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

Abstract

The application provides a biological health system, a physiological state regulation method, a wearable device and a storage medium. The biological health system comprises: a detection component configured to collect biological signals of a user in real time; a processing component configured to calculate physiological state regulation parameters based on phases of the biological signals, generate stimulation signals based on the physiological state regulation parameters; and an output component configured to output the stimulation signals, so that a coupling effect of the biological signals and the stimulation signals regulates physiological states of the user. The application acquires biological signals of a user in real time, calculates individual physiological state regulation parameters of the user based on phases of the biological signals to generate stimulation signals in real time, so as to regulate physiological states of the user through the coupling effect of the stimulation signals and the biological signals.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of biological health, and particularly relates to a biological health system, a physiological state regulation method, a wearable device and a storage medium. BACKGROUND

[0002] As an indispensable basic physiological demand in human life, the physiological state plays an important role in maintaining physical health, promoting psychological balance and restoring energy. A good physiological state not only makes a person full of vitality and energy, but also is a key factor for maintaining the immune system, heart health and cognitive function. However, the fast-paced life, high-pressure work, popularity of electronic products and irregular life schedule in modern society often lead to the physiological state problems of many people.

[0003] Current 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 on the market are mainly divided into two categories: pure detection 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, have large size, low wearing comfort, need professional operation and interpretation, and are inconvenient for use in daily life. The smart bracelet and watch products have great errors due to the limitations of their detection methods and physiological signal monitoring, and cannot improve the physiological state problems of users without effective intervention measures. The intervention products mainly use open-loop neural stimulation, which greatly reduces the effectiveness due to the lack of real-time feedback regulation mechanism according to the actual biological activity. SUMMARY

[0004] The purpose of the present application is to provide a biological health system, a physiological state regulation method, a wearable device and a storage medium, which solve the technical problem of how to regulate the physiological state of the biological activity of the user in real time according to the health needs.

[0005] In a first aspect, the present application provides a biological health system, comprising: a detection component configured to collect biological signals of a user in real time; a processing component configured to calculate physiological state regulation parameters based on the phase of the biological signals, generate a stimulation signal based on the physiological state regulation parameters; and 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 form of the first aspect, the processing component selects at least one target frequency band based on the biosignal, and calculates a corresponding physiological state regulation parameter based on a phase of each of the target frequency bands; the physiological state regulation parameter comprises a physiological state regulation frequency, a physiological state regulation phase, and / or a physiological state regulation amplitude.

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

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

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

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

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

[0012] In an implementation form of the first aspect, the processing component determines generation coefficients of the plurality of the physiological state regulation amplitudes; the processing component respectively calculates a difference value between an amplitude of each of the target frequency bands and a target amplitude, and determines a generation coefficient ratio of the plurality of the physiological state regulation amplitudes based on a proportion of different difference values; the target amplitude corresponds to a target physiological state phase.

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

[0014] In an implementation form of the first aspect, the processing component regulates the physiological state of the user by coupling the biological signal and the stimulation signal; wherein the processing component determines an evoked phase according to a phase of the target frequency band and a latency of an evoked response potential of the stimulation signal; and the output component outputs the stimulation signal, so that the biological signal and the stimulation signal start to produce coupling at the evoked phase, to regulate the physiological state of the user at the physiological state stage corresponding to the target frequency band.

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

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

[0017] In an implementation form of the first aspect, the processing component initially selects the target frequency band, and determines whether a physiological state stage of the user changes based on the biological signal; in a case where the physiological state stage of the user changes, the processing component reselects the target frequency band, so that the target frequency band corresponds to the current physiological state stage of the user; or in a case where the physiological state stage of the user changes, the processing component keeps the target frequency band unchanged.

[0018] In an implementation form of the first aspect, the processing component determines whether a physiological state stage of the user changes 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, counts the at least one physiological state staging result, obtains at least one staging count result, determines a relationship between the at least one staging count result and any preset threshold within the preset time window, and determines that the physiological state stage of the user changes if any of the staging count results is higher than the preset threshold.

[0019] In an implementation form 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 the at least one physiological state staging result within the preset time window based on the biological signal within a plurality of time segments.

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

[0021] In an implementation form of the first aspect, the stimulation signal is a health stimulation signal, including a sound wave stimulation signal, a light stimulation signal, an electric stimulation signal, an ultrasonic wave 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, including: obtaining a biological signal of a user in real time; calculating a physiological state regulation parameter based on a phase of the biological signal; generating a stimulation signal based on the physiological state regulation parameter, to regulate a physiological state of the user through a coupling effect of the biological signal and the stimulation signal.

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

[0024] In a fourth aspect, the present application provides a computer readable storage medium, which stores instructions, and the instructions, when executed by a processor, cause the processor to perform the physiological state regulation method as described.

[0025] As described above, the biological health system, physiological state regulation method, wearable device and storage medium provided by the present application have the following beneficial effects:

[0026] The present application acquires the biological signal of the user in real time through the closed-loop nerve stimulation mode, calculates the personalized physiological state regulation parameter of the user based on the phase of the real-time biological signal to generate 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, so as to realize physiological state regulation of the user through the coupling effect of the stimulation signal and the biological signal.

[0027] 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 by personalization, so that the amplitude of the stimulation signal generated based on this can more specifically regulate the physiological state of the user.

[0028] The present application accurately extracts the phase information of the biological signal and determines the induced phase, so that the stimulation signal and the biological signal can start to produce coupling effect at the induced phase, so as to improve the synchronization and stability between the nerve stimulation and the internal brain, muscle, eye or / and heart activities of the user, and further improve the physiological health state of the user. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1A An implementation structure diagram of the biological health system provided by the present application is shown.

[0030] Figure 1A An application scenario diagram of the physiological state regulation method provided by the present application is shown.

[0031] Figure 1A Another application scenario diagram of the physiological state regulation method provided by the present application is shown.

[0032] Figure 2 A flowchart of the physiological state regulation method provided by the embodiment of the present application in an embodiment is shown.

[0033] Figure 3 A flowchart of the physiological state regulation method provided by the embodiment of the present application in an embodiment is shown.

[0034] Figure 4 A flowchart of the physiological state regulation method provided by the embodiment of the present application in an embodiment is shown.

[0035] Figure 5 A flowchart of the physiological state regulation method provided by the embodiment of the present application in an embodiment is shown.

[0036] Figure 6A flowchart of the physiological state regulation method according to an embodiment of the present application is shown.

[0037] Figure 7 An application diagram of the physiological state regulation method according to an embodiment of the present application is shown.

[0038] Figure 7 An embodiment structure diagram of the biological health system according to an embodiment of the present application in a sleep scenario is shown.

[0039] Figure 8 An internal structure diagram of the wearable device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0040] The present application is described below by way of specific embodiments, and those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in the specification. The present application can also be implemented or applied by means of other different specific embodiments, and each detail in the specification can be modified or changed based on different views 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.

[0041] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner, and only the components related to the present application are shown in the diagrams, not the number, shape and size of the components when actually implemented. The actual implementation of each component may be a random change in shape, number and proportion, and the layout pattern of the components may also be more complex.

[0042] In addition, the description of "first", "second" and the like in the present application is only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the technical features indicated or implicitly indicating the number of technical features. Therefore, the features defined as "first", "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the realization of ordinary skilled in the art, when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, nor within the scope of protection claimed by the present application.

[0043] Currently, the fast-paced life of modern society, high-pressure work, the popularity of electronic products and irregular living and working patterns often lead many people to face physiological state problems. Long-term physiological state problems will seriously affect physical and mental health. Therefore, there are many physiological state intervention products on the market, such as smart bracelets or watches that can detect and track physiological state, smart sleep aid lamps, smart aromatherapy, and the like, which can improve the physiological state of the user and enhance the physiological state experience of the user. However, these products lack personalized physiological state regulation schemes and real-time feedback regulation, thereby greatly reducing the effectiveness of physiological state regulation.

[0044] To at least solve the above technical problems, the physiological state regulation method provided by the embodiments of the present application 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 start to produce coupling effect at a determined 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 brain, muscle, eye or / and heart activities, and thereby improve the physiological health state of the user.

