helps establish a relaxed state, while lower-frequency stimulation is associated with sleep maintenance and deep sleep promotion. By implementing a gradual rather than abrupt frequency transition between the first and second frequency parameters, the stimulation frequency changes experienced by the user's central nervous system are smooth and adaptive, avoiding potential neural maladaptation or perceptual abruptness caused by sudden frequency changes, further improving the coherence and precision of multimodal stimulation modulation. Simultaneously, this frequency transition occurs against a background of continuously outputting pink noise; the continuous presence of pink noise provides stable acoustic masking for the frequency transition, making the transition smoother at the perceptual level.

[0043] Preferably, step S300 includes: S310. Collect the user's EEG signal and extract at least one EEG feature parameter from the EEG signal; S320. Compare the EEG characteristic parameters with the preset sleep state determination threshold, and determine whether the user has entered the target sleep state based on the comparison result.

[0044] In step S310, the user's EEG signal is acquired in real time through the EEG acquisition module, and at least one EEG feature parameter is extracted from the acquired EEG signal. EEG feature parameters are quantitative indicators that reflect the energy distribution or waveform characteristics of a specific frequency band of the EEG signal. In practice, after preprocessing such as amplification, filtering, and analog-to-digital conversion, the EEG signal is used to extract feature parameters through methods such as power spectrum analysis, time-frequency analysis, or nonlinear dynamics analysis. Extractable EEG feature parameters include, but are not limited to: power spectral density of the delta band, power spectral density of the theta band, power spectral density of the alpha band, power spectral density of the beta band, power ratio of the delta band to the beta band, power ratio of the theta band to the alpha band, total power ratio of the delta band to the theta band, and complexity indicators such as approximate entropy or sample entropy of the EEG signal. In practical applications, one of the above parameters can be selected as the judgment criterion, or multiple parameters can be combined to improve the accuracy of the judgment.

[0045] In step S320, the EEG feature parameters extracted in step S310 are compared with a preset sleep state determination threshold, and the user is determined to have entered the target sleep state based on the comparison result. The preset sleep state determination threshold is a pre-defined value or range of values ​​after statistical analysis of a large amount of sample data, used to distinguish different sleep states. Specifically, when the extracted EEG feature parameters meet preset conditions, such as a feature parameter exceeding the threshold, falling below the threshold, or falling into a specific range of values, the user is determined to have entered the target sleep state. This determination can be an instantaneous determination at a single point in time, or a continuous determination within a continuous time window. For example, the determination result can be output only after the EEG feature parameters of multiple consecutive sampling windows meet the preset conditions, in order to reduce the impact of transient interference or artifacts on the accuracy of the determination.

[0046] This scheme breaks down the EEG signal determination process into two quantifiable operational steps: feature extraction and threshold comparison. This provides a clear technical implementation path for the entire closed-loop feedback decision-making mechanism, further enhancing the objectivity and reliability of sleep state determination.

[0047] Preferably, step S400 includes: S410. Obtain the current output intensity values ​​of the transcranial microcurrent stimulation module and the audio output module; S420. According to the preset load reduction strategy, control the output intensity of the transcranial microcurrent stimulation module and the audio output module to gradually reduce from the current output intensity value to the target intensity value.

[0048] In step S410, the output intensity values ​​of the transcranial microcurrent stimulation module and the audio output module at the current moment are acquired. This current output intensity value refers to the current intensity being output by the transcranial microcurrent stimulation module and the volume or sound pressure level being output by the audio output module before the load reduction operation is performed. In practice, the control module can read the current output intensity value from the drive register, status register, or feedback detection circuit of each module, or it can obtain it by sampling the output drive signal of each module and performing calculations. The purpose of acquiring the current output intensity value is to provide a starting reference for the subsequent load reduction operation, enabling the load reduction process to start from the actual operating state, rather than from a preset or assumed value.

