Non-invasive human sleep neuromodulation method and system

Abstract

The application discloses a non-invasive human sleep nerve regulation method and system. The method comprises the following steps: acquiring a first physiological signal of a user, performing real-time sleep staging on the user to determine a deep sleep stage of the user; based on the result of the sleep staging, detecting slow wave power through a second physiological signal when the user is in the deep sleep state; calculating a phase angle of the slow wave according to the slow wave state, so as to perform tACS electric stimulation when the phase angle meets a preset condition; after a period of tACS electric stimulation, acquiring a third physiological signal of the user, and acquiring a physiological active state of the user by analyzing the third physiological signal; and when the physiological active state is an uplink state, performing TMR sound stimulation. Thus, through mutual cooperation of the tACS electric stimulation and the TMR sound stimulation, non-invasive nerve regulation is realized on the user, and the nerve regulation effect on sleep is improved.

Description

Technical Field

[0001] This invention relates to a non-invasive method for human sleep neuromodulation, and also to a corresponding human sleep neuromodulation system, belonging to the field of neuromodulation technology. Background Technology

[0002] Sleep plays a vital role in human health and cognitive function. Numerous studies have shown that sufficient and high-quality sleep is indispensable for maintaining cognitive function, mood regulation, and the body's repair processes. Sleep plays a unique role, particularly in memory consolidation. Memory consolidation refers to the process by which the brain converts short-term memories into long-term memories after learning and experiencing certain events. Sleep is considered a key stage in this process. Many studies have demonstrated the existence of memory consolidation during sleep, and in response to these findings, several intervention strategies have emerged in recent years aimed at influencing the memory consolidation process during sleep through external stimuli. These stimuli can be sounds, smells, or even electrical stimulation, with the goal of strengthening the consolidation of certain memories during specific sleep stages.

[0003] Currently, many studies utilize the Target Memory Reactivation (TMR) paradigm, which activates specific memories during sleep to improve individual outcomes for those memories. TMR experiments connect daytime learning information with specific sound or olfactory stimuli, playing these matching stimuli during nighttime sleep. Participants then complete cognitive tasks such as word recall and image recognition. The impact of TMR on memory consolidation is assessed by comparing the performance of participants who received TMR stimulation with those who did not. The improvement in memory outcomes through TMR is now widely accepted. However, TMR research also has limitations. Most studies randomly administer sound stimuli during NREM or REM sleep, while some use slow-wave detection to deliver sound stimuli during the ascending phase of slow waves. However, the unpredictable nature of slow wave occurrences often leads to missed stimuli, and research indicates that the first slow wave often contains the most memory information; therefore, accurately capturing the first slow wave is crucial.

[0004] tACS is another common neuromodulation technique. It is a non-invasive brain stimulation technique that uses low-intensity alternating current (usually 1-2 mA) to be transmitted to the cerebral cortex through electrodes. The current intensity of tACS is very small and does not directly activate neurons. Instead, it modulates the amplitude of neural oscillations by affecting the synchronicity of neurons. However, the main disadvantages of tACS are as follows: (1) As an electrical stimulation, the current of tACS flows from the cathode to the anode. In traditional tACS, the anode is set in the top or frontal region of the head, and the cathode is set in the mastoid process. This causes the current to spread throughout the brain, resulting in a very low field strength in the cortex; (2) Although tACS can enhance slow waves and change memory outcomes, it cannot accurately locate the slow waves of the corresponding memory. It can only improve the intensity and phase coupling of the slow waves after tACS stimulation at an overall level. Summary of the Invention

[0005] The primary technical problem to be solved by this invention is to provide a non-invasive method for human sleep neuromodulation.

[0006] Another technical problem to be solved by the present invention is to provide a non-invasive human sleep neuromodulation system.