[0045] The biological health system 1 provided by the embodiments of the present application can implement the physiological state regulation method, and the structure thereof is shown in Figure 8 The biological health system 1 comprises a detection component 11, a processing component 12, an output component 13 and a power supply component 14.

[0046] The detection component 11 is configured to collect the biological signal of the user in real time and send the collected biological signal to the processing component 12. The detection component 11 can be a wearable biological signal collection device or a sensor device. The wearable biological signal collection device can be a headband, a headcap, a headring, an eyeshade or any other form, which is not limited in the present application.

[0047] The processing component 12 is configured to calculate physiological state regulation parameters based on the phase of the biological signal and generate a stimulation signal based on the physiological state regulation 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 into a wearable regulation device with the detection component 11 to execute the physiological state regulation method provided in the embodiments 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, which is not limited in the present application.

[0048] The output component 13 is configured to output the stimulation signal, so that the coupling of the biological signal and the stimulation signal can regulate 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 brain of the user at the evoked phase, so as to regulate the physiological state of the user through the coupling of the stimulation signal and the biological signal. The output component 13 can include a stimulation element and a stimulation driving device. For example, when the acoustic wave stimulation signal is generated, the stimulation element can be a speaker or a bone conduction vibrator, and the stimulation driving device can be an audio driving device. The specific structure of the output component 13 is not limited in the present application.

[0049] The power supply component 14 supplies power to the whole biological health system 1.

[0050] The software component 15 can be a GUI interface of the biological health system 1, or an App application interacting with the processing component, which is deployed on any mobile terminal, including a smart phone, a smart watch, a PAD, a tablet computer or other types of smart wearable devices, etc., or deployed on a webpage, and the present application does not make any limitation in this regard.

[0051] In addition, the processing component 12 can also send the biological signal data collected in the physiological state cycle of the user to the software component 15, and the software component 15 can further process and analyze the data, so as to output a detailed physiological state report, so that the user can view the personal physiological state in time.

[0052] It should be noted that, Figure 9 The structure of the biological health system 1 shown in the above embodiment is only provided as an example. In fact, the components of the biological health system 1 can exist physically alone, or two or more components can be integrated into one component to perform part or all of the steps of the physiological state regulation method.

[0053] The biological health system according to the embodiment of the present application is described in detail as follows. The biological health system at least includes a detection component, a processing component and an output component.

[0054] The detection component is configured to collect the biological signal of the user in real time. The biological signal includes an electroencephalogram signal, an electrocorticogram signal, a deep electrode signal, an electrooculogram signal, an electromyogram signal, an electrocardiogram signal, a functional magnetic resonance imaging signal and / or a near-infrared brain function imaging signal.

[0055] Electroencephalogram (EEG) is a recording of electrical activity of neurons in the brain by placing electrodes on the scalp. The characteristics of EEG mainly include amplitude, phase, and frequency. The amplitude of EEG refers to the strength or size of the signal, usually measured in microvolts (μV). The amplitude of EEG varies in different brain regions and different physiological or pathological states. The frequency of EEG refers to the oscillation rate of the signal, usually measured in hertz (Hz). The frequency of EEG can be divided into different bands, such as δ wave (0.5-4 Hz), θ wave (4-8 Hz), α wave (8-13 Hz), β wave (13-30 Hz), and γ wave (above 30 Hz). These bands are related to different functional states of the brain. The phase of EEG refers to the position of the signal waveform in time. In EEG, the phase can reflect the temporal relationship between signals recorded by different electrodes, which is very important for studying the synchrony between brain regions.

[0056] Electrocorticogram (ECoG) is a recording of electrical activity in the brain by placing electrodes on the surface of the cerebral cortex, with high spatial and temporal resolution. The amplitude of ECoG reflects the strength of the electrical signal generated by neuronal activity. The amplitude of ECoG varies in different brain regions and different physiological or pathological states. For example, the amplitude of local field potentials (LFP) and event-related potentials (ERPs) can provide important information about the activity of neuronal populations. The phase of ECoG reflects the temporal relationship between signals recorded by different electrodes in ECoG. Phase synchronization, especially the connectivity patterns between brain regions detected by electrocorticography, is very important for studying the synchrony and functional connectivity between brain regions. In addition, phase-amplitude coupling (PAC) is an important feature in ECoG, which is important for the diagnosis of neurological diseases and deciphering the connectivity of neural networks. The frequency of ECoG reflects the dynamic changes of brain activity. ECoG can be analyzed in multiple frequency bands, including δ wave (1-4 Hz) from low frequency to γ wave (above 30 Hz) from high frequency. Different frequency bands of ECoG are related to different functional states of the brain.

[0057] Deep electrode signals (SEEG), such as local field potentials (LFPs) and microelectrode recordings, are captured through electrodes implanted deep within the brain. These signals provide direct information about neural activity in the brain, which is particularly useful when studying neuronal networks and neurological conditions such as epilepsy. Deep electrode signals typically have larger amplitudes because they are closer to the source of neuronal activity. The amplitude of these signals can reflect the strength of neuronal population activity, usually measured in microvolts (μV). For example, LFP signals have larger amplitudes than EEG, more clearly showing high-frequency discharge patterns. The phase of deep electrode signals reflects the temporal relationship between signals recorded at different electrodes. This phase information is important for studying the synchrony and functional connectivity between brain regions. The frequency of deep electrode signals reflects the dynamic changes in brain activity. These signals can be analyzed across multiple frequency bands, including from low-frequency delta waves (1-4 Hz) to high-frequency gamma waves (above 30 Hz). Different frequency bands of deep electrode signals 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 at axon terminals. Additionally, high-frequency power mainly reflects spike activity and co-varies with LFP components originating from post-synaptic potentials and other membrane voltage fluctuations unrelated to spikes.

[0058] Electro-oculogram (EOG) is an objective and quantitative retinal function test that detects slow changes in the resting potential of the eye as it adapts to light. It reflects the function of the retinal pigment epithelium-photoreceptor complex and is sustained. The amplitude of the electro-oculogram refers to the strength or size of the signal. In electro-oculography (EOG), changes in amplitude reflect the functional state of the retinal pigment epithelium-photoreceptor complex. For example, during dark adaptation, the resting potential gradually decreases to a minimum point (dark trough potential), and then gradually increases to a maximum point (light peak potential) during light adaptation. These changes are recorded by electrodes placed on the skin at the inner and outer canthus and are used to assess retinal function. The phase of the electro-oculogram may be related to the changes in potential during light adaptation, which are related to the retina's response to light. The frequency of the electro-oculogram is usually low because it is related to the light adaptation process of the retina, which is a relatively slow physiological process. Changes in electro-oculography are typically in the range of 0.01 to 0.1 Hz, and these changes are called spontaneous activity transients (SATs), which are particularly important in premature infants because they are crucial for the formation of neuronal connections during early immaturity.

[0059] Electromyography (EMG) is the measurement of electrical signals that initiate muscle contractions and reflect the degree of muscle contraction. The amplitude of the electromyography reflects the strength or size of the signal. In electromyography (EMG), the change in amplitude reflects 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 electromyography reflects the temporal relationship between signals recorded by different electrodes. When analyzing the activation time of the muscle, the type of muscle contraction is usually not concerned, but only the time when the muscle contracts and relaxes. Phase information can be used to determine the exact time point of muscle activation. The frequency of the electromyography reflects the dynamic changes of brain activity. The energy distribution of the electromyography 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 the mean power frequency (MPF) are derived by analyzing the power spectral density of the surface electromyography signal, and are usually used to assess the strength and fatigue of muscle contraction. When the muscle is under greater load or fatigue, these frequency parameters will decrease, and when the muscle is under lighter load or more relaxed, these frequency parameters will increase.