[0049] In step S420, according to a preset load reduction strategy, the output intensity of the transcranial microcurrent stimulation module and the audio output module is gradually reduced from the current output intensity value obtained in step S410 to the target intensity value. The preset load reduction strategy refers to the output intensity variation rules over time that are pre-set and stored in the control module, which define the rate, step size, or variation curve of load reduction. In actual implementation, the control module periodically updates the output intensity commands of each module according to the time intervals and reduction amounts defined in the load reduction strategy until the output intensity reaches the target intensity value. The target intensity value can be zero or a pre-set non-zero low-level maintenance value.

[0050] This solution provides a concrete and operable implementation path for the "gradual reduction" in step S400 by introducing two operations: "obtaining the current output intensity value" and "following a preset load reduction strategy." Obtaining the current output intensity value ensures that the starting reference for load reduction is the actual operating state rather than the theoretical value, avoiding deviations in the load reduction process caused by an inaccurate starting reference. The preset load reduction strategy allows the load reduction rate, load reduction step size, and load reduction curve shape to be pre-designed and adjusted, thereby enabling the selection of an appropriate load reduction method according to different usage scenarios or user needs, further improving the flexibility and adaptability of the exit phase control.

[0051] Preferably, step S200 further includes: S230, Real-time extraction of phase information from EEG signals; S240. Based on the phase information, control the phase of the microcurrent stimulation output by the transcranial microcurrent stimulation module to synchronize with the target rhythm phase of the EEG signal.

[0052] In step S230, during the output of microcurrent stimulation by the transcranial microcurrent stimulation module, the phase information of the user's electroencephalogram (EEG) signal is extracted in real time. The phase information of the EEG signal refers to the instantaneous phase angle of the EEG waveform in a specific frequency band, reflecting the oscillation period position of the EEG rhythm at the current moment. In practice, the acquired EEG signal can be analyzed in real time using methods such as Hilbert transform, wavelet transform, or phase-locked loop (PLL) to extract the instantaneous phase value of the target frequency band, such as the alpha or theta band.

[0053] In step S240, based on the EEG signal phase information extracted in step S230, the phase of the microcurrent stimulation output by the transcranial microcurrent stimulation module is controlled to remain synchronized with the target rhythm phase of the EEG signal. The target rhythm phase refers to the instantaneous phase of the oscillation rhythm corresponding to the target frequency band in the EEG signal. This phase changes continuously over time, therefore, the synchronization of the stimulation phase is a dynamic tracking process. In actual implementation, the control module dynamically adjusts the output timing of the microcurrent stimulation based on the real-time extracted phase information, aligning a specific phase point of the stimulation pulse or waveform with the target rhythm phase point of the EEG signal, thus locking the phase of the microcurrent stimulation onto the phase of the EEG signal. This phase synchronization can be zero-phase-difference synchronization or continuous synchronization with a constant phase difference. Continuous synchronization with a constant phase difference means maintaining a fixed phase offset between the phase of the microcurrent stimulation and the phase of the EEG signal.

[0054] This approach further enhances the precision of transcranial microcurrent stimulation (TCS) in regulating brain rhythms by introducing real-time synchronization between the phase of microcurrent stimulation and the phase of the target brain rhythm. When external electrical stimulation and endogenous neural oscillations are synchronized in phase, the external stimulation can more effectively resonate with or carry over the endogenous rhythm; that is, the external stimulation enhances or inhibits brain activity in specific frequency bands through a phase-locking mechanism. This approach, through real-time extraction and dynamic synchronization control of phase information, enables TCS to achieve dynamic transitions not only in frequency parameters but also in phase matching with the user's real-time brain state, thereby improving the coupling efficiency between stimulation and brain rhythms.

[0055] Preferably, the method further includes the following steps before step S100: S10. Acquire the user's baseline EEG signal in a resting state and extract at least one baseline feature parameter from the baseline EEG signal; S20. Determine the initial output parameters of the transcranial microcurrent stimulation module based on the baseline characteristic parameters.

