Biological health system, physiological state regulation and control method, wearable device and storage medium

Abstract

The invention provides a biological health system, a physiological state regulation and control method, wearable equipment and a storage medium, and the biological health system comprises a detection assembly which is configured to collect a biological signal of a user in real time; a processing component configured to calculate a physiological state regulation parameter based on the phase of the biological signal, and generate a stimulation signal based on the physiological state regulation parameter; and the output component is configured to output the stimulation signal, so that the coupling effect of the biological signal and the stimulation signal regulates and controls the physiological state of the user. The biological signal of the user is acquired in real time, and the personalized physiological state regulation and control parameter of the user is calculated based on the phase of the biological signal to generate the stimulation signal in real time, so that the physiological state of the user is regulated and controlled through the coupling effect of the stimulation signal and the biological signal.

Description

Technical Field

[0001] The present application belongs to the field of bio-health technology, and in particular, relates to a bio-health system, a physiological state regulation method, a wearable device and a storage medium. Background Art

[0002] Physiological state, as an indispensable basic physiological need in human life, plays an important role in maintaining physical health, promoting psychological balance and restoring energy. A good physiological state can not only make people refreshed and energetic, but also is a key factor in maintaining the immune system, heart health and cognitive function. However, the fast-paced life, high-pressure work, popularization of electronic products and irregular lifestyles in modern society often cause many people to face physiological state problems.

[0003] At present, physiological state disorders mainly rely on drug intervention, which often leads to problems such as adverse reactions and drug resistance. In recent years, some health products that have appeared on the market are mainly divided into two categories: pure detection products and pure intervention products. There is no effective closed loop between detection and intervention, so it is difficult for users to achieve the expected use effect. Traditional physiological state monitoring products are mostly used in hospital environments. They are large in size and low in wearing comfort. They require professional operation and interpretation, which is not convenient for use in daily life. Smart bracelets and watches have large errors due to their detection methods and limitations in monitoring physiological signals. In addition, there are no effective intervention measures to improve the user's physiological state problems. Intervention products are mainly based on open-loop neural stimulation. Due to the lack of a mechanism for real-time feedback regulation based on actual biological activities, their effectiveness is greatly reduced. . Summary of the invention

[0004] The purpose of this application is to provide a bio-health system, a physiological state regulation method, a wearable device and a storage medium, which are used to solve the technical problem of how to regulate the user's biological activity state from a healthy to a physiological state in real time according to health needs.

[0005] In a first aspect, the present application provides a biohealth system, comprising: a detection component configured to collect biological signals of a user in real time; a processing component configured to calculate physiological state control parameters based on the phase of the biological signals, and generate stimulation signals based on the physiological state control parameters; an output component configured to output the stimulation signal so that the coupling effect of the biological signals and the stimulation signals regulates the physiological state of the user.

[0006] In an implementation of the first aspect, the processing component selects at least one target frequency band based on the biological signal, and calculates corresponding physiological state control parameters based on the phase of each target frequency band; the physiological state control parameters include physiological state control frequency, physiological state control phase and / or physiological state control amplitude.

[0007] In an implementation of the first aspect, the processing component calculates the corresponding physiological state regulation frequency and / or the physiological state regulation phase based on the phase of each target frequency band and the preset initial frequency of the stimulation signal.

[0008] In an implementation of the first aspect, the processing component calculates the corresponding physiological state regulation amplitude based on the phase of each target frequency band, the preset initial frequency and initial amplitude of the stimulation signal; wherein the initial amplitude corresponds to the amplitude of the target frequency band.

[0009] In an implementation of the first aspect, the processing component calculates the corresponding physiological state regulation amplitude based on the phase of each target frequency band, the preset initial frequency and initial amplitude of the stimulation signal; wherein the initial amplitude is adaptively adjusted with a preset adjustment value following the change of the physiological state stage.

[0010] In an implementation of the first aspect, the processing component generates the frequency of the stimulation signal in real time based on the frequency of each physiological state regulation, so that the stimulation signal is frequency-coupled with the biological signal; the processing component generates the phase of the stimulation signal in real time based on the phase of each physiological state regulation, so that the stimulation signal is phase-coupled with the biological signal; and / or the processing component generates the amplitude of the stimulation signal in real time based on the amplitude of each physiological state regulation, so that the stimulation signal is amplitude-coupled with the biological signal.

[0011] In an implementation of the first aspect, the processing component generates the amplitude of the stimulation signal in real time based on the multiple physiological state regulation amplitudes; wherein the processing component determines the ratio of the generation coefficients of the multiple physiological state regulation amplitudes, and generates the amplitude of the stimulation signal in real time based on the ratio of the generation coefficients of the multiple physiological state regulation amplitudes; the sum of the generation coefficients of the multiple physiological state regulation amplitudes is 1.

[0012] In an implementation of the first aspect, the processing component determines multiple generation coefficients of the physiological state regulation amplitudes; wherein the processing component respectively calculates the difference between the amplitudes of the multiple target frequency bands and the target amplitude, and determines the ratio of the generation coefficients of the multiple physiological state regulation amplitudes based on the ratio of different differences; the target amplitude corresponds to the target physiological state stage.

[0013] In an implementation of the first aspect, the processing component determines the generation coefficients of multiple physiological state regulation amplitudes; wherein the processing component obtains the proportions of multiple target frequency bands when the user is in different physiological state stages, and determines the proportions of the generation coefficients of multiple physiological state regulation amplitudes based on the proportions of multiple target frequency bands.

[0014] In an implementation of the first aspect, the processing component regulates the physiological state of the user through the coupling of the biological signal and the stimulation signal; wherein the processing component determines the induced phase according to the phase of the target frequency band and the evoked response potential delay of the stimulation signal; the output component outputs the stimulation signal so that the biological signal begins to couple with the stimulation signal at the induced phase, so as to achieve physiological state regulation of the user at the physiological state stage corresponding to the target frequency band.

[0015] In an implementation of the first aspect, the processing component acquires a target frequency; the target frequency corresponds to the target frequency band; the processing component acquires a first filtering phase offset and a second filtering phase offset of the target frequency; the first filtering phase offset is the phase offset of the biological signal after being processed by the first filter, the second filtering phase offset is the phase offset of the frequency domain signal after being processed by the second filter, and the frequency domain signal is acquired based on the biological signal; the processing component acquires the phase of the target frequency band based on the first filtering phase offset and the second filtering phase offset.

[0016] In an implementation manner of the first aspect, the processing component obtains a frequency spectrum feature corresponding to the target frequency band, and extracts a frequency value with the highest frequency energy as the target frequency based on the frequency spectrum feature.

[0017] In an implementation of the first aspect, the processing component initially selects the target frequency band and determines whether the user's physiological state stage has changed based on the biological signal; the processing component reselects the target frequency band when the user's physiological state stage has changed, so that the target frequency band corresponds to the user's current physiological state stage; or the processing component maintains the target frequency band unchanged when the user's physiological state stage has changed.

[0018] In an implementation of the first aspect, the processing component determines whether the user's physiological state stage has changed based on the biological signal; wherein, the processing component obtains at least one physiological state staging result of the user based on the biological signal within a preset time window, classifies and counts at least one of the physiological state staging results, obtains at least one staging counting result, determines the relationship between at least one of the staging counting results within the preset time window and any preset threshold, and if any of the staging counting results is higher than the preset threshold, it is determined that the user's physiological state stage has changed.

[0019] In an implementation of the first aspect, the processing component obtains at least one physiological state staging result of the user based on the biological signal within a preset time window; wherein, the processing component segments the biological signal within the preset time window according to time, and obtains at least one physiological state staging result within the preset time window based on the biological signal within multiple time periods.

[0020] In an implementation of the first aspect, the biological signal includes an electroencephalogram signal, a cortical electroencephalogram signal, a deep electrode signal, an electrooculogram signal, an electromyography signal, an electrocardiogram signal, a magnetic resonance imaging signal and / or a near-infrared brain function imaging signal.

[0021] In an implementation manner of the first aspect, the stimulation signal is a health stimulation signal, including an acoustic stimulation signal, a light stimulation signal, an electrical stimulation signal, an ultrasonic stimulation signal, a magnetic stimulation signal and / or a vibration stimulation signal.

[0022] In a second aspect, the present application provides a physiological state regulation method, comprising: acquiring a user's biological signal in real time; calculating a physiological state regulation parameter based on the phase of the biological signal; and generating a stimulation signal based on the physiological state regulation parameter, so as to regulate the user's physiological state through the coupling effect of the biological signal and the stimulation signal.

[0023] In a third aspect, the present application provides a wearable device comprising: one or more sensors configured to collect biological signals of a user in real time; one or more processors; and one or more memories, wherein the memories store computer readable codes, and when the computer readable codes are executed by the one or more processors, the functions of the bio-health system as described are implemented.

[0024] In a fourth aspect, the present application provides a computer-readable storage medium having instructions stored thereon, which, when executed by a processor, causes the processor to execute the physiological state regulation method as described.

[0025] As described above, the bio-health system, physiological state regulation method, wearable device and storage medium described in this application have the following beneficial effects: The present application obtains the user's biological signals in real time through a closed-loop neural stimulation method, calculates the user's personalized physiological state control parameters based on the phase of the real-time biological signals, and generates the frequency, phase and / or amplitude of the stimulation signal in real time, so that the stimulation signal is frequency-coupled, phase-coupled and / or amplitude-coupled with the biological signal, thereby realizing the physiological state control of the user through the coupling effect of the stimulation signal and the biological signal.

