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

Abstract

The present 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 assembly configured to collect a biological signal of a user in real time; a processing assembly configured to calculate, on the basis of a phase of the biological signal, a physiological state regulation parameter and generate, on the basis of the physiological state regulation parameter, a stimulation signal; and an output assembly configured to output the stimulation signal, so that an coupling effect of the biological signal and the stimulation signal regulates a physiological state of the user. In the present application, the biological signal of the user is acquired in real time, and the personalized physiological state regulation parameter of the user is calculated on the basis of the phase of the biological signal so as to generate the stimulation signal in real time, thereby implementing physiological state regulation of the user by means of the coupling effect of the stimulation signal and the biological signal.

Description

Biological health systems, physiological state regulation methods, wearable devices and storage media Technical Field

[0001] This application belongs to the field of bio-health technology, and in particular relates to a bio-health system, a method for regulating physiological state, a wearable device, and a storage medium. Background Technology

[0002] Physiological condition, as an indispensable basic physiological need for human life, plays a vital role in maintaining physical health, promoting mental balance, and restoring energy. A good physiological state not only makes one feel refreshed and energetic, but is also a key factor in maintaining the immune system, heart health, and cognitive function. However, the fast pace of modern life, high-pressure work, the widespread use of electronic products, and irregular lifestyles often lead to many people experiencing physiological problems.

[0003] Current physiological disorders primarily rely on drug intervention, which often leads to adverse reactions and drug resistance. In recent years, health products on the market mainly fall into two categories: pure detection products and pure intervention products. There is no effective closed loop between detection and intervention, making it difficult for users to achieve the desired effects. Traditional physiological monitoring products are mostly used in hospital environments, are bulky, have low wearing comfort, require professional operation and interpretation, and are inconvenient for daily use. Smart bracelets and watches, due to limitations in their detection methods and physiological signal monitoring, have significant errors, and coupled with the lack of effective intervention measures, cannot improve users' physiological conditions. Intervention products mainly rely on open-loop neural stimulation, but lack a mechanism for real-time feedback and regulation based on actual biological activity, thus greatly reducing their effectiveness.

[0004] Summary of the Invention

[0005] The purpose of this application is to provide a biological health system, a physiological state regulation method, a wearable device, and a storage medium to solve the technical problem of how to regulate the user's biological activity state in real time according to health needs.

[0006] In a first aspect, this application provides a biological health system, comprising: a detection component configured to collect a user's biological signals in real time; a processing component configured to calculate physiological state regulation parameters based on the phase of the biological signals and generate a stimulation signal based on the physiological state regulation parameters; and an output component configured to output the stimulation signal, such that the coupling effect between the biological signals and the stimulation signal regulates the user's physiological state.

[0007] In one implementation of the first aspect, the processing component selects at least one target frequency band based on the biological signal and calculates corresponding physiological state regulation parameters based on the phase of each target frequency band; the physiological state regulation parameters include physiological state regulation frequency, physiological state regulation phase and / or physiological state regulation amplitude.

[0008] In one implementation of the first aspect, the processing component calculates the corresponding physiological state regulation frequency and / or the physiological state regulation phase based on the phase of each target frequency band and the preset initial frequency of the stimulation signal.

[0009] In one implementation of the first aspect, the processing component calculates the corresponding physiological state modulation amplitude based on the phase of each of the target frequency bands, the preset initial frequency and initial amplitude of the stimulation signal; wherein the initial amplitude corresponds to the amplitude of the target frequency band.

[0010] In one implementation of the first aspect, the processing component calculates the corresponding physiological state modulation 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 as the physiological state stage changes.

[0011] In one implementation of the first aspect, the processing component generates the frequency of the stimulation signal in real time based on the regulation frequency of each of the physiological states, thereby coupling the stimulation signal with the biological signal frequency; the processing component generates the phase of the stimulation signal in real time based on the regulation phase of each of the physiological states, thereby coupling the stimulation signal with the biological signal; and / or the processing component generates the amplitude of the stimulation signal in real time based on the regulation amplitude of each of the physiological states, thereby coupling the stimulation signal with the biological signal amplitude.

[0012] In one implementation of the first aspect, the processing component generates the 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 the plurality of 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 physiological state regulation amplitudes; the sum of the generation coefficients of the plurality of physiological state regulation amplitudes is 1.

[0013] In one implementation of the first aspect, the processing component determines generation coefficients for a plurality of physiological state regulation amplitudes; wherein, the processing component calculates the difference between the amplitudes of a plurality of target frequency bands and a target amplitude, and determines the generation coefficient ratio of the plurality of physiological state regulation amplitudes based on the ratio of different differences; the target amplitude corresponds to a target physiological state stage.

[0014] In one implementation of the first aspect, the processing component determines generation coefficients for a plurality of physiological state regulation amplitudes; wherein, the processing component acquires the proportions of a plurality of target frequency bands when the user is in different physiological state stages, and determines the proportions of generation coefficients for a plurality of physiological state regulation amplitudes based on the proportions of the plurality of target frequency bands.

[0015] In one implementation of the first aspect, the processing component regulates the user's physiological state through the coupling effect of the biosignal and the stimulation signal; wherein, the processing component determines the evoked phase based on the phase of the target frequency band and the evoked response potential delay of the stimulation signal; the output component outputs the stimulation signal, causing the biosignal to begin coupling with the stimulation signal at the evoked phase, so as to achieve physiological state regulation of the user at the physiological state stage corresponding to the target frequency band.

[0016] In one implementation of the first aspect, the processing component acquires a target frequency; the target frequency corresponds to the target frequency band; the processing component acquires a first filtered phase offset and a second filtered phase offset of the target frequency; the first filtered phase offset is the phase offset of the biological signal after processing by a first filter, and the second filtered phase offset is the phase offset of the frequency domain signal after processing 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 filtered phase offset and the second filtered phase offset.

[0017] In one implementation of the first aspect, the processing component acquires the spectral features corresponding to the target frequency band, and extracts the frequency value with the highest frequency energy based on the spectral features as the target frequency.

[0018] In one implementation of the first aspect, the processing component initially selects the target frequency band and determines whether the user's physiological state stage has changed based on the biosignal; if the user's physiological state stage changes, the processing component reselects the target frequency band so that the target frequency band corresponds to the user's current physiological state stage; or the processing component keeps the target frequency band unchanged if the user's physiological state stage changes.

[0019] In one implementation of the first aspect, the processing component determines whether the user's physiological state stage has changed based on the biosignal; wherein, the processing component obtains at least one physiological state stage result of the user based on the biosignal within a preset time window, classifies and counts the at least one physiological state stage result, obtains at least one stage count result, determines the relationship between the at least one stage count result within the preset time window and any preset threshold, and if any stage count result is higher than the preset threshold, it is determined that the user's physiological state stage has changed.

[0020] In one implementation of the first aspect, the processing component obtains at least one physiological state staging result of the user based on the biosignals within a preset time window; wherein, the processing component segments the biosignals within the preset time window according to time, and obtains at least one physiological state staging result within the preset time window based on biosignals within multiple time periods.

[0021] In one implementation of the first aspect, the biosignals include electroencephalogram (EEG) signals, cortical EEG signals, deep electrode signals, electrooculogram (EOG) signals, electromyogram (EMG) signals, electrocardiogram (ECG) signals, magnetic resonance imaging (MRI) signals, and / or near-infrared brain functional imaging (NIBMI) signals.

[0022] In one implementation of the first aspect, the stimulation signal is a health stimulation signal, including sound wave stimulation signal, light stimulation signal, electrical stimulation signal, ultrasonic stimulation signal, magnetic stimulation signal and / or vibration stimulation signal.

[0023] Secondly, this application provides a method for regulating physiological state, comprising: acquiring a user's biological signals in real time; calculating physiological state regulation parameters based on the phase of the biological signals; and generating a stimulation signal based on the physiological state regulation parameters, so as to regulate the user's physiological state through the coupling effect of the biological signals and the stimulation signal.