[0056] In step S10, the user wears the EEG acquisition module in a resting state to collect baseline EEG signals over a period of time, and extracts at least one baseline characteristic parameter from these signals. The resting state refers to a quiet, awake state where the user is relaxed with eyes closed, not asleep, and without significant mental activity. Typically, the user is required to sit or lie still with eyes closed for 1 to 5 minutes before collecting the baseline signal to allow the EEG signal to reach a relatively stable baseline level. The baseline EEG signal refers to the EEG signal collected in the aforementioned resting state, reflecting the user's intrinsic brainwave rhythm characteristics without external stimulation. The baseline characteristic parameter is a feature value obtained after quantitative analysis of the baseline EEG signal, including but not limited to: individual alpha wave peak frequency, the energy ratio of the alpha band to the theta band, the absolute power value of the alpha band, the total power ratio of the delta band to the theta band, and the 1 / f slope of the EEG power spectrum. In practice, the specific values ​​of the above characteristic parameters are extracted from the spectrum through power spectrum analysis or time-frequency analysis of the baseline EEG signal.

[0057] In step S20, the initial output parameters of the transcranial microcurrent stimulation module are determined based on the baseline feature parameters extracted in step S10. The initial output parameters refer to the initial stimulation parameters output by the transcranial microcurrent stimulation module after activation in step S200, including but not limited to the initial frequency parameters and initial current intensity parameters. This determination process is based on a preset mapping relationship between the baseline feature parameters and the initial output parameters; that is, different baseline feature parameter values ​​correspond to different initial output parameter settings. For example, when an individual's alpha wave peak frequency is high, the initial frequency parameter is set to a higher value; when an individual's alpha wave peak frequency is low, the initial frequency parameter is set to a lower value, so that the initial frequency parameters of the stimulation match the user's inherent EEG rhythm characteristics. In actual implementation, this mapping relationship can be pre-stored in the control module in the form of a lookup table, fitting function, or rule set. In step S20, the control module obtains the corresponding initial output parameters by looking up the table or calculating based on the extracted baseline feature parameter values.

[0058] This scheme achieves adaptation of stimulation parameters to individual user differences by collecting resting-state EEG characteristics of each user before the formal process begins and determining the initial output parameters of microcurrent stimulation based on these characteristics. Since there are significant individual differences in EEG characteristics such as alpha wave peak frequency among different users, using a uniform default frequency parameter may cause the stimulation start point for some users to deviate from the sensitive range of their own EEG rhythm, thus affecting the stimulation effect. This scheme, through pre-baseline calibration, ensures that the initial parameters of microcurrent stimulation are based on the user's own EEG characteristics, thereby avoiding the problem of uniform parameters failing to adapt to individual differences and improving the individual adaptability and overall control efficiency of subsequent multimodal stimulation processes. Furthermore, steps S10 and S20 are only executed during the first use or periodic calibration, and the determined initial output parameters can be stored in the control module for multiple subsequent uses without needing to be repeated before each use.

[0059] Preferably, step S100 includes: S110. Collect the user's EEG signals and extract real-time EEG feature parameters from the EEG signals; S120. Determine the output amplitude modulation value of pink noise based on real-time EEG characteristic parameters; S130. According to the output amplitude modulation value, control the audio output module to output amplitude-modulated pink noise, and at the same time send an optical control command to the optical output module.

[0060] In step S110, the user's EEG signal is acquired, and real-time EEG feature parameters are extracted from the acquired EEG signal. Real-time EEG feature parameters refer to quantitative indicators that reflect the energy or waveform characteristics of a specific frequency band of the EEG signal at the current moment. In practice, the EEG acquisition module continuously acquires EEG signals, and the control module or dedicated signal processing unit performs sliding window analysis on the acquired signals, extracting EEG feature parameters within each time window. Extractable real-time EEG feature parameters include, but are not limited to: the instantaneous power value of the alpha band, the real-time power ratio of the alpha band to the beta band, the total power ratio of the delta band to the theta band, and the real-time complexity index of the EEG signal. Unlike the EEG feature parameters used for sleep state determination in step S310, the real-time EEG feature parameters in this step focus on reflecting the instantaneous changes in the EEG state at the current moment, used to drive the immediate adjustment of the pink noise output amplitude.