[0026] When generating the amplitude of the stimulation signal based on the physiological state regulation amplitude, the present application determines the generation coefficient ratio of the physiological state regulation amplitude in a personalized manner, so that the stimulation signal amplitude generated thereby can regulate the physiological state of the user in a more targeted manner.

[0027] The present application accurately extracts the phase information of the biological signal and determines the induced phase based on it, so that the stimulation signal and the biological signal begin to couple at the induced phase, thereby improving the synchronization and stability between the neural stimulation and the user's internal brain, muscle, eye and / or heart activities, thereby improving the user's physiological health. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1A Shown is a schematic diagram of an implementation structure of the biohealth system described in this application.

[0029] Figure 1B Shown is a schematic diagram of an application scenario of the physiological state regulation method described in this application.

[0030] Figure 1C Shown is a schematic diagram of another application scenario of the physiological state regulation method described in this application.

[0031] Figure 2 Shown is a schematic flow chart of a physiological state regulation method according to an embodiment of the present application.

[0032] Figure 3 Shown is a schematic flow chart of a physiological state regulation method according to an embodiment of the present application.

[0033] Figure 4 Shown is a schematic flow chart of a physiological state regulation method according to an embodiment of the present application.

[0034] Figure 5 Shown is a schematic flow chart of a physiological state regulation method according to an embodiment of the present application.

[0035] Figure 6Shown is a schematic flow chart of a physiological state regulation method according to an embodiment of the present application.

[0036] Figure 7 A schematic diagram showing the application of the physiological state regulation method described in an embodiment of the present application in one embodiment.

[0037] Figure 8 Shown is a schematic structural diagram of an embodiment of the bio-health system described in an embodiment of the present application in a sleep scene.

[0038] Fig. 9 Shown is a schematic diagram of the internal structure of a wearable device in an embodiment of the present application. DETAILED DESCRIPTION

[0039] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0040] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0041] In addition, in this application, descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0042] At present, the fast-paced life, high-pressure work, popularization of electronic products and irregular lifestyles in modern society often cause many people to face physiological state problems. Facing physiological state problems for a long time will seriously affect physical and mental health. For this reason, there are many physiological state intervention products on the market, such as smart bracelets or watches that can detect and track physiological states, smart sleep-aiding lights, smart aromatherapy, etc. to improve the user's physiological state environment and enhance the user's physiological state experience. However, these products lack personalized physiological state regulation solutions and lack real-time feedback regulation, which greatly reduces the effectiveness of physiological state regulation.

[0043] In order to at least solve the above-mentioned technical problems, the embodiments of the present application provide a physiological state regulation method, which can generate a stimulation signal in real time according to the biological signal of the user, and make the stimulation signal and the biological signal begin to couple at a certain induced phase, thereby regulating the physiological state of the user, so as to improve the synchronization and stability between the stimulation signal and the internal activities of the brain, muscles, eyes and / or heart, and thus improve the user's physiological health state.

[0044] The present application also provides a biological health system that can implement the physiological state regulation method. The structure of the system is as follows: Figure 1A The bio-health system 1 comprises a detection component 11 , a processing component 12 , an output component 13 and a power supply component 14 .

[0045] The detection component 11 is configured to collect biological signals of the user in real time and send the collected biological signals to the processing component 12. The detection component 11 can be a wearable biological signal collection device or a sensor device, and the wearable biological signal collection device can be in any form such as a headband, a head cap, a head ring, an eye mask, etc., and this application does not impose any restrictions on this.

[0046] The processing component 12 is configured to calculate the physiological state control parameters based on the phase of the biological signal, and generate a stimulation signal based on the physiological state control parameters. The processing component 12 can be an embedded hardware system or any type of chip with a processor. The processing component 12 can be integrated with the detection component 11 into a wearable control device to execute the physiological state control method provided in the embodiment of the present application. In other implementations, the processing component 12 can also be a separate hardware system that interacts with the detection component 11 to receive the biological signal collected by the detection component 11, and the present application does not impose any restrictions on this.

[0047] The output component 13 is configured to output the stimulation signal so that the coupling effect of the biological signal and the stimulation signal regulates the physiological state of the user. After the processing component 12 generates the stimulation signal, the processing component 12 sends the stimulation signal to the output component 13, and the output component 13 outputs the stimulation signal to the user's brain at the induced phase, and through the coupling effect of the stimulation signal and the biological signal, the physiological state of the user is regulated. The output component 13 may include a stimulation element and a stimulation drive device. For example, when generating a sound wave stimulation signal, the stimulation element may be a speaker or a bone conduction vibrator, and the stimulation drive device may be an audio drive device. With respect to the specific structure of the output component 13, this application does not impose any restrictions on this.

[0048] The power supply assembly 14 provides power for the entire bio-health system 1 .

[0049] The software component 15 can be a GUI interface of the bio-health system 1, or an App application that interacts with the processing component, deployed on any mobile terminal, including smart phones, smart watches, PADs, tablet computers or other types of smart wearable devices, etc., or deployed on a web page. This application does not impose any restrictions on this.

[0050] In addition, the processing component 12 can also send the biological signal data collected during the user's physiological state period to the software component 15, and the software component 15 will reprocess and analyze the data to output a detailed physiological state report so that the user can check his or her personal physiological state in a timely manner.

[0051] It should be noted that Figure 1A The structure of the bio-health system 1 shown in the figure is provided as an example only. In fact, each component of the bio-health system 1 can exist physically separately, or two or more components can be integrated into one component to perform some or all steps in the physiological state regulation method.

[0052] The bio-health system described in the embodiment of the present application is described in detail below. The bio-health system at least includes a detection component, a processing component and an output component.

[0053] The detection component is configured to collect biological signals of the user in real time, wherein the biological signals include electroencephalogram signals, cortical electroencephalogram signals, deep electrode signals, electrooculogram signals, electromyography signals, electrocardiogram signals, magnetic resonance imaging signals and / or near-infrared brain imaging signals.

[0054] Electroencephalogram (EEG) is a method of recording the electrical activity of brain neurons by placing electrodes on the scalp. The characteristics of EEG signals mainly include amplitude, phase and frequency. The amplitude of EEG signals refers to the strength or size of the signal, usually measured in microvolts (μV). The amplitude of EEG signals varies in different brain regions and under different physiological or pathological conditions. The frequency of EEG signals refers to the oscillation rate of the signal, usually measured in Hertz (Hz). The frequency of EEG signals can be divided into different bands, such as delta waves (0.5-4 Hz), theta waves (4-8 Hz), alpha waves (8-13 Hz), beta waves (13-30 Hz) and gamma waves (above 30 Hz). These bands are related to different functional states of the brain. The phase of EEG signals refers to the position of the signal waveform in time. In EEG signals, the phase can reflect the temporal relationship between signals recorded by different electrodes, which is very important for studying the synchronization between brain regions.

[0055] Electrocorticographic (ECoG) signals are electrical activity recorded by placing electrodes on the surface of the cerebral cortex with high spatial and temporal resolution. The amplitude of the ECoG signal reflects the strength of the electrical signal generated by neuronal activity. The amplitude of the ECoG signal varies in different brain regions and under different physiological or pathological conditions. For example, the amplitude of the local field potential (LFP) and event-related potentials (ERPs) can provide important information about the activity of neuronal populations. The phase of the ECoG signal reflects the temporal relationship between the signals recorded by different electrodes in the ECoG signal. Phase synchronization, especially the connection pattern between brain regions detected by ECoG, is very important for studying the synchronization and functional connectivity between brain regions. In addition, phase-amplitude coupling (PAC) is an important feature in the ECoG signal, which is of great significance for the diagnosis of neurological diseases and deciphering the connectivity of neural networks. The frequency of the ECoG signal reflects the dynamic changes of brain activity. The ECoG signal can be analyzed in multiple frequency bands, including from low-frequency delta waves (1-4 Hz) to high-frequency gamma waves (above 30 Hz). ECoG signals in different frequency bands are associated with different brain functional states.

[0056] Deep electrode signals (SEEG), such as local field potentials (LFPs) and microelectrode recordings, are captured by electrodes implanted deep in the brain. These signals provide direct information about neural activity in the brain and are particularly useful in studying neuronal networks and neurological diseases such as epilepsy. The amplitude of deep electrode signals is usually larger because they are closer to the source of neuronal activity. The amplitude of these signals can reflect the strength of neuronal population activity and is usually measured in microvolts (μV). For example, LFP signals have a larger amplitude and show high-frequency firing patterns more clearly than EEG. The phase of deep electrode signals reflects the temporal relationship between signals recorded by different electrodes. This phase information is very important for studying synchronization and functional connectivity between brain regions. The frequency of deep electrode signals reflects the dynamic changes in brain activity. These signals can be analyzed in multiple frequency bands, including low-frequency delta waves (1-4 Hz) to high-frequency gamma waves (above 30 Hz). Deep electrode signals in different frequency bands are associated with different brain functional states. For example, high-frequency power is spatially restricted: it increases in layers with high cell body density and axon terminals. Furthermore, high-frequency power primarily reflects spike activity and co-varies with LFP components that originate from postsynaptic potentials and other spike-independent membrane voltage fluctuations.