[0007] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution:

[0008] According to a first aspect of the present invention, a non-invasive method for human sleep neuromodulation is provided, comprising the following steps:

[0009] Acquire the user's first physiological signals;

[0010] The first physiological signal is analyzed to obtain multiple sleep stages of the user;

[0011] If the sleep stage meets the first preset condition, then the second physiological signal of the user under the corresponding sleep stage is obtained;

[0012] The second physiological signal is analyzed to obtain the user's slow-wave state;

[0013] Based on the user's slow wave state, obtain the phase angle information of the slow wave;

[0014] Upon detecting a series of slow-wave events, the last five seconds of the virtual channel are subjected to a second-order bidirectional filter of 0.5–1.2 Hz, and the waveform of the last five seconds is fitted using a cosine function to obtain its phase and frequency. Furthermore, the time when the waveform will appear at the next 270° position is predicted, and a first preset stimulus is applied at the 270° phase point. The first preset stimulus is tACS electrical stimulation.

[0015] After the first preset stimulus is completed, the user's third physiological signal is acquired;

[0016] Analyze the third physiological signal to obtain the user's physiological activity state;

[0017] When the user's physiological activity state is in an upward state, the user is given a second preset stimulus; wherein, the second preset stimulus includes at least TMR sound stimulation.

[0018] Preferably, the first physiological signal is an electroencephalogram (EEG) signal acquired through an electroencephalogram (EEG) to segment the user's sleep based on the EEG signal, thereby obtaining multiple sleep stages of the user;

[0019] The first preset condition is that the sleep stage is a deep sleep stage; and the second physiological signal is deep sleep data.

[0020] Preferably, the step of parsing the second physiological signal to obtain the user's slow-wave state includes:

[0021] The second physiological signal is analyzed to obtain the cumulative power across the entire frequency band;

[0022] Based on the cumulative power of the entire frequency band, the cumulative power of the slow wave band is obtained;

[0023] When the cumulative power of the slow wave band exceeds a preset ratio of the cumulative power of the entire frequency band, it is determined that the user has experienced continuous slow waves, thereby obtaining the user's slow wave state.

[0024] Preferably, the phase angle information satisfies a preset angle of 240° to 300°.

[0025] Preferably, the frequency range of the tACS electrical stimulation is a slow wave frequency, and each stimulation lasts for a first preset duration, so that the simulated slow wave is coupled with the spontaneous slow wave.

[0026] Alternatively, the frequency range of the tACS electrical stimulation is the spindle wave frequency, and each stimulation lasts for a second preset duration, so that the simulated spindle wave fits the peak of the spontaneous slow wave.

[0027] Preferably, during tACS electrical stimulation, the positions of the anode and cathode are as follows:

[0028] Cathode AFz, anode FCC3h, FCC4h;

[0029] Alternatively, the cathode is Cz, and the anode is CPP3h and CPP4h.

[0030] Preferably, after the tACS electrical stimulation is completed, a third preset time is waited, and the stimulation point of the sound is in the ascending state, and the sound stimulation is performed multiple times, with each time lasting no more than 1 second, and the interval between multiple sound stimulations being 4 to 6 seconds.

[0031] Preferably, the second preset stimulus is one or more of the following: sound stimulation, olfactory stimulation, light stimulation, or magnetic stimulation.

[0032] Preferably, when the phase angle information is detected to meet the preset angle, an electrical stimulation with a frequency range of 4 to 8 Hz is applied, and the intensity does not exceed 3 mA. Each stimulation lasts for 0.5 to 2 seconds, and the interval between multiple stimulations is 3 to 5 seconds.

[0033] According to a second aspect of the present invention, a non-invasive human sleep neuromodulation system is provided, comprising:

[0034] The acquisition unit is used to acquire the user's first physiological signal, acquire the user's second physiological signal under the corresponding sleep stage when the sleep stage meets the first preset condition, and acquire the user's third physiological signal after the first preset stimulus is completed.

[0035] The parsing unit, connected to the acquisition unit, is used to parse the first physiological signal to obtain multiple sleep stages of the user; it is also used to parse the second physiological signal to obtain the user's slow-wave state; and it is also used to parse the third physiological signal to obtain the user's physiological activity state.