[0024] Thirdly, this application provides a wearable device, comprising: one or more sensors configured to acquire 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 above.

[0025] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the physiological state regulation method as described above.

[0026] As described above, the biological health system, physiological state regulation method, wearable device, and storage medium described in this application have the following beneficial effects:

[0027] This application uses a closed-loop neural stimulation method to acquire the user's biological signals in real time. Based on the phase of the real-time biological signals, it calculates the user's personalized physiological state regulation parameters to generate the frequency, phase, and / or amplitude of the stimulation signal in real time. This enables the stimulation signal to be frequency-coupled, phase-coupled, and / or amplitude-coupled with the biological signals, thereby achieving physiological state regulation of the user through the coupling effect of the stimulation signal and the biological signals.

[0028] In generating the amplitude of a stimulus signal based on the amplitude of physiological state regulation, this application determines the generation coefficient ratio of the physiological state regulation amplitude in a personalized manner, so that the amplitude of the stimulus signal generated based on this can more specifically regulate the user's physiological state.

[0029] This application accurately extracts the phase information of biological signals and uses it to determine the evoked phase, thereby enabling the stimulation signal and the biological signal to begin coupling at the evoked phase. This improves the synchronicity and stability between neural stimulation and the user's internal brain, muscles, eyes, and / or heart activities, thereby improving the user's physiological health. Attached Figure Description

[0030] Figure 1A shows a schematic diagram of one implementation structure of the biological health system described in this application.

[0031] Figure 1B shows a schematic diagram of an application scenario of the physiological state regulation method described in this application.

[0032] Figure 1C shows a schematic diagram of another application scenario of the physiological state regulation method described in this application.

[0033] Figure 2 shows a flowchart of the physiological state regulation method described in one embodiment of this application.

[0034] Figure 3 shows a flowchart of the physiological state regulation method described in one embodiment of this application.

[0035] Figure 4 shows a flowchart of the physiological state regulation method described in one embodiment of this application.

[0036] Figure 5 shows a flowchart of the physiological state regulation method described in one embodiment of this application.

[0037] Figure 6 shows a flowchart of the physiological state regulation method described in one embodiment of this application.

[0038] Figure 7 shows a schematic diagram of the application of the physiological state regulation method described in this application in one embodiment.

[0039] Figure 8 shows a schematic diagram of an embodiment of the biological health system described in this application in a sleep scenario.

[0040] Figure 9 shows a schematic diagram of the internal structure of the wearable device described in one embodiment of this application. Detailed Implementation

[0041] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0042] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0043] Furthermore, the use of terms such as "first" and "second" in this application is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. If the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed in this application.

[0044] Currently, the fast pace of modern life, high-pressure work, widespread use of electronic products, and irregular lifestyles often lead to physiological problems for many people. Prolonged exposure to these physiological issues can seriously impact both physical and mental health. To address this, numerous physiological intervention products exist on the market, such as smart bracelets or watches that can monitor and track physiological states, and smart sleep aids and aromatherapy products that improve the user's physiological environment and enhance their experience. However, these products lack personalized physiological regulation solutions and real-time feedback, significantly reducing their effectiveness.

[0045] To at least address the aforementioned technical problems, embodiments of this application provide a physiological state regulation method that can generate stimulation signals in real time based on the user's biological signals, and couple the stimulation signals with the biological signals at a determined evoked phase, thereby regulating the user's physiological state. This improves the synchronicity and stability between the stimulation signals and the activities of the brain, muscles, eyes, and / or heart, thereby improving the user's physiological health.

[0046] This application also provides a biological health system that can implement the aforementioned physiological state regulation method, the structure of which is shown in Figure 1A. The biological health system 1 includes a detection component 11, a processing component 12, an output component 13, and a power supply component 14.

[0047] The detection component 11 is configured to collect the user's biosignals in real time and send the collected biosignals to the processing component 12. The detection component 11 can be a wearable biosignal acquisition device or a sensor. The wearable biosignal acquisition device can be any form such as a headband, hat, head ring, or goggles, and this application does not impose any restrictions on it.

[0048] The processing component 12 is configured to calculate physiological state regulation parameters based on the phase of the biosignal, 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 with the detection component 11 into a wearable regulation device to execute the physiological state regulation method provided in this embodiment. In other implementations, the processing component 12 can also be a separate hardware system that interacts with the detection component 11 to receive the biosignals collected by the detection component 11; this application does not impose any limitations on this.

[0049] The output component 13 is configured to output the stimulation signal, enabling the coupling effect between the biological signal and the stimulation signal to regulate the user's physiological state. After the processing component 12 generates the stimulation signal, it sends the signal to the output component 13. The output component 13 outputs the stimulation signal to the user's brain at the evoked phase, thereby regulating the user's physiological state through the coupling effect between the stimulation signal and the biological signal. The output component 13 may include a stimulation element and a stimulation driving device. For example, when generating a sound wave stimulation signal, the stimulation element may be a speaker or a bone conduction oscillator, and the stimulation driving device may be an audio driving device. This application does not impose any limitations on the specific structure of the output component 13.

[0050] The power supply component 14 provides power to the entire biohealth system 1.

[0051] The software component 15 may be the GUI interface of the biological health system 1, or it may be an App application that interacts with the processing component. It may be deployed on any mobile terminal, including smartphones, smartwatches, PADs, tablets or other types of smart wearable devices, or deployed on a webpage. This application does not impose any restrictions on this.

[0052] In addition, the processing component 12 can send the biosignal data collected during the user's physiological cycle to the software component 15. The software component 15 will process and analyze the data again to output a detailed physiological status report so that the user can check his or her personal physiological status in a timely manner.

[0053] It should be noted that the structure of the biological health system 1 shown in Figure 1A is provided only as an example. In reality, the components of the biological health system 1 can exist physically separately, or two or more can be integrated into one component to perform some or all of the steps in the physiological state regulation method.

[0054] The biological health system described in the embodiments of this application will now be described in detail. The biological health system includes at least a detection component, a processing component, and an output component.

[0055] The detection component is configured to acquire the user's biosignals in real time. These biosignals include electroencephalogram (EEG) signals, cortical EEG signals, deep electrode signals, electrooculogram (EOG) signals, electromyogram (EMG) signals, electrocardiogram (ECG) signals, functional magnetic resonance imaging (fMRI) signals, and / or near-infrared spectroscopy (NIRS) signals.

[0056] Electroencephalography (EEG) records the electrical activity of neurons in the brain by placing electrodes on the scalp. The main characteristics of EEG signals include amplitude, phase, and frequency. EEG amplitude refers to the intensity or magnitude of the signal, usually measured in microvolts (μV). The amplitude of EEG signals varies in different brain regions and under different physiological or pathological conditions. EEG frequency refers to the oscillation rate of the signal, usually measured in hertz (Hz). EEG frequencies can be divided into different bands, such as delta waves (0.5-4Hz), theta waves (4-8Hz), alpha waves (8-13Hz), beta waves (13-30Hz), and gamma waves (above 30Hz). These bands are associated with different functional states of the brain. The phase of an EEG signal refers to the temporal position of the signal waveform. In EEG signals, phase reflects the temporal relationship between signals recorded by different electrodes, which is crucial for studying the synchronicity between brain regions.