[0061] In step S120, the output amplitude modulation value of the pink noise is determined based on the real-time EEG feature parameters extracted in step S110. The output amplitude modulation value refers to the control quantity used to adjust the output amplitude of the pink noise, which determines the offset or proportion of the pink noise amplitude output by the audio output module at the current moment relative to the reference amplitude. This determination process is based on a preset mapping relationship between the real-time EEG feature parameters and the output amplitude modulation value. For example, when an increase in alpha wave power is detected, the output amplitude modulation value is increased; when active beta wave activity is detected, the output amplitude modulation value is decreased. In actual implementation, this mapping relationship can be pre-stored in the control module in the form of a functional relationship, a lookup table, or fuzzy rules. The control module calculates or looks up the current output amplitude modulation value in real time based on the real-time extracted EEG feature parameter values.

[0062] In step S130, according to the output amplitude modulation value determined in step S120, the audio output module is controlled to output amplitude-modulated pink noise, while simultaneously sending a light control command to the light output module. Amplitude modulation refers to the dynamic change of the output amplitude of the pink noise as the real-time EEG characteristic parameters change; that is, the volume or sound pressure level of the pink noise is no longer fixed but is continuously adjusted according to the user's real-time EEG state. In actual implementation, the control module converts the output amplitude modulation value into a drive signal control quantity for the audio output module, causing the audio output module to output pink noise of the corresponding amplitude according to the control quantity. The "simultaneously" in this step has the same meaning as in the aforementioned step S100, that is, the output of amplitude-modulated pink noise and the sending of the light control command are initiated in parallel time.

[0063] This scheme dynamically binds the output amplitude of pink noise to the user's real-time EEG state, transforming pink noise from a static background sound with a fixed amplitude into a dynamic modulation tool that adapts to changes in EEG state. When the user's EEG characteristics indicate a relaxed state, the amplitude of the pink noise can be increased accordingly to enhance the acoustic guidance effect; when the user's EEG characteristics indicate a more alert or disturbed state, the amplitude of the pink noise can be decreased accordingly to avoid overstimulation. This dynamic amplitude modulation enables real-time interaction between acoustic stimulation and the user's instantaneous physiological state, improving the adaptability and guidance efficiency of pink noise as a preliminary modality. Simultaneously, the parallel transmission of optical modulation commands ensures that optical environment regulation and dynamic acoustic stimulation intervene synergistically at the same temporal node.

[0064] Preferably, the step between step S300 and step S400 further includes: S350: Determine the user's sleep stage based on EEG signals; S360. Based on sleep stages, determine the deviation between the current output parameters of the transcranial microcurrent stimulation module and the audio output module and the target output parameters corresponding to the sleep stages. S370. Adjust the output parameters of the transcranial microcurrent stimulation module and / or audio output module according to the deviation.

[0065] This preferred solution adds a parameter navigation stage based on sleep stages, which is used to dynamically adjust the output parameters according to the user's specific sleep stage after determining that the user has entered the target sleep state and before executing the load reduction exit.

[0066] In step S350, the user's current sleep stage is determined based on the collected EEG signals. Sleep staging refers to dividing the sleep process into different physiological stages according to EEG characteristics, typically including light sleep, deep sleep, and REM sleep. In practice, time-frequency analysis of the EEG signals is performed to extract power spectrum features of various frequency bands such as delta waves, theta waves, alpha waves, sigma waves, and beta waves, as well as time-domain waveform features such as sleep spindle waves and K-complex waves. These features are then matched with typical feature templates for each sleep stage or input into a pre-trained classification model to determine the user's specific sleep stage. Different sleep stages correspond to different EEG feature patterns. For example, light sleep is dominated by theta waves and includes sleep spindle waves, deep sleep is dominated by high-amplitude delta waves, and REM sleep is characterized by low-amplitude mixed-frequency EEG activity.