[0036] The stimulation unit is connected to the analysis unit to obtain the phase angle information of the slow wave based on the user's slow wave state, and to perform a first preset stimulation when the phase angle information meets a preset angle, and to perform a second preset stimulation when the user's physiological activity state meets a second preset condition.

[0037] Compared with the prior art, the present invention has the following technical effects:

[0038] (1) By analyzing EEG signals and deep sleep data, the user's slow-wave state can be obtained, and tACS electrical stimulation can be performed at specific times based on the slow-wave state. Furthermore, TMR stimulation is performed after tACS electrical stimulation, thereby enhancing cortical activity through tACS stimulation first, and then enhancing the effect of subsequent TMR stimulation. Thus, by combining the regulation of HD-tACS and TMR, certain memories can be selectively improved to compensate for the shortcomings of tACS, and HD-tACS can enhance the intensity of slow waves, thereby also enhancing the intervention effect of TMR.

[0039] (2) Unlike traditional tACS, the present invention uses a high-density tACS device, which can concentrate the current intensity at a more accurate position compared with traditional tACS, thereby increasing the intensity of cortical current and having a better control effect.

[0040] (3) Unlike traditional TMR sound stimulation methods, this invention employs an innovative closed-loop TMR sound stimulation. By collecting EEG information during sleep, TMR sound stimulation is performed when a positive slope of the brain waves during sleep is detected. Compared to random stimulation, this method can more accurately apply sound stimulation to physiologically active states. Furthermore, compared to only detecting the ascending state of slow waves, the TMR sound stimulation method in this invention can apply more TMR stimulation and can stimulate the first possible slow wave, thus improving the intervention effect of TMR. Attached Figure Description

[0041] Figure 1 The overall flowchart of a non-invasive human sleep neuromodulation method provided in the first embodiment of the present invention;

[0042] Figure 2 A detailed flowchart of a non-invasive human sleep neuromodulation method provided in the first embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of the sleep staging model in the first embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of tACS electrical stimulation in the first embodiment of the present invention.

[0045] Figure 5 This is a schematic diagram of the coupling between the sine wave and the spontaneous slow wave in the first embodiment of the present invention;

[0046] Figure 6 This is a schematic diagram of the physiologically active state in the first embodiment of the present invention;

[0047] Figure 7 A schematic diagram of a non-invasive human sleep neuromodulation system provided in the second embodiment of the present invention;

[0048] Figure 8 This is a schematic diagram of a non-invasive human sleep neuromodulation system provided in the third embodiment of the present invention. Detailed Implementation

[0049] The technical content of the present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0050] The core technical concept of this invention lies in combining the modulation of HD-tACS (high-density tACS) and TMR (target memory reactivation), with TMR stimulation performed after tACS modulation. The principle is that tACS stimulation enhances cortical activity, followed by TMR stimulation, thereby enhancing the effect of TMR. This allows for selective enhancement of certain memories to compensate for the shortcomings of tACS, and HD-tACS can also enhance the intensity of slow waves, thus further strengthening the intervention effect of TMR.

[0051] It is understood that, in this embodiment of the invention, this dual regulation can not only more effectively enhance specific memories, but also improve the efficiency and precision of interventions, showing unique advantages, especially when it is necessary to strengthen certain specific memories or cognitive functions. This combination of technologies provides new possibilities for personalized memory intervention and cognitive enhancement, and has broad application prospects, particularly in areas such as sleep disorders, learning disabilities, or memory decline.

[0052] First Embodiment

[0053] like Figure 1 and Figure 2 As shown, the first embodiment of the present invention provides a non-invasive method for human sleep neuromodulation, which mainly includes the following steps:

[0054] The first step is to acquire the user's first physiological signal (in this embodiment, the scalp EEG signal) to perform real-time sleep staging and determine the user's deep sleep stage.

[0055] The second step involves detecting slow-wave power based on the sleep stage results, while the user is in deep sleep, using the second physiological signal (deep sleep data in this embodiment).

[0056] The third step is to calculate the phase angle of the slow wave based on the slow wave state, so as to perform the first preset stimulation (tACS electrical stimulation in this embodiment) at a specific time.