[0057] Electrocorticometry (ECoG) is a method of recording brain electrical activity using electrodes placed on the surface of the cerebral cortex, offering high spatial and temporal resolution. The amplitude of the ECoG signal reflects the intensity of the electrical signals generated by neuronal activity. The amplitude of ECoG signals varies across different brain regions and under different physiological or pathological conditions. For example, the amplitudes of local field potentials (LFP) and event-related potentials (ERPs) can provide important information about the activity of neuronal populations. The phase of the ECoG signal reflects the temporal relationship between signals recorded by different electrodes. Phase synchronization, particularly the inter-brain connectivity patterns detected by ECoG, is crucial for studying synchronicity and functional connectivity between brain regions. Furthermore, phase-amplitude coupling (PAC) is an important feature of ECoG signals and is significant for the diagnosis of neurological diseases and for deciphering the connectivity of neural networks. The frequency of the ECoG signal reflects the dynamic changes in brain activity. ECoG signals can be analyzed across multiple frequency bands, ranging from low-frequency delta waves (1-4 Hz) to high-frequency gamma waves (above 30 Hz). Different frequency bands of ECoG signals are associated with different brain functional states.

[0058] Deep electrode signals (SEEG), such as local field potentials (LFP) and microelectrode recordings, are captured using electrodes implanted deep within the brain. These signals provide direct information about brain neural activity, particularly useful in studying neural networks and neurological disorders like epilepsy. Deep electrode signals typically have larger amplitudes because they are closer to the source of neuronal activity. The amplitude of these signals reflects the intensity of neuronal population activity, usually measured in microvolts (μV). For example, LFP signals have larger amplitudes and, compared to EEG, more clearly show high-frequency firing patterns. The phase of deep electrode signals reflects the temporal relationship between signals recorded by different electrodes. This phase information is crucial for studying synchronicity 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, ranging 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 limited: it increases in layers with high cell body density and at axonal terminals. In addition, high-frequency power primarily reflects spike activity and varies along with LFP components derived from postsynaptic potential and other spike-independent membrane voltage fluctuations.

[0059] Electrooculography (EOG) is an objective, quantitative method for assessing retinal function by detecting slow, persistent changes in the eye's electrostatic potential as it adapts to light. It reflects the function of the retinal pigment epithelium-photoreceptor complex. The amplitude of an EOG signal typically refers to its intensity or magnitude. In 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 (dark valley potential) and then gradually increases to a maximum (light peak potential) during light adaptation. These changes are recorded by electrodes placed on the skin at the inner and outer canthi and used to assess retinal function. The phase of the EOG signal may be related to potential changes during light adaptation, which are associated with the retina's response to light. The frequency of EOG signals is typically low because they relate to the retinal light adaptation process, a relatively slow physiological process. Changes in electrooculograms typically range from 0.01 to 0.1 Hz. These changes are known as spontaneous activity transients (SATs) and are particularly important in preterm infants because they are crucial for the formation of neuronal connections in the early, immature stages.

[0060] Electromyography (EMG) measures the electrical signals that trigger muscle contractions and reflects the degree of muscle contraction. The amplitude of an EMG signal reflects its intensity or magnitude. In EMG, changes in amplitude reflect the degree of muscle activity. Higher amplitude indicates more action potentials superimposed at the same time, and a greater number of activated muscle fibers. The phase of an EMG signal reflects the temporal relationship between signals recorded by different electrodes. When analyzing muscle activation timing, the type of muscle contraction is usually not the primary concern; only when the muscle contracts and relaxes is relevant. Phase information can be used to pinpoint the precise time of muscle activation. The frequency of an EMG signal reflects the dynamic changes in brain activity. The energy distribution of EMG signals is primarily within the 0–500 Hz frequency range, with the main components in the 50–150 Hz range. Frequency analysis can reveal the complexity of muscle activity, including the degree of muscle fatigue. For example, median frequency (MF) and mean power frequency (MPF), derived from the power spectral density analysis of surface EMG signals, are commonly used to assess the strength and fatigue level of muscle contractions. These frequency parameters decrease when muscles are under heavy load or fatigued, and increase when muscles are under light load or in a more relaxed state.

[0061] An electrocardiogram (ECG) is a graphical representation of changes in the heart's electrical activity recorded on the body surface, reflecting the heart's electrophysiological processes. The amplitude of an ECG signal is typically around 1 mV, reflecting the potential changes during cardiac depolarization and repolarization. Each segment of an ECG, such as the P wave, QRS complex, and T wave, has a specific amplitude range. For example, the P wave amplitude usually does not exceed 0.25 mV, the QRS complex amplitude varies considerably, and the T wave amplitude should not be less than 1 / 10 of the R wave amplitude in the same lead. The phase of an ECG signal usually refers to specific points in the waveform, such as peaks, troughs, and intervals. Each cardiac cycle in an ECG consists of a series of regular waveforms, including the P wave, QRS complex, and T wave. The start, end, peak, trough, and interval of these waveforms record detailed information about the heart's activity. For example, the P wave represents atrial depolarization, the QRS complex represents ventricular depolarization, and the T wave represents ventricular repolarization. The frequency of the ECG signal reflects the dynamic changes in cardiac activity. The frequency range of an electrocardiogram (ECG) signal is approximately 0.05 Hz to 100 Hz, with the signal energy mainly concentrated between 0.5 Hz and 45 Hz. Changes in heart rate directly affect the frequency components of the ECG signal; a normal heart rate range is approximately 60 to 100 beats per minute. Spectral analysis of ECG signals can help identify characteristics of heart disease, such as arrhythmias and myocardial ischemia.

[0062] Functional magnetic resonance imaging (fMRI) is a research method that stimulates specific sensory organs to induce neural activity (functional area activation) in corresponding areas of the cerebral cortex, which is then displayed through magnetic resonance images. fMRI signals are an important brain functional imaging technique that studies brain function by measuring hemodynamic changes induced by neuronal activity. By detecting changes in fMRI signals, neural activity in the brain can be indirectly reflected. The main characteristics of fMRI signals include amplitude, phase, and frequency. The amplitude of an fMRI signal is usually correlated with the intensity of the BOLD signal, reflecting changes in blood flow to a specific brain region during task performance or resting. The phase information of an fMRI signal contains temporal information related to brain functional activity. The frequency components of an fMRI signal reflect dynamic changes in brain activity. Specifically, the resting-state fMRI (rs-fMRI) signal can be decomposed into several intrinsic frequency clusters, which are associated with different physiological processes, such as respiration, pulse, metabolic processes, and vasomotor activity.

[0063] Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique that studies brain function by measuring changes in the concentrations of oxyhemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) in the blood caused by brain activity. Key characteristics of fNIRS signals include amplitude, phase, and frequency. The amplitude of the fNIRS signal typically refers to the degree of change in oxyhemoglobin and deoxyhemoglobin concentrations. The phase of the fNIRS signal may be considered when analyzing its time-domain characteristics, especially when studying temporal drift and baseline correction. The frequency of the fNIRS signal reflects the dynamic changes in brain activity. The resting-state fNIRS signal can be decomposed into several intrinsic frequency clusters, which are associated with different physiological processes such as respiration, pulse, metabolic processes, and vasomotor activity. Furthermore, frequency domain analysis of fNIRS signals can reveal low-frequency oscillations (LFOs) in brain activity, which are related to changes in brain hemodynamics.

[0064] The scope of protection of this application is not limited to the types of biological signals listed in this embodiment. All biological signals that can be directly extended and implemented based on the technical principles of this application are included in the scope of protection of this application.

[0065] The processing component 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 stimulation signal is a health-related stimulation signal, including sound wave stimulation signals, light stimulation signals, electrical stimulation signals, ultrasound stimulation signals, magnetic stimulation signals, and / or vibration stimulation signals. The scope of protection of this application is not limited to the types of stimulation signals listed in this embodiment; all stimulation signals that can be directly extended and implemented based on the technical principles of this application are included within the scope of protection of this application.

[0066] The output component is configured to output the stimulation signal, so that the coupling effect between the biological signal and the stimulation signal regulates the user's physiological state.

[0067] The coupling effect between the electroencephalogram (EEG) signal and the stimulation signal can regulate the user's brain state.

[0068] The coupling effect between the cortical EEG signal and the stimulation signal can enable the regulation of the user's brain state.

[0069] The coupling effect between the deep electrode signal and the stimulation signal can enable the regulation of the user's brain state.