[0057] Electrooculogram (EOG) is an objective and quantitative retinal function test method that detects the slow changes in the electrostatic potential of the eye as it changes with light adaptation. It reflects the function of the retinal pigment epithelium-photoreceptor complex and persists. The amplitude of the electrooculogram usually refers to the strength or size of the signal. In the electrooculogram (EOG), the change in amplitude reflects the functional state of the retinal pigment epithelium-photoreceptor complex. For example, during dark adaptation, the resting potential gradually decreases to the lowest point (dark valley potential), and then gradually rises to the highest point (light peak potential) during light adaptation. These changes are recorded by electrodes placed on the skin of the inner and outer canthi and used to evaluate retinal function. The phase of the electrooculogram may be related to the potential changes during light adaptation, which are related to the response of the retina to light. The frequency of electrooculogram signals is usually low because they are related to the light adaptation process of the retina, which is a relatively slow physiological process. Changes in the EOG typically range from 0.01 to 0.1 Hz. These changes, called spontaneous activity transients (SATs), are particularly important in premature infants because they are critical for the formation of neuronal connections during early, immature stages.

[0058] Electromyography (EMG) is the measurement of electrical signals that trigger muscle contraction and reflect the degree of muscle contraction. The amplitude of the electromyographic signal reflects the strength or size of the signal. In electromyography (EMG), changes in amplitude reflect the degree of muscle activity. The higher the amplitude of the signal, the more action potentials are superimposed at the same time, and the more muscle fibers are activated. The phase of the electromyographic signal reflects the temporal relationship between the signals recorded by different electrodes. When analyzing the activation time of the muscle, the type of muscle contraction is usually not of concern, but only when the muscle contracts and relaxes. Phase information can be used to determine the precise time point of muscle activation. The frequency of the electromyographic signal reflects the dynamic changes in brain activity. The energy distribution of the electromyographic signal is basically in the frequency range of 0 ~ 500 Hz, and the main component is in the range of 50 ~ 150 Hz. Frequency analysis can reveal the complexity of muscle activity, including the degree of muscle fatigue. For example, the median frequency (MF) and mean power frequency (MPF) are obtained by analyzing the power spectral density of the surface electromyographic signal, and are usually used to evaluate the strength and fatigue of muscle contraction. These frequency parameters decrease when the muscle is under greater load or fatigue, and increase when the muscle is under less load or is more relaxed.

[0059] The electrocardiogram (ECG) is a graph that records changes in cardiac electrical activity through the body surface, which reflects the electrophysiological process of the heart. The amplitude of the ECG signal is usually around 1mV, reflecting the potential changes generated during the depolarization and repolarization of the heart. Each band in the ECG, such as the P wave, QRS complex wave and T wave, has a specific amplitude range. For example, the amplitude of the P wave usually does not exceed 0.25mV, the amplitude of the QRS complex wave varies greatly, and the amplitude of the T wave should not be less than 1 / 10 of the R wave in the same lead. The phase of the ECG signal usually refers to a specific point of the waveform, such as the peak, trough and interval. Each cardiac cycle in the ECG consists of a series of regular waveforms, including the P wave, QRS complex wave and T wave. The starting point, end point, peak, trough and interval of these waveforms record detailed information about the state of cardiac activity. For example, the P wave represents the depolarization of the atrium, the QRS complex wave represents the depolarization of the ventricle, and the T wave represents the repolarization of the ventricle. The frequency of the ECG signal reflects the dynamic changes of cardiac activity. The frequency range of ECG signals is approximately 0.05Hz to 100Hz, with the signal energy mainly concentrated in the range of 0.5Hz to 45Hz. Changes in heart rate directly affect the frequency components of ECG signals, and the normal heart rate range is approximately 60 to 100 beats per minute. Spectral analysis of ECG signals can help identify the characteristics of heart disease, such as arrhythmia and myocardial ischemia.

[0060] Functional magnetic resonance imaging (fMRI) is a research method that stimulates specific senses to induce neural activity (functional area activation) in the corresponding parts of the cerebral cortex and displays it through magnetic resonance images. Functional magnetic resonance imaging (fMRI) signals are an important brain functional imaging technology that studies brain function by measuring hemodynamic changes caused by neuronal activity. By detecting changes in fMRI signals, the neural activity of the brain can be indirectly reflected. The main characteristics of fMRI signals include amplitude, phase and frequency. The amplitude of fMRI signals is usually related to the intensity of BOLD signals, reflecting the changes in blood flow in specific brain areas when performing tasks or in a resting state. The phase information of fMRI signals contains timing information related to brain functional activities. The frequency components of fMRI signals reflect the dynamic changes of brain activity. Among them, resting state fMRI (rs-fMRI) signals can be decomposed into several inherent frequency clusters, which are related to different physiological processes, such as breathing, pulse, metabolic processes and vasomotor activity.

[0061] Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique that studies brain function by measuring changes in the concentrations of oxygenated hemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) in the blood caused by brain activity. The main characteristics of fNIRS signals include amplitude, phase, and frequency. The amplitude of fNIRS signals usually refers to the degree of change in the concentrations of oxygenated hemoglobin and deoxyhemoglobin. When analyzing the time domain characteristics of fNIRS signals, the phase of fNIRS signals may be considered, especially when studying the time domain drift and baseline correction of signals. The frequency of fNIRS signals reflects the dynamic changes of brain activity. Among them, the resting fNIRS signal can be decomposed into several inherent frequency clusters, which are related to different physiological processes, such as respiration, pulse, metabolic processes, and vasomotor activity. In addition, frequency domain analysis of fNIRS signals can also reveal low-frequency oscillations (LFOs) of brain activity, which are related to changes in brain hemodynamics.

[0062] The protection scope of the present application is not limited to the types of biological signals listed in this embodiment. All biological signals that can be directly expanded and implemented based on the technical principles of the present application are included in the protection scope of the present application.

[0063] The processing component is configured to calculate a physiological state control parameter based on the phase of the biological signal, and to generate a stimulation signal based on the physiological state control parameter. The stimulation signal is a healthy stimulation signal, including an acoustic stimulation signal, a light stimulation signal, an electrical stimulation signal, an ultrasonic stimulation signal, a magnetic stimulation signal and / or a vibration stimulation signal. The protection scope of the present application is not limited to the types of stimulation signals listed in this embodiment, and all stimulation signals that can be directly expanded and implemented based on the technical principles of the present application are included in the protection scope of the present application.

[0064] The output component is configured to output the stimulation signal so that the coupling effect between the biological signal and the stimulation signal can regulate the physiological state of the user.

[0065] The coupling effect of the EEG signal and the stimulation signal can realize the regulation of the user's brain state.

[0066] The coupling effect of the cortical electroencephalogram signal and the stimulation signal can realize the regulation of the user's brain state.

[0067] The coupling effect of the deep electrode signal and the stimulation signal can regulate the user's brain state.

[0068] The coupling effect of the electrooculogram signal and the stimulation signal can realize the regulation of the user's eye movement state.

[0069] The coupling effect of the electromyographic signal and the stimulation signal can realize the regulation of the muscle activity state of the user.

[0070] The coupling effect of the electrocardiogram signal and the stimulation signal can realize the regulation of the user's sympathetic nerves and parasympathetic nerves.

[0071] The coupling effect of the magnetic resonance functional imaging signal and the stimulation signal can realize the regulation of the user's brain state.

[0072] The coupling effect of the near-infrared brain function imaging signal and the stimulation signal can realize the regulation of the user's brain state.

[0073] The regulation of one or more of the above physiological states can be applied to improve different physiological problems or diseases, including: through the coupling of EEG signals and stimulation signals, brain state regulation such as sleep regulation, emotion regulation and / or mental illness regulation can be achieved, for example, the regulation of depression, mania, autism, schizophrenia and other diseases can be achieved; through the coupling of EMG signals and stimulation signals, sleep regulation (abnormal body movements), Parkinson's disease regulation, etc. can be achieved; through the coupling of ECG signals and stimulation signals, sleep regulation, emotion regulation, heart disease treatment regulation, etc. can be achieved; through the coupling of EOG signals and stimulation signals, sleep regulation, dream regulation, eye spasm treatment regulation, etc. can be achieved.

[0074] In one embodiment of the present application, the processing component selects at least one target frequency band based on the biological signal, and calculates the corresponding physiological state control parameters based on the phase of each target frequency band; the physiological state control parameters include physiological state control frequency, physiological state control phase and / or physiological state control amplitude.

[0075] In one embodiment of the present application, the processing component calculates the corresponding physiological state regulation frequency and / or the physiological state regulation phase based on the phase of each target frequency band and the preset initial frequency of the stimulation signal.

[0076] In one embodiment of the present application, the processing component calculates the corresponding physiological state regulation amplitude based on the phase of each target frequency band, the preset initial frequency and initial amplitude of the stimulation signal; wherein the initial amplitude corresponds to the amplitude of the target frequency band.

[0077] In one embodiment of the present application, the processing component calculates the corresponding physiological state regulation amplitude based on the phase of each target frequency band, the preset initial frequency and initial amplitude of the stimulation signal; wherein the initial amplitude is adaptively adjusted with a preset adjustment value following the changes in the physiological state stage.

[0078] In one embodiment of the present application, the processing component generates the frequency of the stimulation signal in real time based on the frequency of each physiological state regulation, so that the stimulation signal is frequency-coupled with the biological signal; the processing component generates the phase of the stimulation signal in real time based on the phase of each physiological state regulation, so that the stimulation signal is phase-coupled with the biological signal; and / or the processing component generates the amplitude of the stimulation signal in real time based on the amplitude of each physiological state regulation, so that the stimulation signal is amplitude-coupled with the biological signal.