[0060] Electrocardiography (ECG) is a graphical representation of the electrical activity changes of the heart recorded through the body surface, which reflects the electrical physiology of the heart. The amplitude of the electrocardiography is usually around 1 mV, reflecting the potential changes produced during the depolarization and repolarization of the heart. Each wave segment in the electrocardiogram, such as the P wave, QRS complex and T wave, has a specific amplitude range. For example, the amplitude of the P wave is usually not more than 0.25 mV, the amplitude of the QRS complex varies greatly, and the amplitude of the T wave should not be less than 1 / 10 of the R wave of the same lead. The phase of the electrocardiography usually refers to specific points of the waveform, such as the peak, trough and interval. Each cardiac cycle in the electrocardiogram is composed of a series of regular waveforms, including P wave, QRS complex and T wave, and the start and end points, peaks, troughs and intervals 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 represents the depolarization of the ventricle, and the T wave represents the repolarization of the ventricle. The frequency of the electrocardiography reflects the dynamic changes of the heart activity. The frequency range of the electrocardiography is about 0.05 Hz to 100 Hz, and the signal energy is mainly concentrated in 0.5 Hz to 45 Hz. The change of heart rate directly affects the frequency component of the electrocardiography, and the normal heart rate range is about 60 to 100 times / minute. Spectral analysis of the electrocardiography can help identify features of heart disease, such as arrhythmia, myocardial ischemia, etc.

[0061] Functional magnetic resonance imaging (fMRI) is a research method that stimulates a specific sense to cause neural activity in the corresponding part of the cerebral cortex (functional area activation), and displays it through magnetic resonance images. Functional magnetic resonance imaging (fMRI) signal is an important brain function imaging technology, which studies brain function by measuring the blood dynamics changes caused by neuronal activity. By detecting the changes in fMRI signal, the neural activity of the brain can be indirectly reflected. The main features of fMRI signal include amplitude, phase and frequency. The amplitude of fMRI signal is usually related to the intensity of BOLD signal, reflecting the blood flow changes in specific brain areas during task performance or resting state. The phase information of fMRI signal contains the timing information related to brain function activity. The frequency component of fMRI signal reflects the dynamic changes of brain activity. Among them, the resting state fMRI (rs-fMRI) signal can be decomposed into several intrinsic frequency clusters, which are related to different physiological processes such as breathing, pulse, metabolism and vasomotor activity.

[0062] Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique that studies brain function by measuring the changes in oxy-hemoglobin (oxy-Hb) and deoxy-hemoglobin (deoxy-Hb) concentrations caused by brain activity. The main features of functional near-infrared spectroscopy (fNIRS) signal include amplitude, phase and frequency. The amplitude of fNIRS signal usually refers to the degree of change in oxy-hemoglobin and deoxy-hemoglobin concentration. When analyzing the time-domain characteristics of fNIRS signal, the phase of fNIRS signal may be considered, especially when studying the time-domain drift and baseline correction of the signal. The frequency of fNIRS signal reflects the dynamic changes of brain activity. Among them, the resting state fNIRS signal can be decomposed into several intrinsic frequency clusters, which are related to different physiological processes such as breathing, pulse, metabolism and vasomotor activity. In addition, the frequency domain analysis of fNIRS signal can also reveal the low frequency oscillations (LFOs) of brain activity, which are related to the hemodynamic changes of the brain.

[0063] The scope of protection of the present application is not limited to the types of biological signals listed in the present embodiment, and any biological signal that can be directly extended and implemented based on the technical principles of the present application is included in the scope of protection of the present application.

[0064] The processing component is configured to calculate a physiological state regulation parameter based on the phase of the biosignal, and generate a stimulation signal based on the physiological state regulation parameter. The stimulation signal is a healthy stimulation signal, including a sound wave stimulation signal, a light stimulation signal, an electric 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 the embodiment, and any stimulation signal that can be directly extended and implemented based on the technical principles of the present application is included in the protection scope of the present application.

[0065] The output component is configured to output the stimulation signal, so that the coupling of the biosignal and the stimulation signal regulates the physiological state of the user.

[0066] The coupling of the electroencephalogram signal and the stimulation signal can regulate the brain state of the user.

[0067] The coupling of the electroencephalogram signal and the stimulation signal can regulate the brain state of the user.

[0068] The coupling of the deep electrode signal and the stimulation signal can regulate the brain state of the user.

[0069] The coupling of the electrooculogram signal and the stimulation signal can regulate the eye movement state of the user.

[0070] The coupling of the electromyogram signal and the stimulation signal can regulate the muscle activity state of the user.

[0071] The coupling of the electrocardiogram signal and the stimulation signal can regulate the sympathetic and parasympathetic nerves of the user.

[0072] The coupling of the magnetic resonance functional imaging signal and the stimulation signal can regulate the brain state of the user.

[0073] The coupling of the near-infrared brain function imaging signal and the stimulation signal can regulate the brain state of the user.

[0074] The above one or more physiological states can be applied to improve different physiological problems or diseases, including: brain state regulation such as sleep regulation, emotion regulation, and mental disease regulation, for example, depression, mania, autism, schizophrenia, and other disease regulation, through the coupling of brain electrical signals and stimulation signals; sleep regulation (abnormal body movement), Parkinson's disease regulation, and other diseases through the coupling of electromyographic signals and stimulation signals; sleep regulation, emotion regulation, heart disease treatment regulation, and other diseases through the coupling of electrocardiographic signals and stimulation signals; sleep regulation, dream regulation, eye spasm treatment regulation, and other diseases through the coupling of electrooculographic signals and stimulation signals.

[0075] In an embodiment of the present application, the processing component selects at least one target frequency band based on the biological signal, calculates the corresponding physiological state regulation parameter based on the phase of each target frequency band; the physiological state regulation parameter includes physiological state regulation frequency, physiological state regulation phase, and / or physiological state regulation amplitude.

[0076] In an 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.

[0077] In an 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.

[0078] In an 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 change of the physiological state stage.

[0079] In an embodiment of the present application, the processing component generates the frequency of the stimulation signal in real time based on each physiological state regulation frequency, so that the stimulation signal is in frequency-phase coupling with the biological signal; the processing component generates the phase of the stimulation signal in real time based on each physiological state regulation phase, so that the stimulation signal is in phase-phase coupling with the biological signal; and / or the processing component generates the amplitude of the stimulation signal in real time based on each physiological state regulation amplitude, so that the stimulation signal is in amplitude-phase coupling with the biological signal.

[0080] In an embodiment of the present application, the processing component generates the amplitude of the stimulation signal in real time based on the physiological state regulation amplitudes; wherein the processing component determines the generation coefficient proportion of the physiological state regulation amplitudes, and generates the amplitude of the stimulation signal in real time based on the generation coefficient proportion of the physiological state regulation amplitudes; the sum of the generation coefficients of the physiological state regulation amplitudes is 1.

[0081] In an embodiment of the present application, the processing component determines the generation coefficients of the physiological state regulation amplitudes; wherein the processing component calculates the difference between the amplitude of each of the target frequency bands and a target amplitude, and determines the generation coefficient proportion of the physiological state regulation amplitudes based on the proportion of different differences; the target amplitude corresponds to a target physiological state stage.

[0082] In an embodiment of the present application, the processing component determines the generation coefficients of the physiological state regulation amplitudes; wherein the processing component obtains the proportion of the target frequency bands when the user is in different physiological state stages, and determines the generation coefficient proportion of the physiological state regulation amplitudes based on the proportion of the target frequency bands.

[0083] In an embodiment of the present application, the processing component regulates the physiological state of the user through the coupling effect of the biological signal and the stimulation signal; wherein 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; and the output component outputs the stimulation signal, so that the biological signal and the stimulation signal start to produce coupling effect at the evoked phase, so as to regulate the physiological state of the user in the physiological state stage corresponding to the target frequency band.

[0084] In an 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 filter phase offset and a second filter phase offset of the target frequency; the first filter phase offset is the phase offset of the biological signal after being processed by a first filter, and the second filter phase offset is the phase offset of a frequency domain signal after being processed by a second filter, the frequency domain signal being obtained based on the biological signal; and the processing component obtains the phase of the target frequency band based on the first filter phase offset and the second filter phase offset.

[0085] In an embodiment of the present application, the processing component obtains the spectral feature corresponding to the target frequency band, and extracts the frequency value with the highest frequency energy as the target frequency based on the spectral feature.