[0067] In step S360, based on the sleep stage determined in step S350, the deviation between the current output parameters of the transcranial microcurrent stimulation module and the audio output module and the target output parameters corresponding to that sleep stage is determined. The current output parameters refer to the actual stimulation parameters output by the transcranial microcurrent stimulation module and the audio output module at the current moment, including but not limited to the frequency and current intensity parameters of the microcurrent stimulation, and the output amplitude parameters of the pink noise. The target output parameters refer to the expected output parameter values ​​preset for the currently determined sleep stage, that is, the optimal or near-optimal combination of output parameters considered beneficial for maintaining the current sleep state or promoting the transition to a deeper sleep stage under that sleep stage. In actual implementation, the control module compares the values ​​of the current output parameters with the values ​​of the target output parameters one by one, calculates the difference or the percentage of the difference, and obtains the deviation of each output parameter. The specific calculation method for the deviation can be an absolute difference, a relative percentage, or a normalized deviation value, determined according to the actual control requirements.

[0068] In step S370, the output parameters of the transcranial microcurrent stimulation module and / or the audio output module are adjusted according to the deviation determined in step S360. Adjustment refers to changing the output parameters of the corresponding module based on the magnitude and direction of the deviation, making them approach the target output parameters. In practice, when the deviation is positive, it indicates that the current output parameter is higher than the target output parameter, and the control module correspondingly lowers the output parameter of that module; when the deviation is negative, it indicates that the current output parameter is lower than the target output parameter, and the control module correspondingly increases the output parameter of that module. Adjustment can be applied to either the transcranial microcurrent stimulation module or the audio output module, or both simultaneously, depending on the module involved in the current deviation. For example, when only the microcurrent stimulation frequency deviates from the target value, only the frequency parameter of that module can be adjusted; when both the pink noise amplitude and the microcurrent stimulation frequency deviate from the target values, both can be adjusted simultaneously. The adjustment process can use a proportional adjustment method, i.e., using the deviation as input and the adjustment amount as output, adjusting according to a predetermined proportional coefficient; or it can use integral adjustment or proportional-integral adjustment methods to make the adjustment process smoother and avoid frequent fluctuations in output parameters.

[0069] By introducing a parameter navigation mechanism based on sleep stages, the original binary judgment mode is upgraded to a multi-level navigation mode. The original step S300 only performed a binary judgment between entering the target sleep state and exiting; before this judgment, the stimulation parameters remained essentially in open-loop operation. However, this solution continuously monitors the user's specific sleep stage after determining entry into the target sleep state and before executing the download exit, dynamically adjusting the output parameters accordingly. This ensures that all output parameters consistently match the user's current actual sleep stage before download exit. By continuously calculating the deviation between the current output parameters and the target parameters corresponding to the stage and correcting them in real time, significant deviations between the output parameters and the user's sleep state are avoided, improving the continuity and adaptability of regulation throughout the transition from sleep onset to download exit.

[0070] Preferably, the process further includes the following after step S400: S500: Real-time monitoring of the user's brainwave signals, and determination of whether the user has exited the target sleep state based on the brainwave signals; S510. When it is determined that the user has exited the target sleep state, obtain the reduced intensity values ​​of the transcranial microcurrent stimulation module and the audio output module at the time of exit. S520. Based on the reduced intensity value, restore the output intensity of the transcranial microcurrent stimulation module and the audio output module.

[0071] This preferred solution adds a post-exit monitoring and re-entry phase, which continuously tracks the user's sleep state after the system completes its unloading and exit, and automatically resumes stimulation output when the system detects that the user has exited the system.