[0079] In one embodiment of the present application, the processing component generates the amplitude of the stimulation signal in real time based on the multiple physiological state regulation amplitudes; wherein the processing component determines the ratio of the generation coefficients of the multiple physiological state regulation amplitudes, and generates the amplitude of the stimulation signal in real time based on the ratio of the generation coefficients of the multiple physiological state regulation amplitudes; the sum of the generation coefficients of the multiple physiological state regulation amplitudes is 1.

[0080] In one embodiment of the present application, the processing component determines the generation coefficients of the multiple physiological state regulation amplitudes; wherein the processing component respectively calculates the difference between the amplitudes of the multiple target frequency bands and the target amplitude, and determines the ratio of the generation coefficients of the multiple physiological state regulation amplitudes based on the ratio of different differences; the target amplitude corresponds to the target physiological state stage.

[0081] In one embodiment of the present application, the processing component determines the generation coefficients of multiple physiological state regulation amplitudes; wherein, the processing component obtains the proportions of multiple target frequency bands when the user is at different physiological state stages, and determines the proportions of the generation coefficients of multiple physiological state regulation amplitudes based on the proportions of multiple target frequency bands.

[0082] In one embodiment of the present application, the processing component regulates the physiological state of the user through the coupling of the biological signal and the stimulation signal; wherein the processing component determines the induced phase according to the phase of the target frequency band and the evoked response potential delay of the stimulation signal; the output component outputs the stimulation signal so that the biological signal begins to couple with the stimulation signal at the induced phase, so as to achieve physiological state regulation of the user at the physiological state stage corresponding to the target frequency band.

[0083] In one embodiment of the present application, the processing component obtains a target frequency; the target frequency corresponds to the target frequency band; the processing component obtains a first filtering phase offset and a second filtering phase offset of the target frequency; the first filtering phase offset is the phase offset of the biological signal after being processed by the first filter, the second filtering phase offset is the phase offset of the frequency domain signal after being processed by the second filter, and the frequency domain signal is obtained based on the biological signal; the processing component obtains the phase of the target frequency band based on the first filtering phase offset and the second filtering phase offset.

[0084] In one embodiment of the present application, the processing component obtains the frequency spectrum characteristics corresponding to the target frequency band, and extracts the frequency value with the highest frequency energy as the target frequency based on the frequency spectrum characteristics.

[0085] In one embodiment of the present application, the processing component initially selects the target frequency band and determines whether the user's physiological state stage has changed based on the biological signal; the processing component reselects the target frequency band when the user's physiological state stage has changed, so that the target frequency band corresponds to the user's current physiological state stage; or the processing component keeps the target frequency band unchanged when the user's physiological state stage has changed.

[0086] In one embodiment of the present application, the processing component determines whether the user's physiological state stage has changed based on the biological signal; wherein, the processing component obtains at least one physiological state stage result of the user based on the biological signal within a preset time window, classifies and counts at least one of the physiological state stage results, obtains at least one stage counting result, determines the relationship between at least one of the stage counting results within the preset time window and any preset threshold value, and if any of the stage counting results is higher than the preset threshold value, it is determined that the user's physiological state stage has changed.

[0087] In one embodiment of the present application, the processing component obtains at least one physiological state staging result of the user based on the biological signal within a preset time window; wherein, the processing component segments the biological signal within the preset time window according to time, and obtains at least one physiological state staging result within the preset time window based on the biological signal within multiple time periods.

[0088] The embodiment of the present application also provides a physiological state regulation method, which can be implemented by the bio-health system, but the implementation device of the physiological state regulation method includes but is not limited to the structure of the bio-health system described in this embodiment. The physiological state regulation method includes: acquiring the biological signal of the user in real time; calculating the physiological state regulation parameter based on the phase of the biological signal; generating a stimulation signal based on the physiological state regulation parameter, so as to regulate the physiological state of the user through the coupling effect of the biological signal and the stimulation signal.

[0089] In some other embodiments, the physiological state regulation method described in the present application can be applied to terminal-cloud interaction scenarios. Figure 1B The following is a schematic diagram of the structure of the client-cloud interaction scenario in these implementations. Figure 1B As shown, the end-cloud interaction system 2 includes a wearable control device 20 and a cloud server 21. The wearable control device 20 and the cloud server 21 can communicate with each other, and the communication method is not limited to wired or wireless.

[0090] When the user is ready to enter a certain physiological state, he can put on the wearable control device 20. At this time, the wearable control device 20 will collect the user's biological signals in real time and send the biological signals to the cloud server 21. The cloud server 21 will process the biological signals and control the wearable control device 20 to output stimulation signals to the user, so that the user's biological signals are coupled with the stimulation signals, and the user's physiological state is regulated through the coupling effect of the two. In addition, the cloud server 21 will also store the biological signal data and physiological state regulation data within the user's physiological state cycle.

[0091] In some other embodiments, the physiological state regulation method described in the present application can be applied to another terminal-cloud interaction scenario. Figure 1C The following is a schematic diagram of the structure of the client-cloud interaction scenario in these implementations. Figure 1C As shown, the end-cloud interaction system 2 includes a wearable control device 20, a cloud server 21 and a host computer 22. The wearable control device 20, the cloud server 21 and the host computer 22 can communicate with each other, and the communication method is not limited to wired or wireless.

[0092] At this time, when the user wears the wearable control device 20, the wearable control device 20 can be controlled by the host computer 22 to collect the user's biological signals in real time, and execute the physiological state control method provided in the embodiment of the present application to generate a stimulation signal. At the same time, the biological signal data and physiological state control data within the user's physiological state cycle can be uploaded to the host computer 22 via Bluetooth or other communication methods, and then transmitted to the cloud server 21 through the host computer for storage.

[0093] It should be noted that the cloud server 21 may include one or more servers, or one or more processing nodes, or one or more virtual machines running on the server. The cloud server 21 may also be called a server cluster, a management platform, a physiological state control center, etc. This application does not impose any restrictions on this.

[0094] The following embodiments of the present application provide a method for regulating physiological state, and the method for regulating physiological state can be applied to Figure 1A The bio-health system shown, or applied to Figure 1B and Figure 1C In the end-cloud interaction scenario shown in Figure 1, it should be noted that Figures 1A to 1C It is provided as an example only. In fact, the present application does not impose any restrictions on the execution subject of some or all steps in the physiological state regulation method described in the following embodiments of the present application.

[0095] The following will take the EEG signal in the biological signal as an example, combined with the drawings in the embodiments of the present application, to describe in detail the technical solution in the embodiments of the present application.

[0096] Figure 2 The flowchart of the physiological state regulation method described in the embodiment of the present application is shown as follows: Figure 2 As shown, the physiological state regulation method includes steps S1 to S3.

[0097] S1. Obtain the user's EEG signal in real time.

[0098] Specifically, the user's EEG signals are collected in real time. The collection area includes frontal electrodes Fp1, Fp2, F7, F8, Fpz, temporal lobe electrodes Tp9, Tp10, central electrodes C3, C4, Cz and occipital electrodes O1, O2, Oz.

[0099] In some embodiments, frontal electrodes Fp1, Fp2, F7, and F8 are selected to collect the user's EEG signals. The EEG signals include α wave, β wave, θ wave, and δ wave signals. Different EEG signals represent different activity states of the user's brain, for example, β wave corresponds to the excited state of the user's brain, α wave corresponds to the awake state of the user's brain, θ wave corresponds to the user's light sleep state, and δ wave corresponds to the user's deep sleep state.

[0100] Furthermore, in some embodiments, after the user's EEG signal is collected, it can also be preprocessed to improve the signal-to-noise ratio of the EEG signal, thereby improving the signal quality. For example, the collected EEG signal can be re-referenced, filtered, detrended, etc. Among them, the filtering process can use a 0.1-45Hz bandpass filter (such as a second-order Butterworth filter) to filter the collected EEG signal, and use a notch filter to filter out the interference of the 50Hz power frequency and its harmonics. With regard to the specific preprocessing method, this application is not limited to this.

[0101] S2. Calculating a sleep regulation parameter (one of the physiological state regulation parameters) based on the phase of the EEG signal.

[0102] In order to enable the stimulation signal to be coupled with the EEG signal and improve the synchronization and stability between the stimulation signal and the EEG signal, the present application first calculates the sleep control parameters in real time according to the phase of the user's EEG signal, so as to further generate a stimulation signal based on this, thereby achieving the stimulation signal being modulated by the phase of the EEG signal. Figure 3 The flowchart of the sleep regulation method described in the embodiment of the present application is shown as follows: Figure 3 As shown, the calculation of sleep regulation parameters based on the phase of the EEG signal includes step S21 and step S22.

[0103] S21. Select at least one target frequency band based on the EEG signal.

[0104] In some embodiments, the target frequency band can be determined according to the main EEG signal frequency of the next sleep stage of the user's sleep cycle. For example, when the user is in an awake state (Wake period) and hopes to transition to the N1 period through sleep regulation, the main EEG signal in this stage is α wave, so the enhanced α frequency band can be selected as the target frequency band. Alternatively, the β frequency band can also be considered as the target frequency band to suppress the activity of β waves in the brain through sleep regulation. Alternatively, the θ frequency band can also be considered as the target frequency band to enhance the activity of θ waves in the brain through sleep regulation. Alternatively, the β frequency band and the θ frequency band can also be selected as target frequency bands at the same time. That is, whether a target frequency band or multiple target frequency bands are selected, the purpose is to calculate the corresponding sleep regulation parameters based on the phase of the target frequency band, and then modulate the stimulation signal in real time to help the user enter the next sleep stage in the sleep cycle as soon as possible, so as to improve the efficiency of falling asleep and the quality of sleep. Therefore, in the case of meeting this purpose, the specific selection of the target frequency band is not restricted in this application.