[0086] In an embodiment of the present application, the processing component initially selects the target frequency band, and determines whether the physiological state stage of the user changes based on the biosignal; the processing component reselects the target frequency band in the case that 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 keeps the target frequency band unchanged in the case that the physiological state stage of the user changes.

[0087] In an embodiment of the present application, the processing component determines whether the physiological state stage of the user changes based on the biosignal; wherein the processing component obtains at least one physiological state staging result of the user based on the biosignal within a preset time window, classifies and counts at least one physiological state staging result to obtain at least one staging count result, determines the relationship between at least one staging count result within the preset time window and any preset threshold, and determines that the physiological state stage of the user changes if any staging count result is higher than the preset threshold.

[0088] In an embodiment of the present application, the processing component obtains at least one physiological state staging result of the user based on the biosignal within a preset time window; wherein the processing component processes the biosignal 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 biosignal within multiple time periods.

[0089] The present application also provides a physiological state regulation method, which can be implemented by the biological health system, but the implementation device of the physiological state regulation method includes but is not limited to the structure of the biological health system described in the present embodiment. The physiological state regulation method comprises: obtaining the biosignal of the user in real time; calculating the physiological state regulation parameter based on the phase of the biosignal; 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 biosignal and the stimulation signal.

[0090] In some other embodiments, the physiological state regulation method described in the present application can be applied to an end-cloud interaction scenario. Figure 1A The structure schematic diagram of the end-cloud interaction scenario in these implementations is shown. As shown in Figure 1A The end-cloud interaction system 2 includes a wearable regulation device 20 and a cloud server 21, and the wearable regulation device 20 and the cloud server 21 can communicate with each other, and the communication mode is not limited to wired or wireless mode.

[0091] When the user is ready to enter a certain physiological state, the wearable regulation device 20 can be worn, at which time the wearable regulation device 20 will collect the biological signals of the user 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 regulation device 20 to output a stimulation signal to the user, so that the biological signals of the user and the stimulation signal are coupled, and the physiological state regulation of the user is realized through the coupling of the two. In addition, the cloud server 21 will also store the biological signal data and physiological state regulation data of the user in the physiological state cycle.

[0092] In some other embodiments, the physiological state regulation method described in the present application can be applied to another end-cloud interaction scenario. Figure 1B The structure diagram of the end-cloud interaction scenario in these implementations is shown. As Figure 1B shown, the end-cloud interaction system 2 includes the wearable regulation device 20, the cloud server 21, and the host computer 22, and the wearable regulation device 20, the cloud server 21, and the host computer 22 can communicate with each other, and the communication mode is not limited to wired or wireless mode.

[0093] At this time, when the user wears the wearable regulation device 20, the host computer 22 can be used to control the wearable regulation device 20 to collect the biological signals of the user in real time, and the physiological state regulation method provided in the embodiments of the present application is executed to generate a stimulation signal. At the same time, the biological signal data and physiological state regulation data of the user in the physiological state cycle can be uploaded to the host computer 22 through Bluetooth or other communication mode, and then transmitted to the cloud server 21 for storage.

[0094] It should be noted that the cloud server 21 can include one or more servers, or include one or more processing nodes, or include one or more virtual machines running on the server, and the cloud server 21 can also be referred to as a server cluster, a management platform, a physiological state regulation center, etc., and the present application does not make any limitation on this.

[0095] The following embodiments of the present application provide a physiological state regulation method, which can be applied to the biological health system as Figure 1C shown, or applied to the end-cloud interaction scenario as Figure 1C and Figure 1A shown. It should be noted that Figure 1B only as an embodiment. In fact, the present application does not make any limitation on the execution subject of part or all of the steps of the physiological state regulation method described in the following embodiments of the present application.

[0096] The technical solutions in the embodiments of the present application will be described in detail below with the brain electrical signals in the biological signals as an example and in combination with the drawings in the embodiments of the present application.

[0097] Figure 1C A flowchart of a physiological state regulation method according to an embodiment of the present application is shown in FIG. 1. As shown in FIG. 1, the physiological state regulation method includes steps S1-S3. Figures 1A to 1C

[0098] S1, acquiring brain electrical signals of a user in real time.

[0099] Specifically, the brain electrical signals of the user are acquired in real time. The acquisition area includes frontal electrodes Fp1, Fp2, F7, F8, Fpz, temporal electrodes Tp9, Tp10, central electrodes C3, C4, Cz, and occipital electrodes O1, O2, Oz.

[0100] In some embodiments, the frontal electrodes Fp1, Fp2, F7, F8 are selected to acquire the brain electrical signals of the user. The brain electrical signals include alpha waves, beta waves, theta waves, and delta waves. Different brain electrical signals represent different activity states of the user's brain, for example, beta waves correspond to an excited state of the user's brain, alpha waves correspond to a wakeful state of the user's brain, theta waves correspond to a light sleep state of the user, and delta waves correspond to a deep sleep state of the user.

[0101] Further, in some embodiments, after acquiring the brain electrical signals of the user, the brain electrical signals can also be preprocessed to improve the signal-to-noise ratio of the brain electrical signals and thus improve the signal quality. For example, the acquired brain electrical signals can be re-referenced, filtered, de-trended, etc. Among them, the filtering process can use a 0.1-45 Hz band-pass filter (such as a second-order Butterworth filter) to filter the acquired brain electrical signals, and a notch filter to filter out 50 Hz power frequency and its harmonic interference, etc. The present application is not limited to the specific preprocessing method.

[0102] S2, calculating a sleep regulation parameter (one of the physiological state regulation parameters) based on the phase of the brain electrical signals.

[0103] In order to enable the stimulation signal to be coupled with the brain electrical signals and improve the synchronization and stability between the stimulation signal and the brain electrical signals, the present application first calculates a sleep regulation parameter in real time according to the phase of the brain electrical signals of the user, to further generate a stimulation signal based thereon, so as to realize that the stimulation signal is modulated by the phase of the brain electrical signals. Figure 2 A flowchart of a sleep regulation method according to an embodiment of the present application is shown in FIG. 2. As shown in FIG. 2, calculating a sleep regulation parameter based on the phase of the brain electrical signals includes steps S21 and S22. Figure 2

[0104] S21, selecting at least one target frequency band based on the brain electrical signals. ​​

[0105] In some embodiments, the target frequency band can be determined according to the dominant brain electrical signal frequency of the next sleep stage of the user's sleep cycle. For example, when the user is in the wake state (Wake stage) and wants to transit to the N1 stage through sleep regulation, the dominant brain electrical signal of this stage is alpha wave, so the alpha frequency band can be selected as the target frequency band. Alternatively, the beta frequency band can also be considered as the target frequency band to suppress the activity of beta waves in the brain through sleep regulation. Alternatively, the theta frequency band can also be considered as the target frequency band to enhance the activity of theta waves in the brain through sleep regulation. Alternatively, the beta frequency band and the theta frequency band can also be selected as the target frequency band. That is, whether one 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, thereby improving the sleep efficiency and sleep quality. Therefore, the specific selection of the target frequency band is not limited in the present application as long as the purpose is met.

[0106] It should be noted that the sleep stages in the sleep cycle can be divided into Wake stage, N1 stage, N2 stage, N3 stage and REM stage, or N3 stage and REM stage can be divided into one stage according to the user's sleep cycle, indicating that the user will complete the first sleep cycle of falling asleep and will enter the second sleep cycle, at this time the user can no longer be regulated. Therefore, the sleep stages in the user's sleep cycle can be divided according to the regulation needs, and the present application does not make any limitation thereto.

[0107] In some embodiments, when the target frequency band is selected, the corresponding target frequency band can also be preset according to the sleep stage in the sleep cycle, and the corresponding target frequency band can be automatically selected according to the change of the user's sleep stage. For example, Figure 3 As shown in FIG. 8, the step of selecting at least one target frequency band based on the brain electrical signal includes steps S211 to S213.

[0108] S211, initially selecting the target frequency band.

[0109] S212, judging whether the sleep stage of the user changes based on the brain electrical signal.

[0110] S213, if the sleep stage changes, reselecting the target frequency band, so that the target frequency band corresponds to the sleep stage.