[0070] The coupling effect between the electrooculogram signal and the stimulation signal can enable the regulation of the user's eye movement state.

[0071] The coupling effect between the electromyographic signal and the stimulation signal can enable the regulation of the user's muscle activity state.

[0072] The coupling effect between the electrocardiogram signal and the stimulation signal can achieve the regulation of the user's sympathetic and parasympathetic nervous systems.

[0073] The coupling effect between the magnetic resonance imaging signal and the stimulation signal can enable the regulation of the user's brain state.

[0074] The coupling effect between the near-infrared brain functional imaging signal and the stimulation signal can enable the regulation of the user's brain state.

[0075] By regulating one or more of the above physiological states, it can be applied to improve various physiological problems or diseases, including: through the coupling of EEG signals and stimulation signals, it can achieve brain state regulation such as sleep regulation, mood regulation, and / or regulation of mental illnesses, for example, the regulation of diseases such as depression, mania, autism, and schizophrenia; through the coupling of EMG signals and stimulation signals, it can achieve sleep regulation (abnormal body movement) and Parkinson's disease regulation; through the coupling of ECG signals and stimulation signals, it can achieve sleep regulation, mood regulation, and regulation of heart disease treatment; through the coupling of EEG signals and stimulation signals, it can achieve sleep regulation, dream regulation, and regulation of oculomotor spasm treatment.

[0076] In one embodiment of this application, the processing component selects at least one target frequency band based on the biological signal, and calculates corresponding physiological state regulation parameters based on the phase of each target frequency band; the physiological state regulation parameters include physiological state regulation frequency, physiological state regulation phase and / or physiological state regulation amplitude.

[0077] In one embodiment of this application, the processing component calculates the corresponding physiological state regulation frequency and / or physiological state regulation phase based on the phase of each target frequency band and the preset initial frequency of the stimulation signal.

[0078] In one embodiment of this application, the processing component calculates the corresponding physiological state modulation 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.

[0079] In one embodiment of this application, the processing component calculates the corresponding physiological state modulation 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 as the physiological state stage changes.

[0080] In one embodiment of this application, the processing component generates the frequency of the stimulation signal in real time based on the regulation frequency of each of the physiological states, so that the stimulation signal is frequency-coupled with the biological signal; the processing component generates the phase of the stimulation signal in real time based on the regulation phase of each of the physiological states, so that the stimulation signal is frequency-coupled with the biological signal; and / or the processing component generates the amplitude of the stimulation signal in real time based on the regulation amplitude of each of the physiological states, so that the stimulation signal is amplitude-coupled with the biological signal.

[0081] In one embodiment of this application, the processing component generates the amplitude of the stimulation signal in real time based on a plurality of physiological state regulation amplitudes; wherein, the processing component determines the generation coefficient ratio of the plurality of 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 physiological state regulation amplitudes; the sum of the generation coefficients of the plurality of physiological state regulation amplitudes is 1.

[0082] In one embodiment of this application, the processing component determines generation coefficients for a plurality of physiological state regulation amplitudes; wherein, the processing component calculates the difference between the amplitude of a plurality of target frequency bands and a target amplitude, and determines the generation coefficient ratio of the plurality of physiological state regulation amplitudes based on the ratio of different differences; the target amplitude corresponds to a target physiological state stage.

[0083] In one embodiment of this application, the processing component determines generation coefficients for a plurality of physiological state regulation amplitudes; wherein, the processing component obtains the proportions of a plurality of target frequency bands when the user is in different physiological state stages, and determines the proportions of generation coefficients for a plurality of physiological state regulation amplitudes based on the proportions of the plurality of target frequency bands.

[0084] In one embodiment of this application, the processing component regulates the user's physiological state through the coupling effect of the biosignal and the stimulation signal; wherein, the processing component determines the evoked phase based on the phase of the target frequency band and the evoked response potential delay of the stimulation signal; the output component outputs the stimulation signal, causing the biosignal to begin coupling with the stimulation signal at the evoked phase, so as to achieve physiological state regulation of the user at the physiological state stage corresponding to the target frequency band.

[0085] In one embodiment of this application, the processing component acquires a target frequency; the target frequency corresponds to the target frequency band; the processing component acquires a first filtered phase offset and a second filtered phase offset of the target frequency; the first filtered phase offset is the phase offset of the biological signal after processing by a first filter, and the second filtered phase offset is the phase offset of the frequency domain signal after processing 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 filtered phase offset and the second filtered phase offset.

[0086] In one embodiment of this application, the processing component acquires the spectral features corresponding to the target frequency band, and extracts the frequency value with the highest frequency energy based on the spectral features as the target frequency.

[0087] In one embodiment of this application, the processing component initially selects the target frequency band and determines whether the user's physiological state stage has changed based on the biosignal; if the user's physiological state stage changes, the processing component reselects the target frequency band so that the target frequency band corresponds to the user's current physiological state stage; or the processing component keeps the target frequency band unchanged if the user's physiological state stage changes.

[0088] In one embodiment of this application, the processing component determines whether the user's physiological state stage has changed based on the biosignal; wherein, the processing component obtains at least one physiological state stage result of the user based on the biosignal within a preset time window, classifies and counts the at least one physiological state stage result, obtains at least one stage count result, determines the relationship between the at least one stage count result within the preset time window and any preset threshold, and if any stage count result is higher than the preset threshold, it is determined that the user's physiological state stage has changed.

[0089] In one embodiment of this application, the processing component obtains at least one physiological state staging result of the user based on the biosignals within a preset time window; wherein, the processing component segments the biosignals within the preset time window according to time, and obtains at least one physiological state staging result within the preset time window based on biosignals within multiple time periods.

[0090] This application also provides a method for regulating physiological state, which can be implemented by the biological health system. However, the implementation device of the method includes, but is not limited to, the structure of the biological health system described in this embodiment. The method for regulating physiological state includes: acquiring the user's biological signals in real time; calculating physiological state regulation parameters based on the phase of the biological signals; and generating a stimulation signal based on the physiological state regulation parameters, so as to regulate the user's physiological state through the coupling effect of the biological signals and the stimulation signal.

[0091] In some other embodiments, the physiological state regulation method described in this application can be applied to end-to-cloud interaction scenarios. Figure 1B shows a schematic diagram of the structure of an end-to-cloud interaction scenario in these implementations. As shown in Figure 1B, the end-to-cloud interaction system 2 includes a wearable control device 20 and a cloud server 21. The wearable control device 20 and the cloud server 21 can communicate with each other, and the communication method is not limited to wired or wireless methods.

[0092] When a user is preparing to enter a certain physiological state, they can put on the wearable control device 20. At this time, the wearable control device 20 will collect the user's biological signals in real time and send them to the cloud server 21. The cloud server 21 will process the biological signals and control the wearable control device 20 to output stimulation signals to the user, so that the user's biological signals and stimulation signals are coupled, thereby regulating the user's physiological state. In addition, the cloud server 21 will also store the user's biological signal data and physiological state regulation data within the user's physiological state cycle.

[0093] In some other embodiments, the physiological state regulation method described in this application can be applied to another end-to-cloud interaction scenario. Figure 1C shows a schematic diagram of the structure of the end-to-cloud interaction scenario in these implementations. As shown in Figure 1C, the end-to-cloud interaction system 2 includes a wearable control device 20, a cloud server 21, and a host computer 22. The wearable control device 20, the cloud server 21, and the host computer 22 can communicate with each other, and the communication method is not limited to wired or wireless methods.

[0094] At this time, when the user puts on the wearable control device 20, the host computer 22 can control the wearable control device 20 to collect the user's biological signals in real time and execute the physiological state regulation method provided in this application embodiment to generate stimulation signals. At the same time, the user's biological signal data and physiological state regulation data during the physiological state cycle can be uploaded to the host computer 22 through communication methods such as Bluetooth, and then transmitted to the cloud server 21 for storage.