[0072] In step S500, after step S400 is completed and the output intensity of each module has decreased to the target intensity value, the EEG acquisition module continues to monitor the user's EEG signals in real time, and determines whether the user has exited the target sleep state based on the acquired EEG signals. Exit means that the user's EEG characteristics change from the characteristic pattern corresponding to the target sleep state to the characteristic pattern corresponding to a non-target sleep state, such as changing from deep sleep or light sleep characteristics to wakefulness or micro-arousal characteristics. In actual implementation, this judgment process can use the same EEG characteristic parameters and judgment threshold as in step S300 for reverse comparison—when the EEG characteristic parameters no longer meet the judgment conditions of the target sleep state, it is determined that the user has exited the target sleep state. This real-time monitoring is a continuous process, which starts after the load reduction exit and continues to run until exit is detected or manually terminated.

[0073] In step S510, when step S500 determines that the user has exited the target sleep state, the reduced intensity values ​​of the transcranial microcurrent stimulation module and the audio output module at the time of exit are obtained. The reduced intensity value refers to the actual output intensity value of each module after the load reduction exit is performed in step S400. Since the output intensity of each module has decreased from its original value to a lower target value after step S400 is completed, this reduced intensity value is the intensity level that each module is outputting at the time of exit. In actual implementation, the control module reads the current output intensity value from the drive register, status register, or feedback detection circuit of each module as the starting reference for subsequent recovery operations.

[0074] In step S520, based on the reduced intensity value obtained in step S510, the output intensity of the transcranial microcurrent stimulation module and the audio output module is restored. Restoration refers to raising the output intensity of each module back to its reduced value at the time of exit, bringing it back to a level capable of effectively stimulating the user again. In practice, the restoration operation can start from the reduced intensity value and gradually increase the output intensity of each module within a preset time window until it reaches the original output intensity value before step S400 or reaches an effective stimulation level again; alternatively, it can start from the reduced intensity value and continuously increase the output intensity at an appropriate rate until the user's EEG state again meets the target sleep state conditions. The restoration rate can be determined according to a preset restoration strategy, and can be the same as, faster than, or slower than the deload rate.

[0075] This solution forms a complete closed-loop control chain by continuously monitoring the user's sleep state after de-energization and automatically restoring the output intensity when the system detects exit from the target sleep state. During actual sleep, users may briefly exit the target sleep state due to external interference, sleep cycle transitions, or physiological micro-awakening. If the stimulus has already de-energized and will not be reintroduced, the user may find it difficult to fall back asleep due to the lack of stimulation. This solution, through continuous EEG signal monitoring and exit determination, can automatically trigger a re-entry mechanism when the user briefly awakens or experiences sleep state fluctuations, effectively addressing the complexities of sleep state fluctuations during actual sleep. Furthermore, the recovery operation starts from the reduced intensity value at the time of exit, rather than from zero or full values, avoiding disturbance to the user due to sudden intensity changes or missing the optimal intervention opportunity due to inefficient re-energization, achieving orderly connection and smooth re-entry of stimulus intensity. In addition, this monitoring and recovery process can be executed multiple times in a loop; that is, if the user re-enters the target sleep state after one recovery, the system will again de-energize and continue monitoring, thus achieving multiple adaptive loops within a single use.

[0076] Preferably, step S520 includes: S521. Obtain the reduced intensity values ​​of the transcranial microcurrent stimulation module and the audio output module at the exit time, and use them as the initial intensity values ​​for recovery. S522. Starting from the initial recovery intensity value, according to the preset recovery strategy, control the output intensity of the transcranial microcurrent stimulation module and the audio output module to gradually recover to the output intensity value before step S400.

[0077] In step S521, the reduced intensity values ​​of the transcranial microcurrent stimulation module and the audio output module at the exit time are obtained, and these values ​​are used as the recovery starting intensity value. The reduced intensity value refers to the actual output intensity value of each module after the load reduction exit is performed in step S400. This value has been obtained and temporarily stored in the storage unit of the control module in step S510. The recovery starting intensity value refers to the starting point of subsequent recovery operations, that is, the output intensity of each module is gradually increased from this intensity value. In actual implementation, the control module assigns the reduced intensity value obtained in step S510 to the recovery starting intensity variable as the starting reference of the recovery process. This step, by clarifying the starting point of recovery, makes the recovery operation no longer dependent on a preset fixed starting value, but dynamically determined according to the actual intensity level of each module at the exit time.