[0057] The fourth step is to obtain the user's third physiological signal (in this embodiment, the activity signal of the cerebral cortex) after a period of tACS electrical stimulation, and to obtain the user's physiological activity state by analyzing the third physiological signal.

[0058] Fifth, when the physiological activity state is ascending, a second preset stimulus (TMR sound stimulation in this embodiment) is applied, thereby achieving non-invasive neuromodulation of the user through the combination of HD-tACS electrical stimulation and TMR sound stimulation.

[0059] The following is a detailed explanation of each step:

[0060] Step S1: Obtain the user's first physiological signal to stage the user's sleep.

[0061] In this embodiment, the user's brainwave signals (i.e., the first physiological signal) are first acquired through electroencephalography (EEG), including sleep EEG signals throughout the night. These EEG signals are then used to perform real-time sleep staging to determine the user's current sleep stage. Deep sleep includes N2 and N3 stages; therefore, the neuromodulation in this embodiment needs to be performed during the N2 and N3 stages.

[0062] To achieve highly stable slow-wave power detection, virtual channels are calculated by averaging five frontocentral EEG channels (Fz, FC1, FC2, F3, F4) from a 10–20 system to determine the overall synchronous activity of EEG recorded during sleep. Virtual channels allow observation of moments with relatively high slow-wave power, and the included channels are stored in a running 30-second buffer. The EEG acquisition device continuously inputs the subject's whole-brain sleep data into the computer via the Lab Streaming Layer (LSL), while the data acquisition module collects data from the channels required by the aforementioned module in a separate, long-running parallel thread (e.g., the sleep staging model requires acquisition of C3, M2, EOG, and EMG channels). This thread is placed as the first parallel thread and continuously collects sleep data regardless of the main thread's operation.

[0063] To achieve more stable results, a certain duration of data is needed for preprocessing before being input into other modules. Therefore, this acquisition module defines a five-minute data buffer and a forty-second data warm-up period. During the forty-second warm-up period after module activation, the framework only pulls data to expand the buffer; during this time, other modules do not read from the buffer. After the warm-up period, the necessary modules are activated while data is being pulled, and the buffer continues to grow until the maximum buffer duration is reached.

[0064] In this embodiment, the selected EEG signal data undergoes online preprocessing. After a second-order bidirectional filter of 0.1–250 Hz, the last five seconds of the data are taken. After removing channels with amplitudes exceeding 500 μV, the data is averaged. The virtual channel data in the updated buffer is then further processed. The preprocessing procedure includes filtering (EEG 0.1–0.35 Hz, EMG 10–100 Hz, ECG 0.3–35 Hz) and using the contralateral mastoid process as an online reference. Subsequently, during real-time sleep staging, a dedicated sleep staging model (e.g., [model name missing]) is trained by combining a deep learning model with expert sleep staging. Figure 3(As shown). For further reference, please refer to the earlier patent application with application number 202411822510.7 (application date: December 11, 2024, patent title: A real-time sleep staging modeling method, real-time encoding and decoding method and system).

[0065] Step S2: Acquire the user's second physiological signal and analyze the second physiological signal to obtain the user's slow wave state.

[0066] After the user's sleep is staged in step S1, deep sleep data (i.e., the second physiological signal) can be obtained based on the sleep stage results. Then, by analyzing this deep sleep data, the cumulative power across the entire frequency band is obtained; based on the cumulative power across the entire frequency band, the cumulative power of the slow wave band is obtained; when the cumulative power of the slow wave band exceeds a preset ratio of the cumulative power across the entire frequency band, it is determined that the user has experienced continuous slow waves, thus obtaining the user's slow wave state.

[0067] Specifically, slow-wave signals only appear in sleep stages N2 and N3. After acquiring the second physiological signal, the slow-wave detection module can detect the user's unique slow waves during non-rapid eye movement (NREM) sleep. The slow waves are then obtained by calculating the power of the second physiological signal. In this embodiment, when the user enters a stable NREM sleep stage (N2, N3), data from the previous 30 seconds is read every 10ms and subjected to unidirectional filtering of 0.5–1.2Hz. When the cumulative power of the slow waves (0.5–1.2Hz) is 20% of the total cumulative power across the full power band from 0.1 to 250Hz, a continuous slow-wave event is determined to have occurred, and a first preset stimulation (tACS electrical stimulation in this embodiment) is planned for the user.