[0095] It should be noted that the cloud server 21 may include one or more servers, or one or more processing nodes, or one or more virtual machines running on the server. The cloud server 21 may also be referred to as a server cluster, management platform, physiological state control center, etc. This application does not impose any restrictions on this.

[0096] The following embodiments of this application provide a method for regulating physiological states, which can be applied to the biological health system shown in Figure 1A, or to the end-to-cloud interaction scenario shown in Figures 1B and 1C. However, it should be noted that Figures 1A to 1C are provided merely as embodiments. In fact, this application does not impose any restrictions on the entity executing some or all of the steps in the physiological state regulation method described in the following embodiments.

[0097] The technical solutions in this application will be described in detail below, taking electroencephalogram (EEG) signals in biological signals as an example and in conjunction with the accompanying drawings.

[0098] Figure 2 shows a flowchart of the physiological state regulation method according to an embodiment of this application. As shown in Figure 2, the physiological state regulation method includes steps S1 to S3.

[0099] S1. Acquire the user's brainwave signals in real time.

[0100] Specifically, the system collects the user's electroencephalogram (EEG) signals in real time. The collection areas include electrodes Fp1, Fp2, F7, F8, and Fpz in the frontal lobe, electrodes Tp9 and Tp10 in the temporal lobe, electrodes C3, C4, and Cz in the central lobe, and electrodes O1, O2, and Oz in the occipital lobe.

[0101] In some embodiments, frontal electrodes Fp1, Fp2, F7, and F8 are selected to collect the user's electroencephalogram (EEG) signals. The EEG signals include alpha waves, beta waves, theta waves, and delta waves. Different EEG signals represent different states of brain activity; for example, beta waves correspond to a state of heightened activity, alpha waves to a state of wakefulness, theta waves to a state of light sleep, and delta waves to a state of deep sleep.

[0102] Furthermore, in some embodiments, after acquiring the user's EEG signals, preprocessing can be performed to improve the signal-to-noise ratio and thus improve signal quality. For example, the acquired EEG signals can undergo rereference, filtering, and detrending processing. Filtering can utilize a 0.1-45Hz bandpass filter (such as a second-order Butterworth filter) to filter the acquired EEG signals, and a notch filter can be used to filter out interference from 50Hz power frequency and its harmonics. This application is not limited to these specific preprocessing methods.

[0103] S2. Calculate sleep regulation parameters (one of the physiological state regulation parameters) based on the phase of the EEG signal.

[0104] To enable the stimulation signal to couple with the EEG signal and improve the synchronization and stability between the stimulation signal and the EEG signal, this application first calculates sleep regulation parameters in real time based on the phase of the user's EEG signal, and then generates a stimulation signal based on this, thereby achieving the modulation of the stimulation signal by the phase of the EEG signal. Figure 3 shows a schematic flowchart of the sleep regulation method according to an embodiment of this application. As shown in Figure 3, the calculation of sleep regulation parameters based on the phase of the EEG signal includes steps S21 and S22.

[0105] S21. Select at least one target frequency band based on the electroencephalogram (EEG) signal.

[0106] In some embodiments, the target frequency band can be determined based on the dominant EEG signal frequency of the next sleep stage in the user's sleep cycle. For example, when a user is awake (wake stage) and wishes to transition to stage N1 through sleep modulation, the dominant EEG signal in this stage is alpha waves, so enhancing the alpha frequency band can be chosen as the target frequency band. Alternatively, the beta frequency band can be considered as the target frequency band to suppress beta wave activity in the brain through sleep modulation. Alternatively, the theta frequency band can be considered as the target frequency band to enhance theta wave activity in the brain through sleep modulation. Or, both the beta and theta frequency bands can be selected as target frequency bands simultaneously. That is, whether one or multiple target frequency bands are selected, the purpose is to calculate the corresponding sleep modulation 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 quickly as possible, thereby improving sleep onset efficiency and sleep quality. Therefore, this application does not impose any restrictions on the specific selection of the target frequency band while satisfying this objective.

[0107] It should be noted that sleep stages within a sleep cycle can be divided into Wake, N1, N2, N3, and REM stages. Alternatively, N3 and REM stages can be combined into one stage, indicating that the user is about to complete the first sleep cycle and enter the second, at which point sleep regulation can be discontinued. Therefore, sleep stages within a user's sleep cycle can be divided according to regulatory needs, and this application does not impose any restrictions on this.

[0108] In some embodiments, when selecting a target frequency band, the corresponding target frequency band can be preset according to the sleep stage within the sleep cycle, and the corresponding target frequency band can be automatically selected according to the changes in the user's sleep stage. As shown in Figure 4, selecting at least one target frequency band based on the EEG signal includes steps S211 to S213.

[0109] S211. Initially select the target frequency band.

[0110] S212. Determine whether the user's sleep stage has changed based on the electroencephalogram (EEG) signal.

[0111] S213. If the sleep stage changes, the target frequency band is reselected so that the target frequency band corresponds to the sleep stage.

[0112] For example, a user's sleep cycle can be divided into four stages: Stage 1, Stage 2, Stage 3, and Stage 4. Stage 1 corresponds to the Wake stage, Stage 2 to N1, Stage 3 to N2, and Stage 4 to N3 or REM sleep. The target frequency band for Stage 1 can be initially selected as the α band. The target frequency band for Stage 2 is the θ band, and for Stage 3, the δ band. When the user's sleep stage changes, the target frequency band corresponding to the changed sleep stage is selected. Furthermore, when the user enters Stage 4, no further sleep regulation is required. Alternatively, the target frequency bands for Stage 1 can be set as the α and θ bands, and initially selected. The target frequency bands for Stage 2 can be set as the θ and δ bands, and for Stage 3, the δ band. When the user's sleep stage changes, the target frequency band corresponding to the current sleep stage is selected, and no further sleep regulation is required when the user enters Stage 4.

[0113] To address this, this application embodiment also provides an implementation method for determining whether a user's sleep stage has changed based on the electroencephalogram (EEG) signal, thereby supporting the automatic selection of the corresponding target frequency band based on changes in the user's sleep stage provided in the above embodiments. As shown in Figure 5, determining whether a user's sleep stage has changed based on the EEG signal includes steps S2121 to S2123.

[0114] S2121. Obtain at least one sleep stage result of the user based on the EEG signal within a preset time window.

[0115] Specifically, the EEG signals within the preset time window are segmented according to time, and at least one sleep stage result within the preset time window is obtained based on the EEG signals within multiple segmented time periods.

[0116] In some embodiments, a 10-minute time window is preset and processed in 30-second segments. Then, multiple 30-second EEG signals are analyzed to determine the corresponding sleep stage results. For example, if the user is awake for 10 minutes, EEG signals are continuously acquired for 10 minutes, and 20 30-second sleep stage results are determined, including Wake, REM, N1, N2, or N3 stages.

[0117] Furthermore, machine learning algorithms can be used to infer sleep stages from the EEG signals. The machine learning algorithms used may include neural networks, attention mechanisms, decision trees, support vector machines, etc., and this application is not limited to these.

[0118] 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.

[0119] S2122. Classify and count at least one of the sleep stage results to obtain at least one stage count result.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The following will use a target frequency band as an example to illustrate the specific process of calculating the corresponding sleep regulation parameters based on phase.

[0126] 1) Calculate the sleep regulation frequency based on the phase of the target frequency band.

[0127] 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.

[0128] In some embodiments, assuming the phase of the target frequency band is φ(t) and the initial frequency of the stimulation signal is f0, the sleep regulation frequency can be expressed as:

[0129] As can be seen from equation (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 equation (1).

[0130] 2) Calculate the sleep regulation phase based on the phase of the target frequency band.

[0131] Specifically, the initial frequency of the stimulation signal is preset, and the corresponding sleep regulation phase is calculated based on the phase of the target frequency band and the initial frequency.