[0105] It should be noted that the sleep stages in a sleep cycle can be divided into Wake stage, N1 stage, N2 stage, N3 stage and REM stage. N3 stage and REM stage can also be divided into one stage according to the user's sleep cycle, indicating that the user is about to complete the first sleep cycle of falling asleep and will enter the second sleep cycle. At this time, the user's sleep regulation can no longer be performed. Therefore, the sleep stages in the user's sleep cycle can be divided according to the regulation needs, and this application does not impose any restrictions on this.

[0106] In some embodiments, when selecting a target frequency band, the corresponding target frequency band may be preset in advance according to the sleep stage in the sleep cycle, and the corresponding target frequency band may be automatically selected according to the change of the user's sleep stage. Figure 4 As shown, selecting at least one target frequency band based on the EEG signal includes steps S211 to S213.

[0107] S211: Initially select the target frequency band.

[0108] S212: Determine whether the user's sleep stage changes based on the EEG signal.

[0109] S213: If the sleep stage changes, reselect the target frequency band so that the target frequency band corresponds to the sleep stage.

[0110] For example, the sleep stages in the user's sleep cycle are divided into the first sleep stage, the second sleep stage, the third sleep stage and the fourth sleep stage. Among them, the first sleep stage corresponds to the Wake period, the second sleep stage corresponds to the N1 period, the third sleep stage corresponds to the N2 period, and the fourth sleep stage corresponds to the N3 period or the REM period. Then, the target frequency band of the first sleep stage can be determined to be the α frequency band, and it is initially selected. The target frequency band of the second sleep stage is the θ frequency band, and the target frequency band of the third sleep stage is the δ frequency band. When the user's sleep stage changes, the target frequency band corresponding to the changed sleep stage is selected, and when the user enters the fourth sleep stage, there is no need to perform sleep regulation. For another example, the target frequency band of the first sleep stage can also be set to the α frequency band and the θ frequency band, and it is initially selected. The target frequency band of the second sleep stage is set to the θ frequency band and the δ frequency band, and the target frequency band of the third sleep stage is set to the δ frequency band. When the user's sleep stage changes, the target frequency band corresponding to the current sleep stage is selected, and when the user enters the fourth sleep stage, there is no need to perform sleep regulation.

[0111] In this regard, the embodiment of the present application also provides an implementation method for determining whether the user's sleep stage has changed based on the EEG signal, so as to support the automatic selection of the corresponding target frequency band according to the change of the user's sleep stage provided by the above embodiment. Figure 5 As shown, judging whether the user's sleep stage has changed based on the EEG signal includes steps S2121 to S2123.

[0112] S2121. Obtain at least one sleep staging result of the user based on the EEG signal within a preset time window.

[0113] Specifically, the EEG signal within the preset time window is processed in segments according to time, and at least one sleep staging result within the preset time window is obtained based on the EEG signals within multiple segmented time periods.

[0114] In some embodiments, a 10-minute time window is preset, and 30-second segment processing is performed. Afterwards, multiple EEG signals within 30 seconds are inferred and judged to obtain corresponding sleep staging results. For example, the user is awake for 10 minutes, and EEG signals within 10 minutes are continuously obtained, and the sleep staging results within 20 30 seconds are judged, including Wake stage, REM stage, N1 stage, N2 stage or N3 stage.

[0115] Furthermore, a machine learning algorithm can be used to infer the EEG signal to obtain a sleep staging result. The machine learning algorithm used may include a neural network, an attention mechanism, a decision tree, a support vector machine, etc., but the present application is not limited thereto.

[0116] It should be noted that the specific length of the preset time window can be set as needed, and the present application is not limited to this.

[0117] S2122: Classify and count at least one of the sleep staging results to obtain at least one staging counting result.

[0118] Specifically, the sleep stage results in each time window are counted to obtain at least one stage counting result. Taking the above 10-minute time window as an example, there are 12 N1 stages, 5 Wake stages, and 3 N2 stages. The three stage counting results are 12, 5, and 3, respectively. In other embodiments, there may be 20 N1 stages, in which case only one stage counting result is 20.

[0119] S2123. Determine the relationship between at least one of the period counting results within the preset time window and any preset threshold value. If any of the period counting results is higher than the preset threshold value, determine that the sleep stage of the user has changed.

[0120] Specifically, the threshold judgment logic is applied to determine whether the user changes the sleep stage. That is, when the stage counting result is greater than the preset threshold, it can be determined that the user has entered the next sleep stage. For example, when the stage counting result of the N1 period in the past time window is greater than the preset threshold, it is determined that the user has entered the second sleep stage. When the stage counting result of the N2 period in the time window is greater than the preset threshold, it is determined that the user has entered the third sleep stage. When the stage counting result of the N3 period or the REM period in the time window is greater than the preset threshold, it is determined that the user has entered the fourth sleep stage.

[0121] S22. Calculate corresponding sleep regulation parameters based on the phase of each target frequency band; the sleep regulation parameters include sleep regulation frequency, sleep regulation phase and / or sleep regulation amplitude.

[0122] After at least one target frequency band is determined through step S21, the phase information corresponding to the target frequency band is extracted to calculate the sleep regulation parameters corresponding to the target frequency band through the phase of the target frequency band. That is, when a target frequency band is determined, the phase information of the target frequency band is extracted, and the corresponding sleep regulation frequency, sleep regulation phase and / or sleep regulation amplitude are calculated based on the phase of the target frequency band. Alternatively, when multiple target frequency bands are determined, the phase information of the multiple target frequency bands are extracted respectively, and the sleep regulation frequency, sleep regulation phase and / or sleep regulation amplitude corresponding to each target frequency band are calculated based on the phases of the multiple target frequency bands. In fact, for each target frequency band, the corresponding phase information must be extracted, and one or more of the corresponding sleep regulation frequency, sleep regulation phase and sleep regulation amplitude are calculated based on the phase information of each target frequency band.

[0123] The following will take a target frequency band as an example to illustrate the specific process of calculating the corresponding sleep regulation parameters based on the phase.

[0124] 1) Calculating a sleep regulation frequency based on the phase of the target frequency band.

[0125] Specifically, the initial frequency of the stimulation signal is preset, and the corresponding sleep regulation frequency is calculated based on the phase of the target frequency band and the initial frequency.

[0126] In some embodiments, the phase of the target frequency band is assumed to be , the initial frequency of the stimulation signal is , then the sleep regulation frequency can be expressed as: (1) It can be seen from formula (1) that the sleep regulation frequency will actually change in real time following the phase of the target frequency band. When multiple target frequency bands are determined in step S21, the corresponding sleep regulation frequency is calculated for each target frequency band using formula (1).

[0127] In some embodiments, the phase of the target frequency band is assumed to be , the initial frequency of the stimulation signal is , then the sleep regulation phase can be expressed as: (2) It can be seen from formula (1) that the sleep regulation phase will actually change in real time following the phase of the target frequency band. When multiple target frequency bands are determined in step S21, the corresponding sleep regulation phase is calculated for each target frequency band using formula (2).

[0128] 3) Calculating a sleep regulation phase based on the phase of the target frequency band.

[0129] Specifically, the initial frequency and initial amplitude of the stimulation signal are preset, and the corresponding sleep regulation amplitude is calculated based on the phase of each target frequency band, the initial frequency and the initial amplitude.

[0130] In some embodiments, the phase of the target frequency band is assumed to be , the initial frequency of the stimulation signal is , the initial amplitude is , then the sleep regulation amplitude can be expressed as: (3) It can be seen from formula (3) that the sleep control amplitude will actually change in real time following the phase of the target frequency band. Among them, B represents the parameter affecting the control amplitude by phase modulation. When multiple target frequency bands are determined by step S21, the corresponding sleep control amplitude is calculated for each target frequency band using formula (3).

[0131] Furthermore, in some embodiments, the initial amplitude That is, after determining a target frequency band according to step S21, the amplitude corresponding to the target frequency band can be obtained according to the EEG signal, and used as the initial amplitude. It should be noted that when multiple target frequency bands are determined, there will actually be multiple initial amplitudes. .

[0132] In other embodiments, the initial amplitude Adaptive adjustment is performed with a preset adjustment value as the sleep stage changes. That is, if the sleep stage changes, the initial amplitude Performs adaptive adjustment with preset adjustment values.

[0133] For example, the initial amplitude can be set directly based on the amplitude of the target frequency band. Afterwards, the initial amplitude will be adaptively adjusted according to the preset value as the user's sleep stage changes. For example, in the first sleep stage, the amplitude of the α frequency band can be directly set as the initial amplitude. After that, each time the user enters the next sleep stage, the initial amplitude will be adaptively adjusted downward by a percentage until it enters the fourth sleep stage, drops to the minimum or stops. For another example, when the user's sleep stage returns to the third sleep stage or the second sleep stage, the initial amplitude will be adjusted upward accordingly to help the user deepen his sleep. If the user wakes up during sleep and resumes a waking state, the initial amplitude will be adjusted upward accordingly to help the user fall asleep again. That is, the initial amplitude is adaptively adjusted to rise or fall according to the change of the user's sleep stage. Among them, the change of the sleep stage can be judged according to steps S2121 to S2123 in the above content.

[0134] Furthermore, for the phase of the target frequency band in equations (1) to (3), In particular, the present application embodiment provides a method for obtaining the phase of the target frequency band. Figure 6 As shown, obtaining the phase of the target frequency band includes steps S221 to S223.