[0068] Step S3: Calculate the phase angle of the slow wave based on the slow wave state, and then perform the first preset stimulus at a specific time.

[0069] After a series of slow-wave events are detected based on step S2, the last five seconds of the virtual channel are subjected to a second-order bidirectional filter of 0.5–1.2 Hz, and the waveform of the last five seconds is fitted with a cosine function to obtain its phase and frequency. The time of occurrence of the next 270° position of the waveform (since tACS is a sine wave, stimulation should be performed at the 270° position fitted by the cosine function) is predicted, and the first preset stimulus is performed at the 270° phase point.

[0070] Specifically, after confirming the occurrence of continuous slow-wave events, tACS electrical stimulation is applied using an HD-tACS device. For example... Figure 4As shown, tACS electrodes were placed at three points: AFz, FCC3h, and FCC4h (the locations of the 128-channel electrode points). This maximized activation of the superior thalamic cortex (because the thalamus is where spindle waves are generated, and spindle waves and slow waves are considered important sleep rhythms for memory consolidation; stimulating this area can effectively alter these sleep rhythms), achieving a standard field strength. The duration of tACS was set to 6.66 seconds, the stimulation frequency was approximately 0.8 Hz (slow wave frequency), for 5 cycles, and the total current intensity was set to 1.5 mA. Therefore, as... Figure 5 As shown, the sine wave of tACS and the spontaneous slow wave can be coupled together to increase the amplitude of the slow wave.

[0071] In another embodiment, the first preset stimulus is an electrical stimulus with a duration of 0.5 to 2 seconds and a frequency range of 10 to 16 Hz (approximate to the frequency of a spindle wave), thereby enabling the simulated spindle wave to fit the upward state of a slow wave.

[0072] Step S4: Obtain the user's third physiological signal and analyze the third physiological signal.

[0073] In this embodiment, after the first preset stimulus is completed, the brain will be silent for 10 seconds. After the brain is silent, the cortex is activated and the physiological signal at this moment is obtained. This physiological signal is the third physiological signal. Then, the physiological activity state is obtained based on the third physiological signal.

[0074] like Figure 6 As shown, based on the characteristics of neuronal firing activity, when the slope of the EEG is upward (i.e., when the EEG signal moves from the trough to the peak, it is a physiologically active state), this embodiment marks it as a physiologically active state (different from the ascending and descending states). When the EEG activity in the third physiological signal is detected to be in a physiologically active state, a second preset stimulus is planned to be applied to the user. The reason for applying stimulation in a physiologically active state is that EEG activity is more active in this state, and by applying stimulation at this location, it is easier to apply stimulation to the ascending state of slow waves, and more stimulation can be applied.

[0075] Step S5: Perform the second preset stimulus while the physiologically active state is in the ascending state.

[0076] In this embodiment, the second preset stimulus is a TMR sound stimulus. Before the stimulus begins, the user is asked to memorize predefined image-audio pairs, each image and audio having the same semantic meaning. For example, an image labeled "sheep" is paired with a sheep's bleating sound. Over a period of time, the user is exposed to 1000 such pairings, presented in image-audio or audio-image order, and is asked to determine whether these pairs correspond correctly.

[0077] Subsequently, online real-time sleep staging was performed during nocturnal EEG recording. When the user entered N2 and N3 sleep stages and slow waves meeting the power criteria were detected, tACS stimulation was performed, and EEG signals were monitored in real time 10 seconds after tACS stimulation. When the slope of the EEG signal was positive (i.e., the physiologically active state was ascending), auditory cues randomly selected from the image-audio pair were played every 4–6 seconds, with a duration of 0.5 seconds.

[0078] In another embodiment, the second preset stimulus is one or more of the following: sound stimulation, olfactory stimulation, light stimulation, or magnetic stimulation.