[0132] In some embodiments, assuming the phase of the target frequency band is φ(t) and the initial frequency of the stimulation signal is f0, the sleep regulation phase can be expressed as:

[0133] X s (t)=cos(2πf0t+φ(t)) (2)

[0134] As can be seen from equation (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 equation (2).

[0135] 3) Calculate the sleep regulation phase based on the phase of the target frequency band.

[0136] 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.

[0137] In some embodiments, let the phase of the target frequency band be φ(t), the initial frequency of the stimulation signal be f0, and the initial amplitude be A0, then the sleep regulation amplitude can be expressed as:

[0138] F s (t)=(A0+Bsin(φ(t)))sin(2πf0t) (3)

[0139] As can be seen from equation (3), the sleep regulation amplitude will actually change in real time following the phase of the target frequency band. Here, B represents the parameter that affects the regulation amplitude by phase modulation. When multiple target frequency bands are determined through step S21, the corresponding sleep regulation amplitude is calculated for each target frequency band using equation (3).

[0140] Furthermore, in some embodiments, the initial amplitude A0 corresponds to the amplitude of the target frequency band. That is, after a target frequency band is determined according to step S21, the amplitude corresponding to the target frequency band can be obtained based on the EEG signal, and this amplitude can be used as the initial amplitude A0. It should be noted that when multiple target frequency bands are determined, there will actually be multiple initial amplitudes A0.

[0141] In other embodiments, the initial amplitude A0 is adaptively adjusted by a preset adjustment value as the sleep stage changes. That is, if the sleep stage changes, the initial amplitude A0 is adaptively adjusted by the preset adjustment value.

[0142] For example, the initial amplitude can be set directly based on the amplitude of the target frequency band. Then, the initial amplitude will adaptively adjust according to the user's sleep stage based on the preset value. For example, in the first sleep stage, the amplitude of the α band can be directly set as the initial amplitude. Subsequently, each time the user enters the next sleep stage, the initial amplitude will adaptively decrease by a percentage until entering the fourth sleep stage, reaching its minimum or stopping. Alternatively, when the user's sleep stage returns to the third or second sleep stage, the initial amplitude will increase accordingly to help the user deepen their sleep. If the user wakes up during sleep and regains consciousness, the initial amplitude will increase accordingly to help the user fall asleep again. In other words, the initial amplitude adaptively adjusts up or down according to the user's sleep stage. The changes in sleep stage can be determined according to steps S2121 to S2123 described above.

[0143] Furthermore, regarding the phase φ(t) of the target frequency band in equations (1) to (3), this application embodiment provides an implementation method for obtaining the phase of the target frequency band. As shown in FIG6, obtaining the phase of the target frequency band includes steps S221 to S223.

[0144] S221. Obtain the target frequency; the target frequency corresponds to the target frequency band.

[0145] Specifically, the method for obtaining the target frequency includes: obtaining the spectral characteristics corresponding to the target frequency band, and extracting the frequency value with the highest frequency energy based on the spectral characteristics as the target frequency.

[0146] In some embodiments, after filtering the EEG signal obtained in step S1, N-point sliding window processing is performed, and the spectral characteristics of the EEG signal in the target frequency band are 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 personal center frequency (ICF), that is, the personal center frequency is used as the target frequency. In other words, for each target frequency band, a corresponding target frequency can actually be obtained.

[0147] S222. Obtain the first filter phase offset and the second filter phase offset of the target frequency.

[0148] Specifically, the first filtered phase offset θ1 of the target frequency band is obtained. The first filtered phase offset θ1 is the phase offset generated after the EEG signal is filtered by the first filter. In some embodiments, after the user's EEG signal is obtained through step S1, a 0.1-45Hz bandpass filter (such as a second-order Butterworth filter) can be used as the first filter to filter the acquired EEG signal. At this time, the first filtered phase offset θ1 of the target frequency can be calculated based on the parameters of the 0.1-45Hz bandpass filter.

[0149] Specifically, the second filtered phase offset θ2 of the target frequency band is obtained. The second filtered phase offset is the phase offset of the frequency domain signal after processing by the second filter, and the frequency domain signal is obtained based on the EEG signal. The second filter is a filter corresponding to the target frequency band.

[0150] In some embodiments, the method for obtaining the second filtered phase offset θ2 includes: after filtering the EEG signal obtained in step S1, performing N-point sliding window processing, and using Fast Fourier Transform to calculate the spectral characteristics of the EEG signal in the target frequency band, i.e., X[f]. Then, performing a Hilbert transform on X[f], setting the negative frequency components to zero, and multiplying the positive frequency components by 2 to obtain Y[f]. Applying the second filter of the target frequency band to Y[f], obtaining the filtered frequency domain signal, and obtaining the amplitude and phase response of the pulse frequency according to the parameters of the filter, the second filtered phase offset θ2 of the target frequency can then be obtained.

[0151] S223. Obtain the phase of the target frequency band based on the first filter phase offset and the second filter phase offset.

[0152] Specifically, the filtered frequency domain signal is inversely transformed back to the time domain to obtain a complex-valued signal Z[n]. The real-time phase angle is obtained by analyzing the complex-valued signal Z[n]. Based on this, the phase information of the target frequency band can be extracted by subtracting θ1 and θ2.

[0153] To further illustrate the specific process of obtaining the phase of the target frequency band, the following explanation will use the α band as the target frequency band. Once the α band is determined as the target frequency band, a 0.1-45Hz bandpass filter is used to filter the acquired EEG signal. An N-point sliding window is then applied to the filtered EEG signal, and a Fast Fourier Transform (FFT) is used to calculate the spectral characteristics of the α band EEG signal, i.e., X[f]. The frequency value with the highest frequency energy in the α band is extracted as the individual center frequency (ICF). At this point, the first filtered phase offset θ1 of the ICF can be obtained based on the parameters of the 0.1-45Hz bandpass filter. Then, a Hilbert transform is performed on X[f], setting the negative frequency components to zero and multiplying the positive frequency components by 2 to obtain Y[f]. Y[f] is then filtered using the α band (7.5-12.5Hz) filter to obtain the filtered frequency domain signal, which is used to obtain the second filtered phase offset θ2 of the ICF. Finally, the filtered frequency domain signal is inversely transformed back to the time domain to obtain the complex signal Z[n]. The real-time phase angle is obtained by analyzing the complex signal Z[n], and the phase information of the α frequency band can be extracted by subtracting θ1 and θ2 from it.

[0154] Furthermore, the initial frequency f0 in equations (1) to (3) can be selected within the target frequency band. For example, when the target frequency band is the α band, its frequency range is 7.5–12.5 Hz. Any frequency value within this frequency range can be preset as the initial frequency f0 to calculate the corresponding sleep regulation frequency. For another example, when the target frequency band is both the α and θ bands, any frequency value within the α band's frequency range (7.5–12.5 Hz) can be used as the initial frequency to calculate the sleep regulation frequency corresponding to the α band, and any frequency value within the θ band's frequency range (4.5–7.5 Hz) can be used as the initial frequency to calculate the sleep regulation frequency corresponding to the θ band. Alternatively, the target frequency (personal central frequency, ICF) of the target frequency band can be set as the initial frequency to calculate the sleep regulation frequency corresponding to that target frequency band. Furthermore, the initial frequency can also be a pre-set frequency within the low-frequency range of 70 Hz.

[0155] S3. Generate a stimulation signal based on the sleep regulation parameters, so as to regulate the user's sleep through the coupling effect of the stimulation signal and the EEG signal.