[0111] For example, the sleep stages in a sleep cycle of a user are divided into a first sleep stage, a second sleep stage, a third sleep stage, and a fourth sleep stage. The first sleep stage corresponds to the Wake stage, the second sleep stage corresponds to the N1 stage, the third sleep stage corresponds to the N2 stage, and the fourth sleep stage corresponds to the N3 stage or the REM stage. The target frequency band of the first sleep stage is determined as the alpha frequency band, and the alpha frequency band is initially selected. The target frequency band of the second sleep stage is the theta frequency band, and the target frequency band of the third sleep stage is the delta frequency band. When the sleep stage of the user changes, the target frequency band corresponding to the changed sleep stage is selected, and when the user enters the fourth sleep stage, the sleep regulation is no longer needed. For another example, the target frequency band of the first sleep stage can be set as the alpha frequency band and the theta frequency band, and the alpha frequency band and the theta frequency band are initially selected. The target frequency band of the second sleep stage is set as the theta frequency band and the delta frequency band, and the target frequency band of the third sleep stage is set as the delta frequency band. When the sleep stage of the user changes, the target frequency band corresponding to the current sleep stage is selected, and when the user enters the fourth sleep stage, the sleep regulation is no longer needed.

[0112] To this end, the embodiment of the present application further provides an implementation manner for determining whether the sleep stage of the user changes based on the EEG signal, so as to support the automatic selection of the target frequency band corresponding to the change of the sleep stage of the user provided by the above-mentioned embodiments. As shown in the following table, the determination of whether the sleep stage of the user changes based on the EEG signal includes steps S2121 to S2123. Figure 3

[0113] S2121, obtaining at least one sleep staging result of the user based on the EEG signal in a preset time window.

[0114] Specifically, the EEG signal in the preset time window is segmented and processed according to time, and at least one sleep staging result in the preset time window is obtained based on the EEG signal in a plurality of segmented time.

[0115] In some embodiments, a 10-minute time window is preset, and a 30-second segmentation processing is performed. Then, the EEG signal in a plurality of 30 seconds is inferred and determined to obtain the corresponding sleep staging result. For example, the user is in a wake state in 10 minutes, the EEG signal in 10 minutes is continuously obtained, and the sleep staging result in 20 30 seconds is determined, including the Wake stage, the REM stage, the N1 stage, the N2 stage, or the N3 stage.

[0116] Further, the machine learning algorithm can be used to infer the EEG signal to obtain the sleep staging result. The machine learning algorithm used can include a neural network, an attention mechanism, a decision tree, a support vector machine, etc., and the present application is not limited thereto.

[0117] ​It should be noted that the specific length of the preset time window can be set as needed, and this application is not limited thereto.

[0118] S2122. Classify and count at least one of the sleep stage results to obtain at least one stage count result.

[0119] Specifically, the sleep stage results within each time window are counted to obtain at least one stage count result. Taking the aforementioned 10-minute time window as an example, there are 12 N1 stages, 5 wake stages, and 3 N2 stages. Therefore, the three stage count results are 12, 5, and 3, respectively. In some other embodiments, there may be 20 N1 stages, in which case only one stage count result is 20.

[0120] S2123. Determine the relationship between at least one of the phase count results within the preset time window and any preset threshold. If any of the phase count results is higher than the preset threshold, then determine that the user's sleep stage has changed.

[0121] Specifically, a threshold-based logic is used to determine whether a user has changed their sleep stage. That is, when the stage count result is greater than a preset threshold, it can be determined that the user has entered the next sleep stage. For example, if the stage count result for stage N1 within a past time window is greater than the preset threshold, the user is determined to have entered the second sleep stage. If the stage count result for stage N2 within the time window is greater than the preset threshold, the user is determined to have entered the third sleep stage. If the stage count result for stage N3 or REM within the time window is greater than the preset threshold, the user is determined to have entered the fourth sleep stage.

[0122] S22. Calculate the 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.

[0123] 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 that target frequency band. That is, when one target frequency band is determined, the phase information of that 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 that target frequency band. Alternatively, when multiple target frequency bands are determined, the phase information of each of the multiple target frequency bands is extracted, and the sleep regulation frequency, sleep regulation phase, and / or sleep regulation amplitude corresponding to each target frequency band is calculated based on the phase of each target frequency band. In practice, 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 must be calculated based on the phase information of each target frequency band.

[0124] The following will take one target frequency band as an example to illustrate the specific process of calculating the corresponding sleep regulation parameter based on the phase.

[0125] 1) Calculate the sleep regulation frequency based on the phase of the target frequency band.

[0126] 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.

[0127] In some embodiments, the phase of the target frequency band is , the initial frequency of the stimulation signal is , and the sleep regulation frequency can be represented as:

[0128] (1)

[0129] As can be seen from formula (1), 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 through step S21, the corresponding sleep regulation frequency is calculated for each target frequency band using formula (1).

[0130] In some embodiments, the phase of the target frequency band is , the initial frequency of the stimulation signal is , and the sleep regulation phase can be represented as:

[0131] (2)

[0132] As can be seen from formula (1), 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 through step S21, the corresponding sleep regulation phase is calculated for each target frequency band using formula (2).

[0133] 3) Calculate the sleep regulation phase based on the phase of the target frequency band.

[0134] 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.

[0135] In some embodiments, the phase of the target frequency band is , the initial frequency of the stimulation signal is , and the initial amplitude is , and the sleep regulation amplitude can be represented as:

[0136] (3)

[0137] As can be seen from equation (3), the sleep regulation amplitude will actually follow the phase of the target frequency band in real time. Wherein, B represents the parameter that the regulation amplitude is affected by the phase modulation. When a plurality of target frequency bands are determined through step S21, the corresponding sleep regulation amplitude is calculated for each target frequency band by using equation (3).

[0138] Further, in some embodiments, the initial amplitude corresponds to the amplitude of the target frequency band. That is, when one of the target frequency bands is determined according to step S21, the amplitude corresponding to the target frequency band can be obtained according to the brain electrical signal, and used as the initial amplitude . It should be noted that when a plurality of target frequency bands are determined, there will actually be a plurality of initial amplitudes .

[0139] In other embodiments, the initial amplitude follows the change of the sleep stage to adaptively adjust by a preset adjustment value. That is, if the sleep stage changes, the initial amplitude is adaptively adjusted by a preset adjustment value.

[0140] For example, the initial amplitude can be directly set based on the amplitude of the target frequency band. Then, the initial amplitude will be adaptively adjusted according to the preset value as the sleep stage of the user changes. For example, in the first sleep stage, the amplitude of the alpha frequency band can be directly set as the initial amplitude. Then, as the user enters the next sleep stage, the initial amplitude will be adaptively adjusted downward by a percentage until the fourth sleep stage is entered, and then it is reduced to the lowest or stopped. For another example, when the sleep stage of the user returns to the third sleep stage or the second sleep stage, the initial amplitude will be adjusted upward accordingly to help the user deepen sleep. If the user wakes up during sleep and returns to a wakeful state, the initial amplitude will be adjusted upward accordingly to help the user fall asleep again. That is, the initial amplitude is adaptively adjusted upward or downward according to the change of the sleep stage of the user. Wherein, the change of the sleep stage can be determined according to steps S2121 to S2123 in the above content.

[0141] Further, for the phase of the target frequency band in equations (1) to (3) , the embodiment of the present application provides an implementation manner for obtaining the phase of the target frequency band. As shown in Figure 4 , obtaining the phase of the target frequency band includes steps S221 to S223.

[0142] S221, obtaining a target frequency; the target frequency corresponds to the target frequency band.

[0143] Specifically, the implementation of obtaining the target frequency comprises: obtaining 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.

[0144] In some embodiments, after the electroencephalogram signal obtained in step S1 is filtered, N-point sliding window processing is performed, and the frequency spectrum feature of the electroencephalogram signal of the target frequency band is calculated using fast Fourier transform. Based on this, the frequency value with the highest frequency energy in the target frequency band is extracted as the individual center frequency ICF, that is, the individual center frequency is taken as the target frequency. That is, for each target frequency band, the corresponding target frequency can actually be obtained.