[0079] The human sleep neuromodulation method provided in this invention is a closed-loop modulation method. This method can automatically perform TACS electrical stimulation at a calculated specific slow-wave phase based on the user's sleep cycle and EEG signal state. Specifically, after detecting continuous slow-wave events in step S2, the last five seconds of the virtual channel are subjected to a second-order bidirectional filter of 0.5–1.2 Hz, and the waveform of the last five seconds is fitted using a cosine function to obtain its phase and frequency. The timing of the next 270° position (near the trough) of this waveform is predicted, and a first preset stimulation is applied at the 270° phase point. Applying TACS at the 270° phase point (early rising edge of the slow wave) maximizes neuronal synchronization. This phase corresponds to the repolarization period after synaptic inhibition, and electric field stimulation easily induces clustered discharges.

[0080] Ten seconds after stimulation, ascending TMR sound stimulation is performed (EEG signals are monitored in real time 10 seconds after tACS stimulation. When the slope of the EEG signal is positive, it indicates that the marker neuron cluster has shifted from inhibition to excitation, i.e., the physiologically active state is the ascending state. Auditory cues randomly selected from the image-audio pair are played every 4-6 seconds, with a duration of 0.5 seconds). This precise stimulation of tACS at a slow wave phase of 270° (near the trough) and the temporal coupling of ascending TMR enhance the offline reconstruction of memory traces through a double entrainment effect, which can enhance the amplitude and synchronicity of slow wave oscillations and promote synaptic plasticity. Therefore, this invention directly captures the ascending state when the EEG slope changes from negative to positive, without relying on a slow wave amplitude threshold.

[0081] Furthermore, this invention, by setting an ascending state for TMR stimulation, can apply more auditory stimulation, leading to more repetitive activity. This is because the ascending state (positive slope of the EEG signal) represents a window of increased excitability in cortical neuronal populations. During this period, the hippocampus transmits memory information to the cortex with the highest efficiency, resulting in enhanced sensitivity to TMR response. This allows for precise activation of neural traces associated with learning, avoiding disruption to sleep continuity. In contrast, existing stimulation strategies based on preset slow-wave thresholds tend to miss some low-amplitude slow waves and cannot distinguish neuronal excitability. Moreover, only a small portion of slow-wave events meet the stimulation conditions above the threshold, resulting in limited TMR stimulation opportunities and unsatisfactory stimulation effects.

[0082] Second Embodiment

[0083] like Figure 7 As shown, based on the first embodiment described above, the second embodiment of the present invention provides a non-invasive human sleep neuromodulation system, including an acquisition unit 1, an analysis unit 2, and a stimulation unit 3. The acquisition unit 1 is used to acquire user physiological signals, including a first physiological signal, a second physiological signal, and a third physiological signal. Since the three physiological signals are not of the same type, the acquisition unit 1 can be pre-configured with three acquisition modules 11 to acquire different types of physiological signals at different times. The specific signal acquisition periods are described in the first embodiment above and will not be repeated here.

[0084] The analysis unit 2 is connected to the acquisition unit 1 to analyze three physiological signals. The analysis unit 2 includes a pre-set sleep staging model 21 for analyzing the first physiological signal. Furthermore, the analysis unit 2 includes a power detection module 22 for analyzing the second physiological signal through power detection. Additionally, the analysis unit 2 includes an electroencephalogram (EEG) analysis module 23 to determine whether the slope of the neuronal activity signal is rising or falling, thereby analyzing the third physiological signal.

[0085] Stimulation unit 3 is connected to parsing unit 2 for performing a first preset stimulus followed by a second preset stimulus. It is understood that stimulation unit 3 includes a first stimulation module 31 and a second stimulation module 32, where the first stimulation module 31 performs electrical stimulation and the second stimulation module 32 performs auditory stimulation.

[0086] It is understood that each unit and module in this embodiment is a functional module that implements each step in the first embodiment, but is not limited to this module type. In other embodiments, it can also be replaced with a module structure to implement each step in the first embodiment.