[0156] Specifically, once the sleep regulation parameters are calculated, a stimulation signal can be generated based on these parameters. As described above, in step S2, when calculating the sleep regulation parameters, one or more of the following can be selected: sleep regulation frequency, sleep regulation phase, and sleep regulation amplitude. That is, sleep regulation frequency, sleep regulation phase, and sleep regulation amplitude are in an AND / OR relationship. Therefore, the frequency, phase, and / or amplitude of the stimulation signal can be generated in real time based on one or more of the following: sleep regulation frequency, sleep regulation phase, and sleep regulation amplitude. For example, the frequency of the stimulation signal can be generated in real time based on each of the aforementioned sleep regulation frequencies, or the phase of the stimulation signal can be generated in real time based on each of the aforementioned sleep regulation phases, or the amplitude of the stimulation signal can be generated in real time based on each of the aforementioned sleep regulation amplitudes, or the frequency, phase, and amplitude of the stimulation signal can be generated in real time based on each sleep regulation frequency, each sleep regulation phase, and each sleep regulation amplitude.

[0157] The stimulation signals include acoustic stimulation signals, light stimulation signals, electrical stimulation signals, and / or vibration stimulation signals.

[0158] To further illustrate the generation of stimulation signals based on the aforementioned sleep regulation parameters, the specific process of generating stimulation signals in real time based on sleep regulation frequency, sleep regulation phase, and / or sleep regulation amplitude will be described below.

[0159] (A) The frequency of the stimulation signal is generated in real time based on each of the aforementioned sleep regulation frequencies.

[0160] Specifically, when the sleep regulation frequency f corresponding to each target frequency band is obtained based on equation (1) above... s After (t), 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 equation (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 equation (1), and the frequency of the stimulation signal should be generated in real time based on multiple sleep regulation frequencies. Since each sleep regulation frequency is calculated in real time based on the phase of the target frequency band, the stimulation signal generated in real time based on the sleep regulation frequency is actually modulated by the phase of the EEG signal, which means that the stimulation signal and the EEG signal are frequency-coupled. That is, when the frequency of the stimulation signal is generated in real time based on each sleep regulation frequency, the frequency of the stimulation signal is coupled with the phase of the EEG signal.

[0161] In some embodiments, when generating the frequency of the stimulation signal in real time based on multiple sleep regulation frequencies, it can be generated using a linear superposition method. That is, multiple sleep regulation frequencies are synthesized into a composite signal through linear superposition. The stimulation signal generated in real time using this method contains components of all sleep regulation frequencies.

[0162] (B) The phase of the stimulation signal is generated in real time based on each of the aforementioned sleep regulation phases.

[0163] Specifically, when the sleep regulation phase X corresponding to each target frequency band is obtained based on equation (2) above... s After (t), 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 equation (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 equation (2), and the phase of the stimulation signal should be generated in real time based on multiple sleep regulation phases. Since each sleep regulation phase is calculated in real time based on the phase of the target frequency band, the stimulation signal generated in real time based on the sleep regulation phase is actually modulated by the phase of the EEG signal, which means that the stimulation signal and the EEG signal are coupled. That is, when the phase of the stimulation signal is generated in real time based on each sleep regulation phase, the phase of the stimulation signal is coupled with the phase of the EEG signal.

[0164] 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 frequency components of each target frequency band in the brain. The phase of the stimulation signal is then synthesized after weighted superposition.

[0165] (C) The amplitude of the stimulation signal is generated in real time based on the amplitude of each sleep regulation.

[0166] Specifically, when the sleep regulation amplitude F corresponding to each target frequency band is obtained based on equation (3) above, sAfter (t), the amplitude of the stimulation signal can be generated in real time based on the sleep regulation amplitude. At this time, when only one target frequency band is selected, the sleep regulation amplitude calculated based on equation (3) is actually the amplitude of the stimulation signal. When multiple target frequency bands are determined, multiple sleep regulation amplitudes will be calculated based on equation (3), and the amplitude of the stimulation signal should be generated in real time based on multiple sleep regulation amplitudes. Since each sleep regulation amplitude is calculated in real time based on the phase of the target frequency band, the stimulation signal generated in real time based on the sleep regulation amplitude is actually modulated by the phase of the EEG signal, which means that the amplitude of the stimulation signal and the EEG signal are coupled. That is, when the phase of the stimulation signal is generated in real time based on each sleep regulation amplitude, the amplitude of the stimulation signal is coupled with the phase of the EEG signal.

[0167] In some embodiments, when generating the amplitude of the stimulation signal in real time based on multiple sleep regulation amplitudes, it is necessary to determine the generation coefficient ratio of the multiple sleep regulation amplitudes, and generate the amplitude of the stimulation signal in real time based on the generation coefficient ratio of the multiple sleep regulation amplitudes. The sum of the generation coefficients of the multiple sleep regulation amplitudes is 1. In this case, after determining the generation coefficient ratio of the multiple sleep regulation amplitudes, each sleep regulation amplitude can be multiplied by its respective generation coefficient ratio, and the results of these multiplications can be added together to obtain the amplitude of the stimulation signal.

[0168] It should be noted that the amplitude of the stimulus signal should not exceed 70dB.

[0169] In some embodiments, determining the generation coefficients of the plurality of sleep regulation amplitudes includes: calculating the difference between the amplitude of the plurality of target frequency bands and the target amplitude, and determining the generation coefficient ratio of the plurality of sleep regulation amplitudes based on the ratio of the different differences. The target amplitude corresponds to the target sleep stage.

[0170] For example, taking the selection of the alpha and theta bands as target frequency bands, the amplitudes of the alpha and theta bands are obtained based on real-time acquired EEG signals. If the target sleep stage is N1, the target amplitude corresponding to stage N1 can be obtained based on the user's historical sleep data or a database. Then, the difference 1 between the amplitude of the alpha band and the target amplitude is calculated, and the difference 2 between the amplitude of the theta band and the target amplitude is calculated. Finally, the generation coefficient ratio of sleep regulation amplitude 1 and sleep regulation amplitude 2 is determined based on the ratio of difference 1 and difference 2. Here, sleep regulation amplitude 1 corresponds to the alpha band, and sleep regulation amplitude 2 corresponds to the theta band.

[0171] It can be seen that the ratio of the generation coefficients of multiple sleep regulation amplitudes actually changes in real time with the ratio of the strength of the difference between the amplitudes of multiple target frequency bands and the target amplitude. For example, the ratio of difference 1 to difference 2 may initially be 4:1, then the ratio of the generation coefficients of sleep regulation amplitude 1 to sleep regulation amplitude 2 is 4:1. As sleep regulation progresses and the user gets closer to the target sleep stage N1, the frequency component of the theta band in the EEG signal should increase. At this time, the ratio of difference 1 to difference 2 may become 2:2, and the ratio of the generation coefficients of sleep regulation amplitude 1 to sleep regulation amplitude 2 will also be 2:2.

[0172] In another embodiment, determining the generation coefficients of the plurality of sleep regulation amplitudes includes: obtaining the proportions of the plurality of target frequency bands when the user is in different sleep stages, and determining the proportions of the generation coefficients of the plurality of sleep regulation amplitudes based on the proportions of the plurality of target frequency bands.

[0173] For example, large-scale sleep data from publicly available databases can be used to determine the proportions of multiple target frequency bands in EEG signals at various sleep stages, and the generation coefficients of multiple sleep regulation amplitudes can be determined based on these proportions. Alternatively, sleep data from different populations can be experimentally measured to determine the proportions of multiple target frequency bands in EEG signals at various sleep stages, and the generation coefficients of multiple sleep regulation amplitudes can be determined based on these proportions. Or, historical sleep data of users can be used to determine the proportions of multiple target frequency bands in EEG signals at various sleep stages, and the generation coefficients of multiple sleep regulation amplitudes can be determined based on these proportions.

[0174] Furthermore, once the proportions of multiple target frequency bands in the EEG signals at each sleep stage are determined, stimulation signals with different generation coefficient ratios can be generated through extensive experimental testing, and the generation coefficient ratio with the best effect can be selected as the final generation coefficient ratio for each sleep regulation amplitude.