[0135] S221. Acquire a target frequency; the target frequency corresponds to the target frequency band.

[0136] Specifically, the implementation method of acquiring the target frequency includes: acquiring a frequency spectrum feature corresponding to the target frequency band, and extracting a frequency value with the highest frequency energy as the target frequency based on the frequency spectrum feature.

[0137] In some embodiments, after filtering the EEG signal obtained in step S1, N-point sliding window processing is performed, and the frequency spectrum characteristics of the EEG signal in the target frequency band are calculated using fast Fourier transform, based on which the frequency value with the highest frequency energy in the target frequency band is extracted as the personal center frequency ICF, that is, the personal center frequency is used as the target frequency. That is, for each target frequency band, the corresponding target frequency can actually be obtained.

[0138] S222: Obtain a first filtering phase offset and a second filtering phase offset of the target frequency.

[0139] Specifically, the first filtering phase offset of the target frequency band is obtained. The first filtering phase shift It is the phase offset generated after the EEG signal is filtered by the first filter. In some embodiments, after the user's EEG signal is obtained through step S1, a 0.1-45Hz bandpass filter (such as a second-order Butterworth filter) can be used as the first filter to filter the collected EEG signal. At this time, the first filtering phase offset of the target frequency can be calculated according to the parameters of the 0.1-45Hz bandpass filter. .

[0140] Specifically, the second filtering phase offset of the target frequency band is obtained The second filtering phase offset is a phase offset of a frequency domain signal after being processed by the second filter, and the frequency domain signal is acquired based on the EEG signal. The second filter is a filter corresponding to a target frequency band.

[0141] In some embodiments, obtaining a second filtering phase offset The implementation method includes: after filtering the EEG signal obtained in step S1, performing N-point sliding window processing, and using fast Fourier transform to calculate the spectral characteristics of the EEG signal in the target frequency band, that is, X[f]. Then, performing Hilbert transform on X[f], setting the negative frequency component value to zero, and multiplying the positive frequency component by 2 to obtain Y[f]. Applying the second filter of the target frequency band to Y[f] to obtain the filtered frequency domain signal, and obtaining the amplitude and phase response of the pulse frequency according to the parameters of the filter, the second filtered phase offset of the target frequency can be obtained. .

[0142] S223: Acquire the phase of the target frequency band based on the first filtering phase offset and the second filtering phase offset.

[0143] Specifically, the filtered frequency domain signal is inversely transformed back to the time domain to obtain a complex valued signal Z[n]. The real-time phase angle is obtained by analyzing the complex valued signal Z[n], and based on this, the real-time phase angle is subtracted. and The phase information of the target frequency band can then be extracted.

[0144] In order to further illustrate the specific process of obtaining the phase of the target frequency band, the α band will be used as the target frequency band for explanation below. After determining the α band as the target frequency band, use a 0.1-45Hz bandpass filter to filter the collected EEG signal, and perform N-point sliding window processing on the filtered EEG signal, and use fast Fourier transform to calculate the spectral characteristics of the α band EEG signal, that is, X[f], and extract the frequency value with the highest frequency energy in the α band as the personal center frequency ICF. At this time, the first filtering phase offset of the personal center frequency ICF can be obtained according to the parameters of the 0.1-45Hz bandpass filter. After that, perform Hilbert transform on X[f], set the negative frequency component value to zero, multiply the positive frequency component by 2, and obtain Y[f]. Apply the α band (7.5-12.5Hz) filter to Y[f] to obtain the filtered frequency domain signal, and use it to obtain the second filtering phase offset of the personal center frequency ICF. Finally, the filtered frequency domain signal is inversely transformed back to the time domain to obtain the complex valued signal Z[n]. The real-time phase angle is obtained by analyzing the complex valued signal Z[n], and based on this, the phase angle is subtracted. and The phase information of the α band can be extracted.

[0145] Furthermore, for the initial frequency in equations (1) to (3), For example, when the target frequency band is the α band, its frequency band range is 7.5~12.5Hz. Any frequency value within the frequency band can actually be preset as the initial frequency. , so as to calculate the corresponding sleep regulation frequency. For another example, when the target frequency bands are the α band and the θ band, any frequency value within the frequency band range (7.5~12.5Hz) of the α band can be used as the initial frequency to calculate the sleep regulation frequency corresponding to the α band, and any frequency value within the frequency band range (4.5~7.5Hz) of the θ band can be used as the initial frequency to calculate the sleep regulation frequency corresponding to the θ band. Alternatively, the target frequency (personal center frequency ICF) of the target frequency band can be set as the initial frequency to calculate the sleep regulation frequency corresponding to the target frequency band. In addition, the initial frequency can also be a pre-set frequency within the low frequency of 70Hz.

[0146] S3. Generate a stimulation signal based on the sleep regulation parameter to regulate the user's sleep through the coupling effect of the stimulation signal and the electroencephalogram signal.

[0147] Specifically, after the sleep regulation parameters are calculated, a stimulation signal can be generated based on the sleep regulation parameters. As described above, in step S2, when calculating the sleep regulation parameters, one or more of the sleep regulation frequency, sleep regulation phase, and sleep regulation amplitude can be selected for calculation. That is, the sleep regulation frequency, the sleep regulation phase, and the sleep regulation amplitude are in an and / or relationship. Therefore, the frequency, phase, and / or amplitude of the stimulation signal can be generated in real time based on one or more of the sleep regulation frequency, the sleep regulation phase, and the sleep regulation amplitude. For example, the frequency of the stimulation signal can be generated in real time based on each of the sleep regulation frequencies, or the phase of the stimulation signal can be generated in real time based on each of the sleep regulation phases, or the amplitude of the stimulation signal can be generated in real time based on each of the sleep regulation amplitudes, or the frequency, phase, and amplitude of the stimulation signal can be generated in real time based on each of the sleep regulation frequencies, each of the sleep regulation phases, and each of the sleep regulation amplitudes.

[0148] Wherein, the stimulation signal includes an acoustic stimulation signal, a light stimulation signal, an electrical stimulation signal and / or a vibration stimulation signal.

[0149] In order to further illustrate the generation of the stimulation signal based on the sleep regulation parameters, the specific process of generating the frequency, phase and / or amplitude of the stimulation signal in real time based on the sleep regulation frequency, sleep regulation phase and / or sleep regulation amplitude will be described below.

[0150] (A) Generating the frequency of the stimulation signal in real time based on each of the sleep regulation frequencies.

[0151] Specifically, when the sleep control frequency corresponding to each target frequency band is obtained based on the formula (1) in the above content, After that, the frequency of the stimulation signal can be generated in real time based on the sleep regulation frequency. At this time, when only one target frequency band is selected, the sleep regulation frequency calculated based on formula (1) is actually the frequency of the stimulation signal. When multiple target frequency bands are determined, multiple sleep regulation frequencies will be calculated based on formula (1), and the frequency of the stimulation signal should be generated in real time based on the multiple sleep regulation frequencies. Since each sleep regulation frequency is calculated in real time according to the phase of the target frequency band, the stimulation signal generated in real time based on the sleep regulation frequency is actually modulated by the phase of the EEG signal, which means that the stimulation signal and the EEG signal are frequency-coupled. That is, when the frequency of the stimulation signal is generated in real time based on each sleep regulation frequency, the frequency of the stimulation signal is coupled with the phase of the EEG signal.

[0152] In some embodiments, when the frequency of the stimulation signal is generated in real time based on multiple sleep regulation frequencies, it can be generated according to the linear superposition method. That is, multiple sleep regulation frequencies are synthesized into a composite signal by linear superposition. The stimulation signal generated in real time using this method contains the components of all sleep regulation frequencies.

[0153] (B) Generating the phase of the stimulation signal in real time based on each of the sleep regulation phases.

[0154] Specifically, when the sleep regulation phase corresponding to each target frequency band is obtained based on the formula (2) in the above content After that, the phase of the stimulation signal can be generated in real time based on the sleep regulation phase. At this time, when only one target frequency band is selected, the sleep regulation phase calculated based on formula (2) is actually the phase of the stimulation signal. When multiple target frequency bands are determined, multiple sleep regulation phases will be calculated based on formula (2), and the phase of the stimulation signal should be generated in real time based on the multiple sleep regulation phases. Since each sleep regulation phase is calculated in real time according to the phase of the target frequency band, the stimulation signal generated in real time based on the sleep regulation phase is actually modulated by the phase of the EEG signal, which means that the stimulation signal and the EEG signal are coupled. That is, when the phase of the stimulation signal is generated in real time based on each sleep regulation phase, the phase of the stimulation signal is coupled with the phase of the EEG signal.

[0155] In some embodiments, when the phase of the stimulation signal is generated in real time based on multiple sleep regulation phases, the multiple sleep regulation phases can be weighted and superimposed, and the weight can be set according to the frequency band component ratio of each target frequency band in the brain. The phase of the stimulation signal is synthesized after weighted superposition.

[0156] (C) Generating the amplitude of the stimulation signal in real time based on each of the sleep regulation amplitudes.