[0145] S222, obtaining a first filter phase offset and a second filter phase offset of the target frequency.

[0146] Specifically, the first filter phase offset of the target frequency band is obtained . The first filter phase offset is the phase offset generated after the electroencephalogram signal is filtered by the first filter. In some embodiments, after the electroencephalogram signal of the user is obtained in step S1, a 0.1-45Hz band-pass filter (such as a second-order Butterworth filter) can be used as the first filter to filter the collected electroencephalogram signal. At this time, the first filter phase offset of the target frequency can be calculated according to the parameters of the 0.1-45Hz band-pass filter .

[0147] Specifically, the second filter phase offset of the target frequency band is obtained . The second filter phase offset is the phase offset of the frequency domain signal after the frequency domain signal is processed by the second filter, and the frequency domain signal is obtained based on the electroencephalogram signal. The second filter is a filter corresponding to the target frequency band.

[0148] In some embodiments, the implementation of obtaining the second filter phase offset comprises: after the electroencephalogram signal obtained in step S1 is filtered, N-point sliding window processing is performed, and the frequency spectrum feature of the electroencephalogram signal of the target frequency band is calculated using fast Fourier transform, that is, X[f]. Then, the Hilbert transform is performed on X[f], the negative frequency component value is set to zero, and the positive frequency component is multiplied by 2 to obtain Y[f]. The second filter of the target frequency band is applied to Y[f], the filtered frequency domain signal is obtained, and the amplitude and phase response of the pulse frequency are obtained according to the parameters of the filter, so that the second filter phase offset of the target frequency .

[0149] S223, obtaining the phase of the target frequency band based on the first filtered phase offset and the second filtered phase offset.

[0150] Specifically, the filtered frequency domain signal is inverse transformed back to the time domain to obtain a complex value signal Z[n], a real-time phase angle is obtained through analysis of the complex value signal Z[n], and based on this, the first filtered phase offset and the second filtered phase offset are subtracted. and Then the phase information of the target frequency band can be extracted.

[0151] In order to further illustrate the specific process of obtaining the phase of the target frequency band, the following will take the alpha frequency band as the target frequency band for illustration. When the alpha frequency band is determined as the target frequency band, a 0.1-45Hz band-pass filter is used to filter the collected electroencephalogram signals, and the filtered electroencephalogram signals are subjected to N-point sliding window processing, and the fast Fourier transform is used to calculate the frequency spectrum characteristics of the alpha frequency band electroencephalogram signals, i.e. X[f], and the frequency value with the highest frequency energy in the alpha frequency band is extracted as the individual center frequency ICF. At this time, the first filtered phase offset of the individual center frequency ICF can be obtained according to the parameters of the 0.1-45Hz band-pass filter . Then, the Hilbert transform is applied to X[f], the negative frequency component value is set to zero, and the positive frequency component is multiplied by 2 to obtain Y[f]. And the filter of the alpha frequency band (7.5-12.5Hz) is applied to filter Y[f] to obtain the filtered frequency domain signal, and the second filtered phase offset of the individual center frequency ICF is obtained based on this . Finally, the filtered frequency domain signal is inverse transformed back to the time domain to obtain a complex value signal Z[n], a real-time phase angle is obtained through analysis of the complex value signal Z[n], and based on this, the first filtered phase offset and the second filtered phase offset are subtracted. and Then the phase information of the alpha frequency band can be extracted.

[0152] Further, the initial frequency in the formula (1) to formula (3) may be selected within the frequency range of the target frequency band. For example, when the target frequency band is the alpha band, the frequency range thereof is 7.5~12.5Hz. Therefore, any frequency value within the frequency range can be actually preset as the initial frequency For example, when the target frequency band is the alpha frequency band, any frequency value within the frequency band range (7.5~12.5Hz) of the alpha frequency band can be taken as the initial frequency, and the sleep regulation frequency corresponding to the alpha frequency band is calculated accordingly. Alternatively, the target frequency (individual center frequency ICF) of the target frequency band can be taken as the initial frequency, and the sleep regulation frequency corresponding to the target frequency band is calculated accordingly. In addition, the initial frequency can also be a pre-set frequency within 70Hz.

[0153] S3, generating a stimulation signal based on the sleep regulation parameter to regulate the user's sleep through the coupling of the stimulation signal and the brain electrical signal.

[0154] Specifically, after the sleep regulation parameter is calculated, a stimulation signal can be generated based on the sleep regulation parameter. As described above, in step S2, when calculating the sleep regulation parameter, one or more of the sleep regulation frequency, the sleep regulation phase, and the sleep regulation amplitude can be selected. That is, the sleep regulation frequency, the sleep regulation phase, and the sleep regulation amplitude are in a relationship of and / or. 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.

[0155] The stimulation signal includes a sound wave stimulation signal, a light stimulation signal, an electrical stimulation signal, and / or a vibration stimulation signal.

[0156] To further illustrate the generation of the stimulation signal based on the sleep regulation parameter, the specific process of generating the frequency, phase, and / or amplitude of the stimulation signal in real time based on the sleep regulation frequency, the sleep regulation phase, and / or the sleep regulation amplitude will be described below.

[0157] (A) Generating the frequency of the stimulation signal in real time based on each of the sleep regulation frequencies.

[0158] Specifically, when each target frequency band corresponds to a sleep regulation frequency based on formula (1) in the above content Then, 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 electroencephalogram signal, which means that the stimulation signal and the electroencephalogram signal are coupled in frequency and phase. 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 electroencephalogram signal.

[0159] In some embodiments, when the frequency of the stimulation signal is generated in real time based on multiple sleep regulation frequencies, the generation can be performed according to the linear superposition method. That is, multiple sleep regulation frequencies are combined into a composite signal through linear superposition. The stimulation signal generated in real time by this method contains the components of all sleep regulation frequencies.

[0160] (B) The phase of the stimulation signal is generated in real time based on each sleep regulation phase.

[0161] Specifically, when the sleep regulation phase corresponding to each target frequency band is obtained based on formula (2) in the above content Then, 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 electroencephalogram signal, which means that the stimulation signal and the electroencephalogram signal are coupled in phase. 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 electroencephalogram signal.

[0162] 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 weights can be set according to the proportion of the frequency band component of each target frequency band in the brain. After weighted superposition, the phase of the stimulation signal is synthesized.

[0163] (C) The amplitude of the stimulation signal is generated in real time based on each sleep regulation amplitude.

[0164] Specifically, when the sleep control amplitudes corresponding to each target frequency band are obtained based on the formula (3) in the above content Then, the amplitude of the stimulation signal can be generated in real time based on the sleep control amplitudes. At this time, when only one target frequency band is selected, the sleep control amplitude obtained based on the formula (3) is actually the amplitude of the stimulation signal. When multiple target frequency bands are determined, multiple sleep control amplitudes will be obtained based on the formula (3), and the amplitude of the stimulation signal should be generated in real time based on the multiple sleep control amplitudes. Since each sleep control 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 control amplitude is actually modulated by the phase of the brain electrical signal, which means that the amplitude and phase of the stimulation signal are coupled with the brain electrical signal. That is, when the phase of the stimulation signal is generated in real time based on each sleep control amplitude, the amplitude of the stimulation signal is coupled with the phase of the brain electrical signal.

[0165] In some embodiments, when the amplitude of the stimulation signal is generated in real time based on multiple sleep control amplitudes, the generation coefficient proportions of the multiple sleep control amplitudes need to be determined, and the amplitude of the stimulation signal is generated in real time based on the generation coefficient proportions of the multiple sleep control amplitudes. The sum of the generation coefficients of the multiple sleep control amplitudes is 1. At this time, after the generation coefficient proportions of the multiple sleep control amplitudes are determined, the multiple sleep control amplitudes can be multiplied by the respective generation coefficient proportions, and the multiple multiplication results are added to obtain the amplitude of the stimulation signal.

[0166] It should be noted that the amplitude of the stimulation signal should not exceed 70 dB.