[0078] In step S522, starting from the recovery initiation intensity value determined in step S521, the output intensity of the transcranial microcurrent stimulation module and the audio output module is gradually restored to the output intensity value before step S400 is executed, according to a preset recovery strategy. The preset recovery strategy refers to the output intensity variation rules over time, pre-set and stored in the control module, which defines the recovery rate, step size, or change curve, corresponding to but opposite in direction to the deload strategy in step S420. In actual implementation, the control module periodically updates the output intensity commands of each module according to the time intervals and increments defined in the recovery strategy, causing the output intensity to gradually increase from the recovery initiation intensity value until it reaches the output intensity value before step S400 is executed. The recovery rate can be the same as, faster than, or slower than, depending on the setting of the recovery strategy. The recovery strategy can be executed based on a fixed time pattern or dynamically adjusted according to the user's current EEG state. The output intensity value before step S400 refers to the stable output intensity level established in steps S100 and S200 before the load reduction exit in step S400. This value has been stored in the control module as a reference before step S400 is executed. In step S522, the control module calls this stored value as the target endpoint for recovery. The recovery process can be linearly increasing, stepwise increasing, exponentially increasing, or a combination of the above methods, as long as the output intensity shows an overall upward trend over time.

[0079] This solution further clarifies the initial recovery intensity value and the target recovery endpoint value, and introduces a preset recovery strategy as the control rule for the recovery process, transforming the recovery operation from a simple functional description into an implementable technical solution with complete control logic. The initial recovery intensity value directly uses the reduced intensity value at the exit time instead of a preset value or zero value, ensuring that the initial state of the recovery operation is consistent with the actual hardware state and avoiding output jumps that may result from a mismatch between the preset value and the actual state. Using the output intensity value before step S400 is executed as the recovery endpoint ensures that the stimulation level after recovery is consistent with the effective stimulation level before deload, guaranteeing sufficient stimulation intensity to regain its regulatory effect after re-entry. The preset recovery strategy provides an adjustable control framework for the recovery process, allowing the recovery rate and recovery curve to be flexibly configured according to different usage scenarios or user needs, improving the adaptability and controllability of the recovery operation.

[0080] In the description of this specification, the terms "Embodiment 1," "this embodiment," or "in one embodiment," etc., indicate that the specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example; moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in one or more embodiments or examples.

[0081] In the description of this specification, the terms "connection," "installation," "fixing," "setting," and "having" are interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0082] In the description of this specification, relational terms such as “first” and “second” are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0083] The above description of the embodiments is intended to enable those skilled in the art to understand and apply the technology of this invention. Those skilled in the art can easily make various modifications to these examples and apply the general principles described herein to other embodiments without creative effort. Therefore, this invention is not limited to the above embodiments. Modifications in the following situations should be within the scope of protection of this invention: ① New technical solutions implemented based on the technical solution of this invention and combined with existing common knowledge, where the technical effects of the new technical solution do not exceed the technical effects of this invention; ② Equivalent substitutions of some features of the technical solution of this invention using known technology, resulting in the same technical effects as those of this invention; ③ Extendable technical solutions based on the technical solution of this invention, where the substantive content of the extended technical solution does not exceed the technical solution of this invention; ④ Equivalent transformations made using the content of this specification and drawings, directly or indirectly applied to other related technical fields.

Claims

1. A transcranial microcurrent stimulation modulation method based on electroencephalographic feedback, characterized in that, include: S100: Start the audio output module to output pink noise, and at the same time send a light control command to the light output module; S200. While maintaining the output of pink noise from the audio output module, activate the transcranial microcurrent stimulation module to output microcurrent stimulation. S300: Collect the user's brainwave signals and determine whether the user has entered the target sleep state based on the brainwave signals; S400. When it is determined that the user has entered the target sleep state, the audio output module and the transcranial microcurrent stimulation module are controlled to gradually reduce the output intensity.