[0157] Specifically, when the sleep regulation amplitude corresponding to each target frequency band is obtained based on the formula (3) in the above content, After that, the amplitude of the stimulation signal can be generated in real time based on the sleep regulation amplitude. At this time, when only one target frequency band is selected, the sleep regulation amplitude calculated based on formula (3) is actually the amplitude of the stimulation signal. When multiple target frequency bands are determined, multiple sleep regulation amplitudes will be calculated based on formula (3), and the amplitude of the stimulation signal should be generated in real time based on multiple sleep regulation amplitudes. Since each sleep regulation amplitude is calculated in real time according to the phase of the target frequency band, the stimulation signal generated in real time based on the sleep regulation amplitude is actually modulated by the phase of the EEG signal, which means that the stimulation signal and the EEG signal are coupled in amplitude. That is, when the phase of the stimulation signal is generated in real time based on each sleep regulation amplitude, the amplitude of the stimulation signal is coupled with the phase of the EEG signal.

[0158] In some embodiments, when the amplitude of the stimulation signal is generated in real time based on multiple sleep regulation amplitudes, it is necessary to determine the ratio of the generation coefficients of the multiple sleep regulation amplitudes, and generate the amplitude of the stimulation signal in real time based on the ratio of the generation coefficients of the multiple sleep regulation amplitudes. The sum of the generation coefficients of the multiple sleep regulation amplitudes is 1. At this time, after determining the ratio of the generation coefficients of the multiple sleep regulation amplitudes, the multiple sleep regulation amplitudes can be multiplied by their respective ratios of generation coefficients, and the multiple multiplication results can be added to obtain the amplitude of the stimulation signal.

[0159] It should be noted that the amplitude of the stimulation signal should not exceed 70dB.

[0160] In some embodiments, determining the generation coefficients of the plurality of sleep regulation amplitudes comprises: calculating the difference between the amplitudes of the plurality of target frequency bands and the target amplitude respectively, and determining the ratio of the generation coefficients of the plurality of sleep regulation amplitudes based on the ratio of the different differences. The target amplitude corresponds to the target sleep stage.

[0161] For example, taking the α band and the θ band as the target bands, the amplitude of the α band and the amplitude of the θ band are obtained according to the real-time EEG signal. At this time, the target sleep stage is stage N1, and the target amplitude corresponding to stage N1 can be obtained according to the user's historical sleep data or database. After that, the difference 1 between the amplitude of the α band and the target amplitude is calculated, and the difference 2 between the amplitude of the θ band and the target amplitude is calculated, and then the generation coefficient ratio of the sleep regulation amplitude 1 and the sleep regulation amplitude 2 is determined based on the ratio of the difference 1 and the difference 2. Among them, the sleep regulation amplitude 1 corresponds to the α band, and the sleep regulation amplitude 2 corresponds to the θ band.

[0162] It can be seen that, in fact, the ratio of the generation coefficients of multiple sleep regulation amplitudes changes in real time with the ratio of the strength of the difference between the amplitudes of multiple target frequency bands and the target amplitude. For example, the ratio of difference 1 and difference 2 may start at 4:1, then the ratio of the generation coefficients of sleep regulation amplitude 1 and sleep regulation amplitude 2 is 4:1. As sleep regulation proceeds, the user is closer to the target sleep stage N1, then the frequency band component of the θ frequency band in the EEG signal should increase, and the ratio of difference 1 and difference 2 may become 2:2, then the ratio of the generation coefficients of sleep regulation amplitude 1 and sleep regulation amplitude 2 will also be 2:2.

[0163] In another embodiment, determining the generation coefficients of the plurality of sleep regulation amplitudes includes: obtaining ratios of the plurality of target frequency bands during different sleep stages of the user, and determining the ratios of the generation coefficients of the plurality of sleep regulation amplitudes based on the ratios of the plurality of target frequency bands.

[0164] For example, the proportion of multiple target frequency bands in the EEG signal at each sleep stage can be determined through large-scale sleep data in a public database, and the proportion of the generation coefficients of the multiple sleep regulation amplitudes can be determined based on the proportion of the multiple target frequency bands. Alternatively, the sleep data of different groups of people can be measured experimentally to determine the proportion of multiple target frequency bands in the EEG signal at each sleep stage, and the proportion of the generation coefficients of the multiple sleep regulation amplitudes can be determined based on the proportion of the multiple target frequency bands. Alternatively, the proportion of multiple target frequency bands in the EEG signal at each sleep stage can be determined through the user's historical sleep data, and the proportion of the generation coefficients of the multiple sleep regulation amplitudes can be determined based on the proportion of the multiple target frequency bands.

[0165] Furthermore, after determining the proportions of multiple target frequency bands in the EEG signal at each sleep stage, a large number of experimental tests can be conducted to generate stimulation signals with different generation coefficient ratios, and the generation coefficient ratio with the best effect can be selected as the final generation coefficient ratio for each sleep regulation amplitude.

[0166] Therefore, as described above, the stimulation signal generated according to the sleep regulation parameters will be frequency-coupled, phase-coupled and / or amplitude-coupled with the EEG signal. At this time, the user's sleep can be regulated by the coupling effect of the stimulation signal and the EEG signal. In fact, when the stimulation signal and the EEG signal are coupled, an EEG coupling signal will be generated in the user's brain. The EEG coupling signal actually represents a new signal formed by the coupling of the user's EEG signal and the stimulation signal under the action of the stimulation signal. That is, under the action of the coupled stimulation signal, the user's EEG signal will change and generate an EEG coupling signal. The EEG coupling signal will induce an increase in the EEG signal of the target frequency band in the user's brain, induce the brain to enter a corresponding sleep state, or may also reduce the generation of EEG signals of the target frequency band in the user's brain.

[0167] Furthermore, regulating the user's sleep through the coupling of the stimulation signal and the EEG signal includes: determining an induced phase according to the phase of the target frequency band of the EEG signal and the evoked response potential delay of the stimulation signal, and the stimulation signal and the EEG signal begin to generate coupling at the induced phase.

[0168] In fact, when the stimulation signal acts on the user's brain, an evoked response potential delay will occur. This indicates that there is a certain delay time in the electrophysiological response of the brain to the stimulation signal. For example, for the sound wave stimulation signal, the delay of the evoked response potential is 62.5ms. It is assumed that the target frequency band is determined to be the δ band. After obtaining the sleep regulation parameters, the corresponding sound wave stimulation signal is generated to act on the user's brain. Assuming that the target frequency of the δ band is 2.5Hz and the period is 400ms, the phase offset when the sound wave stimulation signal occurs should be (62.5 / 400)*360=15.625°. In order to make the EEG signal and the stimulation signal produce a coupling effect, the induced phase can be determined to be 360°-15.625°=344.375°, that is, the sound wave stimulation signal is applied to the user's brain at 344.375°. At this time, the evoked response potential of the sound wave stimulation signal will be coupled with the peak of the EEG signal, so that the EEG signal will generate an EEG coupling signal at the peak due to the action of the sound wave stimulation signal, that is, the coupling effect of the stimulation signal and the EEG signal effectively induces an increase in the EEG signal in the δ frequency band in the user's brain.

[0169] In some other embodiments, if the generation of EEG signals in the target frequency band is to be reduced by coupling the stimulation signal with the EEG signal, the evoked response potential of the stimulation signal should be coupled with the trough of the EEG signal so that the EEG signal generates an EEG coupling signal at the trough, thereby reducing the generation of EEG signals in the target frequency band in the user's brain. Also taking the above-mentioned sound wave stimulation signal as an example, assuming that the target frequency of the δ frequency band is 2.5 Hz and the period is 400 ms, the phase shift when the stimulation signal acts should be (62.5 / 400)*360=15.625°. At this time, the induced phase is determined to be 180°-15.625°=164.375°, that is, the sound wave stimulation signal acts on the user's brain at 164.375°. At this time, the evoked response potential of the sound wave stimulation signal will be coupled with the trough of the EEG signal. The EEG signal will generate an EEG coupling signal at the trough due to the action of the sound wave stimulation signal. At this time, the generation of EEG signals in the δ frequency band in the user's brain can be reduced through the coupling effect of the stimulation signal and the EEG signal.

[0170] It should be noted that for different types of stimulation, the delay of the stimulus evoked response potential is different. The above embodiment of the present application only takes the delay of the evoked response potential of the sound wave stimulation as 62.5ms as an example to illustrate the specific process of generating the coupling effect.

[0171] Figure 7 The following is a schematic diagram showing an application of a sleep regulation method provided in an embodiment of the present application. Figure 7 As shown, when the sleep regulation method is started, the EEG signal is first collected and the target frequency band is determined, then the target frequency is determined, and the phase information of the target frequency band is extracted based on the target frequency, so as to calculate the sleep regulation parameters in real time, and then the stimulation signal is generated in real time based on the sleep regulation parameters, and the stimulation signal is applied to the brain, so as to achieve sleep regulation through the coupling of the stimulation signal and the EEG signal.

[0172] The present application calculates the user's personalized sleep regulation parameters based on the phase of the real-time EEG signal to generate a stimulation signal in real time, so that the stimulation signal is frequency-coupled, phase-coupled and / or amplitude-coupled with the EEG signal, so as to better regulate the brain's neural oscillations, improve the synchronization and stability between neural stimulation and intrinsic brain activity, and improve the user's efficiency in falling asleep and sleep quality at night.

[0173] The protection scope of the sleep regulation method described in the embodiment of the present application is not limited to the execution order of the steps listed in the present embodiment. All solutions implemented by adding, reducing or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0174] The embodiments of the present application also provide a sleep regulation system, which can implement the sleep regulation method in the physiological state regulation method described in the embodiments of the present application. However, the implementation device of the sleep regulation system described in the present application includes but is not limited to the structure of the sleep regulation system listed in the embodiments. All structural deformations and replacements of the prior art made according to the principles of the present application are included in the protection scope of the present application.