[0167] In some embodiments, determining the generation coefficients of the multiple sleep control amplitudes includes: calculating the difference between the amplitude of each target frequency band and the target amplitude, and determining the generation coefficient proportions of the multiple sleep control amplitudes based on the proportions of the different differences. The target amplitude corresponds to a target sleep stage.

[0168] For example, taking the selection of the alpha frequency band and the theta frequency band as target frequency bands as an example, the amplitudes of the alpha frequency band and the theta frequency band are obtained according to the brain electrical signal obtained in real time. At this time, the target sleep stage is the N1 stage, and the target amplitude corresponding to the N1 stage can be obtained according to the historical sleep data or the database of the user. Then, the difference 1 between the amplitude of the alpha frequency band and the target amplitude is calculated, and the difference 2 between the amplitude of the theta frequency band and the target amplitude is calculated. Then, the generation coefficient proportions of the sleep control amplitude 1 and the sleep control amplitude 2 are determined based on the proportions of the difference 1 and the difference 2. The sleep control amplitude 1 corresponds to the alpha frequency band, and the sleep control amplitude 2 corresponds to the theta frequency band.

[0169] It can be seen that the generation coefficient ratio of the plurality of sleep regulation amplitudes actually changes in real time with the strength ratio of the difference between the amplitudes of the plurality of target frequency bands and the target amplitude. For example, the ratio of difference 1 and difference 2 may initially be 4:1, and the generation coefficient ratio of sleep regulation amplitude 1 and sleep regulation amplitude 2 is 4:1. As the sleep regulation progresses, the user is closer to the target sleep stage N1, and at this time the frequency band component of the theta frequency band in the electroencephalogram should increase, and at this time the ratio of difference 1 and difference 2 may change to 2:2, and the generation coefficient ratio of sleep regulation amplitude 1 and sleep regulation amplitude 2 will also be 2:2.

[0170] In another embodiment, determining the generation coefficients of the plurality of sleep regulation amplitudes includes: obtaining the ratio of the plurality of target frequency bands when the user is in different sleep stages, and determining the generation coefficient ratio of the plurality of sleep regulation amplitudes based on the ratio of the plurality of target frequency bands.

[0171] For example, the ratio of the plurality of target frequency bands in the electroencephalogram at each sleep stage can be determined by large-scale sleep data in a public database, and the generation coefficient ratio of the plurality of sleep regulation amplitudes can be determined based on the ratio of the plurality of target frequency bands. Alternatively, the sleep data of different populations can be determined by experiments, and the ratio of the plurality of target frequency bands in the electroencephalogram at each sleep stage can be determined, and the generation coefficient ratio of the plurality of sleep regulation amplitudes can be determined based on the ratio of the plurality of target frequency bands. Alternatively, the ratio of the plurality of target frequency bands in the electroencephalogram at each sleep stage can also be determined by historical sleep data of the user, and the generation coefficient ratio of the plurality of sleep regulation amplitudes can be determined based on the ratio of the plurality of target frequency bands.

[0172] Further, after determining the ratio of the plurality of target frequency bands in the electroencephalogram at each sleep stage, a large number of experimental tests can be performed 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 of each sleep regulation amplitude.

[0173] Therefore, as described above, the stimulation signal generated according to the sleep regulation parameter will be in-phase coupled, phase coupled and / or amplitude-phase coupled with the brain electrical signal. At this time, the user can be regulated in sleep by the coupling of the stimulation signal and the brain electrical signal. In fact, when the stimulation signal and the brain electrical signal are coupled, the brain electrical coupling signal will be generated in the user's brain. The brain electrical coupling signal actually represents a new signal formed by the coupling of the user's brain electrical signal and the stimulation signal under the action of the stimulation signal. That is, under the action of the in-phase coupled stimulation signal, the user's brain electrical signal will change to generate the brain electrical coupling signal. And the brain electrical coupling signal will induce the increase of the brain electrical signal of the target frequency band in the user's brain, induce the brain to enter the corresponding sleep state, or also can reduce the generation of the brain electrical signal of the target frequency band in the user's brain.

[0174] Further, the sleep regulation of the user by the coupling of the stimulation signal and the brain electrical signal comprises: determining an induced phase according to the phase of the target frequency band of the brain electrical signal and the induced response potential delay of the stimulation signal, and the stimulation signal and the brain electrical signal start to produce coupling at the induced phase.

[0175] In fact, when the stimulation signal acts on the user's brain, the induced response potential delay will be generated. 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 induced response potential is 62.5ms. Then assuming that the target frequency band is δ frequency band, after obtaining the sleep regulation parameter, the corresponding sound wave stimulation signal is generated to act on the user's brain. Assuming that the target frequency of the δ frequency band is 2.5Hz and the period is 400ms, the phase shift of the sound wave stimulation signal should be (62.5 / 400)*360=15.625°. Then in order to make the brain electrical signal and the stimulation signal produce coupling, the induced phase can be determined as 360°-15.625°=344.375°, that is, the sound wave stimulation signal acts on the user's brain at 344.375°. At this time, the induced response potential of the sound wave stimulation signal will be coupled with the wave crest of the brain electrical signal, so that the brain electrical coupling signal is generated at the wave crest of the brain electrical signal due to the action of the sound wave stimulation signal, that is, the brain electrical signal of the δ frequency band in the user's brain is effectively induced by the coupling of the stimulation signal and the brain electrical signal.

[0176] In some embodiments, to reduce the generation of the brain electrical signal of the target frequency band through the coupling of the stimulation signal and the brain electrical signal, the evoked response potential of the stimulation signal should be coupled with the trough of the brain electrical signal, so that the brain electrical signal generates the brain electrical coupling signal at the trough, thereby reducing the generation of the brain electrical signal of the target frequency band in the user's brain. Taking the above-mentioned sound wave stimulation signal as an example, assuming that the target frequency of the delta 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 evoked phase is determined as 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 is coupled with the trough of the brain electrical signal, and the brain electrical signal generates the brain electrical coupling signal at the trough due to the action of the sound wave stimulation signal. At this time, the generation of the brain electrical signal of the delta frequency band in the user's brain can be reduced through the coupling of the stimulation signal and the brain electrical signal.

[0177] It should be noted that the delay of the stimulation evoked response potential is different for different types of stimulation. The above-mentioned embodiment of the present application only takes the delay of the evoked response potential of the sound wave stimulation as an example of 62.5 ms to illustrate the specific process of generating coupling.

[0178] Figure 5 An application diagram of a sleep regulation method provided by an embodiment of the present application is shown. As shown in Figure 6 When the sleep regulation method is started, the brain electrical 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 parameter in real time. The stimulation signal is generated in real time based on the sleep regulation parameter, and the stimulation signal acts on the brain, so as to realize sleep regulation through the coupling of the stimulation signal and the brain electrical signal.

[0179] The present application calculates the personalized sleep regulation parameter of the user based on the phase of the real-time brain electrical signal to generate the stimulation signal in real time, so that the stimulation signal is coupled with the brain electrical signal in frequency, phase and / or amplitude, so as to better regulate the brain neural oscillation, improve the synchronization and stability between the neural stimulation and the intrinsic brain activity, and improve the night sleep efficiency and sleep quality of the user.

[0180] The protection scope of the sleep regulation method described in the embodiments of the present application is not limited to the order of the steps listed in the embodiments. Any scheme realized by adding, replacing or deleting steps of the prior art according to the principle of the present application is included in the protection scope of the present application.

[0181] The embodiment of the present application further provides a sleep regulation system, which can implement the sleep regulation method in the physiological state regulation method according to the embodiment of the present application, but the implementation device of the sleep regulation system according to the present application includes but is not limited to the structure of the sleep regulation system listed in the embodiment, and any structure transformation and replacement of the prior art according to the principle of the present application is included in the protection scope of the present application.

[0182] Figure 7 The structure of the sleep regulation system according to the embodiment of the present application is shown in the schematic diagram, as shown in the figure, the sleep regulation system 4 includes an acquisition module 41, a generation module 42 and a regulation module 43. Figure 7

[0183] The acquisition module 41 is used for acquiring the brain electrical signal of the user in real time.