[0087] Third Embodiment

[0088] Based on the aforementioned non-invasive human sleep neuromodulation method, the third embodiment of the present invention further provides a non-invasive human sleep neuromodulation system. For example... Figure 8 As shown, the human sleep neuromodulation system includes one or more processors and a memory. The memory is coupled to the processors and is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the human sleep neuromodulation method as described in the above embodiments.

[0089] The processor controls the overall operation of the human sleep neuromodulation system to complete all or part of the steps of the aforementioned non-invasive human sleep neuromodulation method. The processor can be a central processing unit (CPU), graphics processing unit (GPU), field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), digital signal processing (DSP) chip, etc. The memory stores various types of data to support the operation of the human sleep neuromodulation system. This data may include, for example, instructions for any application or method operating on the human sleep neuromodulation system, as well as application-related data. The memory can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, etc.

[0090] In one exemplary embodiment, the human sleep neuromodulation system may be implemented by a computer chip or physical entity, or by a product with certain functions, to perform the non-invasive human sleep neuromodulation method described above and achieve the same technical effect as the method described above. A typical embodiment is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, an in-vehicle human-machine interaction device, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0091] In another exemplary embodiment, the present invention also provides a computer-readable storage medium including program instructions that, when executed by a processor, implement the steps of the human sleep neuromodulation method in any of the above embodiments. For example, the computer-readable storage medium may be the memory including the program instructions described above, which can be executed by the processor of the neuromodulation system to complete the non-invasive human sleep neuromodulation method described above and achieve the same technical effects as the method described above.

[0092] In summary, the non-invasive human sleep neuromodulation method and system provided by the embodiments of the present invention have the following beneficial effects:

[0093] (1) By analyzing EEG signals and deep sleep data, the user's slow-wave state can be obtained, and tACS electrical stimulation can be performed at specific times based on the slow-wave state. Furthermore, TMR stimulation is performed after tACS electrical stimulation, thereby enhancing cortical activity through tACS stimulation first, thus enhancing the effect of subsequent TMR stimulation. Therefore, by combining HD-tACS and TMR regulation, certain memories can be selectively improved to compensate for the shortcomings of tACS, and HD-tACS can enhance the intensity of slow waves, thereby also enhancing the intervention effect of TMR.

[0094] (2) Unlike traditional tACS, the present invention uses a high-density tACS device, which can concentrate the current intensity at a more accurate position compared with traditional tACS, thereby increasing the intensity of cortical current and having a better control effect.

[0095] (3) Unlike traditional TMR sound stimulation methods, this invention employs an innovative closed-loop TMR sound stimulation. By collecting EEG information during sleep, TMR sound stimulation is performed when a positive slope of the brain waves during sleep is detected. Compared to random stimulation, this method can more accurately apply sound stimulation to the ascending state. Furthermore, compared to only detecting slow waves and stimulating the ascending state of slow waves, the TMR sound stimulation method in this embodiment can apply more TMR stimulation and can stimulate the first possible slow wave, thus improving the intervention effect of TMR.

[0096] It should be noted that the above embodiments are merely illustrative examples. The technical solutions of each embodiment can be combined, and all are within the protection scope of this invention.

[0097] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0098] The above provides a detailed description of the human sleep neural regulation method and system provided by this invention. Any obvious modifications made by those skilled in the art without departing from the essence of this invention will constitute an infringement of the patent rights of this invention and will incur corresponding legal liability.