2. The transcranial microcurrent stimulation modulation method based on electroencephalographic feedback according to claim 1, characterized in that, Step S200 further includes: S210. Control the transcranial microcurrent stimulation module to output microcurrent stimulation at a first frequency parameter. S220. While maintaining the output of pink noise by the audio output module, the output frequency parameter of the transcranial microcurrent stimulation module is gradually transitioned from the first frequency parameter to the second frequency parameter, wherein the second frequency parameter is lower than the first frequency parameter.

3. The transcranial microcurrent stimulation modulation method based on electroencephalographic feedback according to claim 1, characterized in that, Step S300 includes: S310. Collect the user's EEG signal and extract at least one EEG feature parameter from the EEG signal; S320. The EEG characteristic parameters are compared with a preset sleep state determination threshold, and the user is determined to have entered the target sleep state based on the comparison result.

4. The transcranial microcurrent stimulation modulation method based on electroencephalographic feedback according to claim 1, characterized in that, Step S400 includes: S410. Obtain the current output intensity values ​​of the transcranial microcurrent stimulation module and the audio output module; S420. According to the preset load reduction strategy, control the output intensity of the transcranial microcurrent stimulation module and the audio output module to gradually reduce from the current output intensity value to the target intensity value.

5. The transcranial microcurrent stimulation modulation method based on electroencephalographic feedback according to claim 1, characterized in that, Step S200 further includes: S230. Extract the phase information of the EEG signal in real time; S240. Based on the phase information, control the phase of the microcurrent stimulation output by the transcranial microcurrent stimulation module to be synchronized with the target rhythm phase of the EEG signal.

6. The transcranial microcurrent stimulation modulation method based on electroencephalographic feedback according to claim 1, characterized in that, The procedure before step S100 also includes: S10. Acquire the user's baseline EEG signal in a resting state, and extract at least one baseline feature parameter from the baseline EEG signal; S20. Determine the initial output parameters of the transcranial microcurrent stimulation module based on the baseline characteristic parameters.

7. The transcranial microcurrent stimulation modulation method based on electroencephalographic feedback according to claim 1, characterized in that, Step S100 includes: S110. Collect the user's EEG signal and extract the real-time EEG feature parameters from the EEG signal; S120. Determine the output amplitude modulation value of the pink noise based on the real-time EEG characteristic parameters; S130. According to the output amplitude modulation value, control the audio output module to output amplitude-modulated pink noise, and at the same time send an optical control command to the optical output module.

8. The transcranial microcurrent stimulation modulation method based on electroencephalographic feedback according to any one of claims 1 to 7, characterized in that, Between step S300 and step S400, the following is also included: S350. Determine the user's sleep stage based on the electroencephalogram (EEG) signals; S360. Based on the sleep stage, determine the deviation between the current output parameters of the transcranial microcurrent stimulation module and the audio output module and the target output parameters corresponding to the sleep stage; S370. Adjust the output parameters of the transcranial microcurrent stimulation module and / or the audio output module according to the deviation.

9. The transcranial microcurrent stimulation modulation method based on electroencephalographic feedback according to any one of claims 1 to 8, characterized in that, The process further includes the following after step S400: S500: Monitor the user's brainwave signals in real time, and determine whether the user has exited the target sleep state based on the brainwave signals; S510. When it is determined that the user has exited the target sleep state, obtain the reduced intensity values ​​of the transcranial microcurrent stimulation module and the audio output module at the time of exit. S520. Based on the reduced intensity value, restore the output intensity of the transcranial microcurrent stimulation module and the audio output module.

10. The transcranial microcurrent stimulation modulation method based on electroencephalographic feedback according to claim 9, characterized in that, Step S520 includes: S521. Obtain the reduced intensity values ​​of the transcranial microcurrent stimulation module and the audio output module at the exit time, and use them as the restored initial intensity values; S522. Starting from the recovery initial intensity value, and following a preset recovery strategy, control the output intensity of the transcranial microcurrent stimulation module and the audio output module to gradually recover to the output intensity value before step S400.

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