[0184] The generation module 42 is used for calculating the sleep regulation parameter based on the frequency, phase and / or amplitude of the brain electrical signal.

[0185] The regulation module 43 is used for generating a stimulation signal based on the sleep regulation parameter, so as to regulate the sleep of the user through the coupling effect of the stimulation signal and the brain electrical signal.

[0186] It should be noted that the structure and principle of the acquisition module 41, the generation module 42 and the regulation module 43 correspond to the steps of the sleep regulation method one by one, so they will not be described in detail here.

[0187] It should be noted that the sleep regulation system provided in the embodiment of the present application is only a virtual system structure setting. The specific implementation subject of each module is not limited by the present application.

[0188] In several embodiments provided by 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. The division of the modules / unit is only a logical function division. There can be another division manner in actual implementation. For example, a plurality of modules or units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the modules shown or discussed can be indirect coupling or communication connection through some interfaces, devices or modules, and can be electrical, mechanical or other forms.

[0189] ​The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this 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.

[0190] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0191] This application also provides a wearable device, including: one or more sensors configured to collect a user's biosignals in real time; one or more processors; and one or more memories, wherein the memories store computer-readable code that, when executed by the one or more processors, implements the functions of the biohealth system as described.

[0192] The wearable device can be used by means of Figure 8 The architecture of the exemplary computing device shown is used for implementation. Figure 8 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 devices 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, as well as program instructions executed by the CPU. The computing device 900 may also include a user interface 980. Of course, Figure 9 The architecture shown is merely exemplary and can be omitted as needed when implementing different devices. Figure 9 Figure 9 Figure 9 One or more components in the computing device shown.

[0193] The embodiments of the present application further provide a computer readable storage medium, which has instructions stored thereon. The instructions, when executed by a processor, cause the processor to perform the physiological state regulation method according to the embodiments of the present application. Those skilled in the art can understand that all or part of the steps of the method described in the above embodiments can be performed by computer readable instructions stored in the computer readable storage medium. The computer readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM), cache memory, and the like. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, and the like.

[0194] The embodiments of the present application further provide a computer program product or 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 the above embodiments.

[0195] The descriptions of the corresponding processes or structures of the above various figures are each focused on, and the parts not described in detail in a certain process or structure can be referred to the related descriptions of other processes or structures.

[0196] The above embodiments are only illustrative of the principles and effects of the present application, and are not used to limit the present application. Any person skilled in the art can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes completed by those skilled in the art without departing from the spirit and technical thought of the present application should be covered by the claims of the present application.

Claims

1. A bio-health system, characterized by, The method comprises: detecting a biological signal of a user in real time; calculating a physiological state regulation parameter based on a phase of the biological signal, generating a stimulation signal based on the physiological state regulation parameter; the processing component selects at least one target frequency band based on the biological signal, calculates a corresponding physiological state regulation parameter based on a phase of each target frequency band; the processing component calculates a corresponding physiological state regulation frequency and / or a physiological state regulation phase based on a phase of each target frequency band and a preset initial frequency of the stimulation signal; outputting the stimulation signal, and performing physiological state regulation on the user through coupling of the biological signal and the stimulation signal.

2. The bio-health system according to claim 1, characterized in that, Further comprising: The physiological state regulation parameter comprises a physiological state regulation frequency, a physiological state regulation phase, and / or a physiological state regulation amplitude.

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

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

5. The bio-health system according to claim 2, characterized by the fact that, Further comprising: The processing component generates a frequency of the stimulation signal in real time based on each physiological state regulation frequency, so that the stimulation signal is frequency-coupled with the biological signal; The processing component generates a phase of the stimulation signal in real time based on each physiological state regulation phase, so that the stimulation signal is phase-coupled with the biological signal; And / or The processing component generates an amplitude of the stimulation signal in real time based on each physiological state regulation amplitude, so that the stimulation signal is amplitude-coupled with the biological signal.

6. The bio-health system according to claim 5, characterized by the fact that, Further comprising: The processing component generates an amplitude of the stimulation signal in real time based on a plurality of physiological state regulation amplitudes; wherein the processing component determines a generation coefficient ratio of a plurality of physiological state regulation amplitudes, and generates an amplitude of the stimulation signal in real time based on the generation coefficient ratio of a plurality of physiological state regulation amplitudes; The sum of the generation coefficients of a plurality of physiological state regulation amplitudes is 1.

7. The bio-health system according to claim 6, characterized by the fact that, Further comprising: The processing component determines a generation coefficient of a plurality of physiological state regulation amplitudes; wherein the processing component calculates a difference value between an amplitude of each target frequency band and a target amplitude, and determines a generation coefficient ratio of a plurality of physiological state regulation amplitudes based on a proportion of different difference values; The target amplitude corresponds to a target physiological state stage.

8. The bio-health system according to claim 6, characterized by the fact that, Further comprising: The processing component determines a generation coefficient of a plurality of physiological state regulation amplitudes; wherein the processing component obtains a proportion of a plurality of target frequency bands when the user is in different physiological state stages, and determines a generation coefficient ratio of a plurality of physiological state regulation amplitudes based on the proportion of a plurality of target frequency bands.

9. The biohealth system of claim 1, wherein, Further comprising: The physiological state regulation performed by the processing component on the user through coupling of the biological signal and the stimulation signal comprises: The processing component determines an evoked phase according to a phase of the target frequency band and a latency of an evoked response potential of the stimulation signal, The output component outputs the stimulation signal, so that the biological signal and the stimulation signal start to produce coupling effect at the evoked phase, to achieve physiological state regulation of the user at a physiological state stage corresponding to the target frequency band.

10. The bio-health system according to claim 1 or 9, characterized by the fact that, Further comprising: The processing component acquires a target frequency; The target frequency corresponds to the target frequency band; The processing component acquires a first filter phase offset and a second filter phase offset of the target frequency; The first filter phase offset is a phase offset of the biological signal after being processed by a first filter, and the second filter phase offset is a phase offset of a frequency domain signal after being processed by a second filter, the frequency domain signal being acquired based on the biological signal; The processing component acquires the phase of the target frequency band based on the first filter phase offset and the second filter phase offset.

11. The bio-health system according to claim 10, characterized by the fact that, Further comprising: The processing component acquires a spectral feature corresponding to the target frequency band, and extracts a frequency value with the highest frequency energy as the target frequency based on the spectral feature.

12. The biohealth system of claim 1, wherein, Further comprising: The processing component initially selects the target frequency band, and determines whether a physiological state stage of the user changes based on the biological signal; The processing component reselects the target frequency band in the case that 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 keeps the target frequency band unchanged in the case that the physiological state stage of the user changes.

13. The bio-health system according to claim 4 or 12, characterized by the fact that, Further comprising: The processing component determines whether a physiological state stage of the user changes based on the biological signal; wherein the processing component acquires 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 to acquire at least one staging count result, determines a relationship between at least one of the staging count results and any preset threshold value, and determines that the physiological state stage of the user changes if any of the staging count results is higher than the preset threshold value.

14. The bio-health system according to claim 10, characterized by the fact that, Further comprising: The processing component acquires 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 and processes the biological signal within the preset time window according to time, and acquires at least one physiological state staging result within the preset time window based on the biological signal within a plurality of time periods.

15. The biohealth system of claim 1, wherein, Further comprising: The biological signal includes an electroencephalogram signal, an electrocorticogram signal, a deep electrode signal, an electrooculogram signal, an electromyogram signal, an electrocardiogram signal, a nuclear magnetic resonance image signal, and / or a near-infrared brain function imaging signal.

16. The biohealth system of claim 1, wherein, Further comprising: The stimulation signal is a health stimulation signal, including a sound wave stimulation signal, a light stimulation signal, an electric stimulation signal, an ultrasonic wave stimulation signal, a magnetic stimulation signal, and / or a vibration stimulation signal.

17. A wearable device, comprising: Comprising: 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 have stored computer readable code therein to implement the functionality of the bio-health system of any one of claims 1-16 when executed by the one or more processors.

18. A computer-readable storage medium, characterized in that, instructions stored on the computer readable storage medium to implement the functionality of the bio-health system of any one of claims 1-16 when executed by a processor.

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