Claims

1. A non-invasive method for regulating human sleep neural pathways, characterized in that... Includes the following steps: Acquire the user's first physiological signals; The first physiological signal is analyzed to obtain multiple sleep stages of the user; If the sleep stage meets the first preset condition, then the second physiological signal of the user under the corresponding sleep stage is obtained; The second physiological signal is analyzed to obtain the user's slow-wave state; Based on the user's slow wave state, obtain the phase angle information of the slow wave; Upon detecting continuous slow-wave events, the virtual channel's last preset duration is subjected to second-order bidirectional filtering within a preset frequency range. A cosine function is then used to fit the waveform of the last preset duration to obtain its phase and frequency. Furthermore, the time when the waveform will appear at the next 270° position is predicted, and a first preset stimulus is applied at the 270° phase point. This first preset stimulus is tACS electrical stimulation. The frequency range of the tACS electrical stimulation is the slow-wave frequency, and each stimulation lasts for a first preset duration, so that the simulated slow wave couples with the spontaneous slow wave. Alternatively, the frequency range of the tACS electrical stimulation is the spindle wave frequency, and each stimulation lasts for a second preset duration, so that the simulated spindle wave fits onto the crest of the spontaneous slow wave. After the first preset stimulus is completed, wait for the brain to remain silent for 10 seconds before acquiring the user's third physiological signal; The third physiological signal is analyzed to obtain the user's physiological activity state; wherein, when the slope of the EEG of the third physiological signal is upward, it is marked as a physiologically active state; When the brain electrical activity in the third physiological signal is detected to be in a physiologically active state, a second preset stimulus is applied to the user; wherein the second preset stimulus includes at least TMR sound stimulation.

2. The method for human sleep neural regulation as described in claim 1, characterized in that: The first physiological signal is an electroencephalogram (EEG) signal acquired through an electroencephalogram (EEG) to segment the user's sleep and thereby obtain multiple sleep stages of the user. The first preset condition is that the sleep stage is a deep sleep stage; and the second physiological signal is deep sleep data.

3. The method for regulating human sleep neural pathways as described in claim 2, characterized in that... Analyzing the second physiological signal to obtain the user's slow-wave state specifically includes: The second physiological signal is analyzed to obtain the cumulative power across the entire frequency band; Based on the cumulative power of the entire frequency band, the cumulative power of the slow wave band is obtained; When the cumulative power of the slow wave band exceeds a preset ratio of the cumulative power of the entire frequency band, it is determined that the user has experienced continuous slow waves, thereby obtaining the user's slow wave state.

4. The method for regulating human sleep neural pathways as described in claim 1, characterized in that: The phase angle information satisfies a preset angle of 240° to 300°.

5. The method for regulating human sleep neural pathways as described in claim 1, characterized in that... During tACS electrical stimulation, the positions of the anode and cathode are as follows: Cathode AFz, anode FCC3h, FCC4h; Alternatively, the cathode is Cz, and the anode is CPP3h and CPP4h.

6. The method for regulating human sleep neural pathways as described in claim 1, characterized in that: The second preset stimulus is multiple sound stimuli, each lasting no more than 1 second, with an interval of 4 to 6 seconds between the multiple sound stimuli.

7. The method for regulating human sleep neural pathways as described in claim 1, characterized in that: The second preset stimulus is one or more of the following: sound stimulation, olfactory stimulation, light stimulation, or magnetic stimulation.

8. The method for regulating human sleep neural pathways as described in claim 1, characterized in that: When the phase angle information is detected to meet the preset angle, an electrical stimulation with a frequency range of 4 to 8 Hz is applied, and the intensity does not exceed 3 mA. Each stimulation lasts for 0.5 to 2 seconds, and the interval between multiple stimulations is 3 to 5 seconds.

9. A non-invasive human sleep neuromodulation system for implementing the human sleep neuromodulation method as described in claim 1, characterized in that... include: The acquisition unit is used to acquire the user's first physiological signal, acquire the user's second physiological signal under the corresponding sleep stage when the sleep stage meets the first preset condition, and acquire the user's third physiological signal after the first preset stimulus is completed. The parsing unit, connected to the acquisition unit, is used to parse the first physiological signal to obtain multiple sleep stages of the user; it is also used to parse the second physiological signal to obtain the user's slow-wave state; and it is also used to parse the third physiological signal to obtain the user's physiological activity state. The stimulation unit is connected to the analysis unit to obtain the phase angle information of the slow wave based on the user's slow wave state, and to perform a first preset stimulation when the phase angle information meets a preset angle, and to perform a second preset stimulation when the user's third physiological signal meets a second preset condition.

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