[0175] Therefore, as described above, the stimulation signal generated based on the sleep regulation parameters will be frequency-coupled, phase-coupled, and / or amplitude-coupled with the EEG signal. At this time, sleep regulation can be achieved through the coupling effect between the stimulation signal and the EEG signal. In fact, when the stimulation signal and the EEG signal are coupled, an EEG coupling signal will be generated in the user's brain. The EEG coupling signal essentially represents a new signal formed by the coupling of the user's EEG signal with the stimulation signal under its influence. That is, under the influence of the coupled stimulation signal, the user's EEG signal will change, generating an EEG coupling signal. This EEG coupling signal will induce an increase in the target frequency band of EEG signal in the user's brain, inducing the brain to enter the corresponding sleep state, or it may reduce the generation of the target frequency band of EEG signal in the user's brain.

[0176] Furthermore, the sleep regulation of the user through the coupling effect of the stimulation signal and the EEG signal includes: determining the evoked phase based on the phase of the target frequency band of the EEG signal and the evoked response potential delay of the stimulation signal, and the stimulation signal and the EEG signal begin to couple at the evoked phase.

[0177] In practice, when a stimulus signal is applied to the user's brain, it will produce a delay in the evoked response potential. This indicates that there is a certain delay in the brain's electrophysiological response to the stimulus signal. For example, for a sound wave stimulus signal, the delay in the evoked response potential is 62.5 ms. Assuming the target frequency band is determined to be the delta band, after obtaining the sleep regulation parameters, a corresponding sound wave stimulus signal is generated and applied to the user's brain. Assuming the target frequency of the delta band is 2.5 Hz and the period is 400 ms, the phase shift when the sound wave stimulus signal occurs should be (62.5 / 400)*360 = 15.625°. Therefore, in order to couple the EEG signal with the stimulus signal, the evoked phase can be determined to be 360° - 15.625° = 344.375°, that is, the sound wave stimulus signal is applied to the user's brain at 344.375°. At this time, the evoked response potential of the sound wave stimulation signal will couple with the peak of the EEG signal, so that the EEG signal generates an EEG coupling signal at the peak due to the effect of the sound wave stimulation signal. That is, the coupling effect of the stimulation signal and the EEG signal effectively induces an increase in the delta frequency band EEG signal in the user's brain.

[0178] In some embodiments, to reduce the generation of EEG signals in the target frequency band through the coupling effect of the stimulation signal and the EEG signal, the evoked response potential of the stimulation signal should be coupled with the trough of the EEG signal, so that the EEG signal generates an EEG coupling signal at the trough, thereby reducing the generation of EEG signals in the user's brain in the target frequency band. Again, taking the aforementioned acoustic stimulation signal as an example, assuming the target frequency of the delta band is 2.5 Hz and the period is 400 ms, the phase shift when the stimulation signal is applied should be (62.5 / 400)*360 = 15.625°. At this point, the evoked phase is determined to be 180° - 15.625° = 164.375°, meaning the acoustic stimulation signal is applied to the user's brain at 164.375°. At this time, the evoked response potential of the sound wave stimulation signal will couple with the trough of the EEG signal. The EEG signal generates an EEG coupling signal at the trough due to the effect of the sound wave stimulation signal. At this time, the generation of EEG signal in the delta band in the user's brain can be reduced through the coupling effect of the stimulation signal and the EEG signal.

[0179] It should be noted that the delay of the stimulus-evoked response potential varies for different types of stimuli. The embodiments described above in this application use a 62.5 ms delay in the evoked response potential of sound wave stimulation as an example to illustrate the specific process of coupling.

[0180] Figure 7 shows an application diagram of a sleep regulation method provided in an embodiment of this application. As shown in Figure 7, when the sleep regulation method is started, electroencephalogram (EEG) signals are first collected and a target frequency band is determined. Then, a target frequency is determined, and the phase information of the target frequency band is extracted based on the target frequency. Sleep regulation parameters are calculated in real time based on these parameters. Stimulation signals are then generated in real time based on these parameters and applied to the brain. Sleep regulation is achieved through the coupling effect of the stimulation signals and EEG signals.

[0181] This application calculates personalized sleep regulation parameters for users based on the phase of real-time EEG signals to generate stimulation signals in real time, so that the stimulation signals are frequency-coupled, phase-coupled and / or amplitude-coupled with the EEG signals, thereby better regulating brain nerve oscillations, improving the synchronicity and stability between nerve stimulation and internal brain activity, and improving the user's nighttime sleep onset efficiency and sleep quality.

[0182] The scope of protection of the sleep regulation method described in this application is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.

[0183] This application also provides a sleep regulation system, which can implement the sleep regulation method in the physiological state regulation method described in this application. However, the implementation device of the sleep regulation system described in this application includes, but is not limited to, the structure of the sleep regulation system listed in this embodiment. All structural modifications and substitutions of the prior art made based on the principles of this application are included within the protection scope of this application.

[0184] Figure 8 shows a schematic diagram of the sleep regulation system according to an embodiment of this application. As shown in Figure 8, the sleep regulation system 4 includes an acquisition module 41, a generation module 42, and a regulation module 43.

[0185] Acquisition module 41 is used to acquire the user's EEG signals in real time;

[0186] Generation module 42 is used to calculate sleep regulation parameters based on the frequency, phase and / or amplitude of the EEG signal;

[0187] The regulation module 43 is used to generate a stimulation signal based on the sleep regulation parameters, so as to regulate the user's sleep through the coupling effect of the stimulation signal and the electroencephalogram (EEG) signal.

[0188] It should be noted that the structure and principle of the acquisition module 41, the generation module 42 and the regulation module 43 correspond one-to-one with the steps in the above sleep regulation method, so they will not be described in detail here.

[0189] It should be noted that the sleep regulation system provided in this application embodiment is merely a virtualized system structure. This application does not impose any restrictions on the specific implementation of each module.

[0190] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] The wearable device can be implemented using the architecture of the exemplary computing device shown in FIG9. As shown in FIG9, 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 by the computer for 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, the architecture shown in FIG9 is only exemplary, and one or more components in the computing device shown in FIG9 may be omitted as needed when implementing different devices.

[0195] This application also provides a computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the physiological state regulation method as described in this application. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be executed by computer-readable instructions stored on the computer-readable storage medium. The computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0196] This application also provides a computer program product or computer program that includes computer-readable instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer-readable instructions from the computer-readable storage medium and execute the instructions to cause the computer device to perform the physiological state regulation methods described in the above embodiments.

[0197] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0198] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A biological health system, characterized in that, include: The detection component is configured to collect the user's biosignals in real time; The processing component 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. An output component is configured to output the stimulation signal, such that the coupling effect between the biological signal and the stimulation signal regulates the user's physiological state.

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

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

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

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

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

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

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

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

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

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

12. The biological health system according to claim 11, characterized in that, Also includes: The processing component acquires the spectral features corresponding to the target frequency band, and extracts the frequency value with the highest frequency energy based on the spectral features as the target frequency.

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

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

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

16. The biological health system according to claim 1, characterized in that, Also includes: The biosignals include electroencephalogram (EEG) signals, cortical EEG signals, deep electrode signals, electrooculogram (EOG) signals, electromyogram (EMG) signals, electrocardiogram (ECG) signals, magnetic resonance imaging (MRI) signals, and / or near-infrared brain functional imaging (NIBMI) signals.

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

18. A method for regulating physiological states, characterized in that, include: Real-time acquisition of users' biosignals; Physiological state regulation parameters are calculated based on the phase of the biological signals. Stimulation signals are generated based on the physiological state regulation parameters to regulate the user's physiological state through the coupling effect of the biological signals and the stimulation signals.

19. A wearable device, characterized in that, include: One or more sensors are configured to acquire the user's biosignals in real time; One or more processors; and One or more memories, wherein computer-readable code is stored in the memories, which, when executed by the one or more processors, implements the functions of the biological health system as described in any one of claims 1 to 17.

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

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