[0175] Figure 8 The structure diagram of the sleep regulation system described in the embodiment of the present application is shown as follows: Figure 8 As shown, the sleep regulation system 4 includes an acquisition module 41 , a generation module 42 and a regulation module 43 .

[0176] An acquisition module 41 is used to acquire the user's EEG signal in real time; A generating module 42, configured to calculate a sleep regulation parameter based on the frequency, phase and / or amplitude of the EEG signal; The control module 43 is used to generate a stimulation signal based on the sleep control parameter, so as to control the sleep of the user through the coupling effect of the stimulation signal and the electroencephalogram signal.

[0177] It should be noted that the structures and principles of the acquisition module 41 , the generation module 42 and the control module 43 correspond one to one with the steps in the above-mentioned sleep control method, so they will not be described in detail here.

[0178] It should be noted that the sleep control system provided in the embodiment of the present application is only a virtualized system structure setting. Among them, the present application does not impose any restrictions on the specific implementation subject of each module.

[0179] In the several embodiments provided in the present application, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules or units, which can be electrical, mechanical or other forms.

[0180] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0181] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0182] An embodiment of the present application also provides a wearable device, comprising: one or more sensors configured to collect biological signals of a user in real time; one or more processors; and one or more memories, wherein the memories store computer-readable codes, and when the computer-readable codes are executed by the one or more processors, the functions of the bio-health system as described above are implemented.

[0183] The wearable device can be used with the help of Fig. 9 The exemplary computing device architecture shown in FIG. Fig. 9 As shown, the exemplary computing device may include a bus 910, one or more CPUs 920, a read-only memory (ROM) 930, a random access memory (RAM) 940, a communication port 950 connected to a network, an input / output component 960, a hard disk 970, etc. The storage device in the computing device 900, such as the ROM 930 or the hard disk 970, may store various data or files used for computer processing and / or communication and program instructions executed by the CPU. The computing device 900 may also include a user interface 980. Of course, Fig. 9 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Fig. 9 One or more components of a computing device are shown.

[0184] The embodiment of the present application also provides a computer-readable storage medium, on which instructions are stored, and when the instructions are executed by the processor, the processor executes the physiological state regulation method as described in the present application. A person of ordinary skill in the art can understand that all or part of the steps in the method for implementing the above embodiment can be executed by computer-readable instructions stored on a computer-readable storage medium. Computer-readable storage media include, but are not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0185] The present application also provides a computer program product or a computer program, which includes computer-readable instructions stored in a computer-readable storage medium. The processor of the computer device can read the computer-readable instructions from the computer-readable storage medium, and the processor executes the computer-readable instructions, so that the computer device performs the physiological state regulation method described in each of the above embodiments.

[0186] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0187] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A biological health system, characterized in that: include: A detection component configured to collect biological signals of a user in real time; a processing component configured to calculate a physiological state regulation parameter based on the phase of the biological signal, and generate a stimulation signal based on the physiological state regulation parameter; The output component is configured to output the stimulation signal so that the coupling effect between the biological signal and the stimulation signal can regulate the physiological state of the user.

2. The bio-health system according to claim 1, characterized in that: Also includes: The processing component selects at least one target frequency band based on the biological signal, and calculates corresponding physiological state control parameters based on the phase of each target frequency band; The physiological state regulation parameters include physiological state regulation frequency, physiological state regulation phase and / or physiological state regulation amplitude.

3. The bio-health system according to claim 2, characterized in that: Also includes: The processing component calculates the corresponding physiological state regulation frequency and / or the physiological state regulation phase based on the phase of each target frequency band and the preset initial frequency of the stimulation signal.

4. The bio-health system according to claim 2, characterized in that: Also includes: The processing component calculates the corresponding physiological state regulation amplitude based on the phase of each target frequency band, the preset initial frequency and initial amplitude of the stimulation signal; wherein the initial amplitude corresponds to the amplitude of the target frequency band.

5. The bio-health system according to claim 2, characterized in that: Also includes: The processing component calculates the corresponding physiological state regulation amplitude based on the phase of each target frequency band, the preset initial frequency and initial amplitude of the stimulation signal; wherein the initial amplitude is adaptively adjusted with a preset adjustment value following the change of the physiological state stage.

6. The bio-health system according to claim 2, characterized in that: Also includes: The processing component generates the frequency of the stimulation signal in real time based on the frequency of each physiological state regulation, so that the stimulation signal is coupled with the biological signal frequency; The processing component generates the phase of the stimulation signal in real time based on the phase of each physiological state regulation, so that the stimulation signal is coupled with the biological signal; and / or The processing component generates the amplitude of the stimulation signal in real time based on the amplitude of each physiological state regulation, so that the stimulation signal is coupled with the biological signal amplitude.

7. The bio-health system according to claim 6, characterized in that: Also includes: The processing component generates the amplitude of the stimulation signal in real time based on the multiple physiological state regulation amplitudes; wherein the processing component determines the generation coefficient ratio of the multiple physiological state regulation amplitudes, and generates the amplitude of the stimulation signal in real time based on the generation coefficient ratio of the multiple physiological state regulation amplitudes; The sum of the generation coefficients of the multiple physiological state regulation amplitudes is 1.

8. The bio-health system according to claim 7, characterized in that: Also includes: The processing component determines the generation coefficients of the multiple physiological state regulation amplitudes; wherein the processing component calculates the difference between the amplitudes of the multiple target frequency bands and the target amplitude respectively, and determines the ratio of the generation coefficients of the multiple physiological state regulation amplitudes based on the ratio of different differences; The target amplitude corresponds to a target physiological state stage.

9. The bio-health system according to claim 7, characterized in that: Also includes: The processing component determines the generation coefficients of multiple physiological state regulation amplitudes; wherein, the processing component obtains the proportions of multiple target frequency bands when the user is in different physiological state stages, and determines the proportions of the generation coefficients of multiple physiological state regulation amplitudes based on the proportions of multiple target frequency bands.

10. The bio-health system according to claim 2, characterized in that: Also includes: The processing component regulates the physiological state of the user through the coupling effect of the biological signal and the stimulation signal, including: The processing component determines the evoked phase according to the phase of the target frequency band and the evoked response potential delay of the stimulation signal, The output component outputs the stimulation signal so that the biological signal begins to couple with the stimulation signal at the induced phase, so as to achieve physiological state regulation of the user at the physiological state stage corresponding to the target frequency band.

11. The bio-health system according to claim 2 or 10, characterized in that: Also includes: The processing component acquires a target frequency; The target frequency corresponds to the target frequency band; The processing component obtains a first filtering phase offset and a second filtering phase offset of the target frequency; The first filtering phase offset is a phase offset of the biological signal after being processed by the first filter, the second filtering phase offset is a phase offset of the frequency domain signal after being processed by the second filter, and the frequency domain signal is acquired based on the biological signal; The processing component obtains the phase of the target frequency band based on the first filtering phase offset and the second filtering phase offset.

12. The bio-health system according to claim 11, characterized in that: Also includes: The processing component obtains the frequency spectrum characteristics corresponding to the target frequency band, and extracts the frequency value with the highest frequency energy as the target frequency based on the frequency spectrum characteristics.

13. The bio-health system according to claim 2, characterized in that: Also includes: The processing component initially selects the target frequency band and determines whether the user's physiological state stage changes based on the biosignal; The processing component reselects the target frequency band when the physiological state stage of the user changes, so that the target frequency band corresponds to the current physiological state stage of the user; or The processing component maintains the target frequency band unchanged when the user's physiological state stage changes.

14. The bio-health system according to claim 5 or 13, characterized in that: Also includes: The processing component determines whether the user's physiological state stage has changed based on the biological signal; wherein, The processing component obtains at least one physiological state staging result of the user based on the biological signal within a preset time window, classifies and counts at least one physiological state staging result, and obtains at least one staging counting result. The relationship between at least one of the period counting results within the preset time window and any preset threshold is determined. If any of the period counting results is higher than the preset threshold, it is determined that the user's physiological state stage has changed.

15. The bio-health system according to claim 11, characterized in that: Also includes: The processing component obtains at least one physiological state staging result of the user based on the biological signal within a preset time window; wherein, the processing component segments the biological signal within the preset time window according to time, and obtains at least one physiological state staging result within the preset time window based on the biological signal within multiple time periods.

16. The bio-health system according to claim 1, characterized in that: Also includes: The biological signals include electroencephalogram signals, cortical electroencephalogram signals, deep electrode signals, electrooculogram signals, electromyography signals, electrocardiogram signals, magnetic resonance imaging signals and / or near-infrared brain function imaging signals.

17. The bio-health system according to claim 1, characterized in that: Also includes: The stimulation signal is a health stimulation signal, including an acoustic stimulation signal, a light stimulation signal, an electrical stimulation signal, an ultrasonic stimulation signal, a magnetic stimulation signal and / or a vibration stimulation signal.

18. A method for regulating physiological state, characterized in that: include: Acquire users’ biological signals in real time; Calculating physiological state regulation parameters based on the phase of the biological signal; A stimulation signal is generated based on the physiological state regulation parameter, so as to regulate the physiological state of the user through the coupling effect of the biological signal and the stimulation signal.

19. A wearable device, characterized in that: include: One or more sensors configured to collect biological signals of the user in real time; one or more processors; and One or more memories, wherein the memories store computer-readable codes, and when the computer-readable codes are executed by the one or more processors, the functions of the bio-health system as described in any one of claims 1 to 17 are implemented.

20. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, which, when executed by a processor, cause the processor to execute the physiological state regulation method as claimed in claim 18.

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