Brain wave audio sleep aiding method and brain wave audio sleep aiding device

Abstract

The invention belongs to the technical field of sleep aiding, and particularly relates to a brain wave audio sleep aiding method, which comprises the following steps that: firstly, a brain wave detection module detects a brain wave signal of a user, preprocesses the brain wave signal to obtain a sleep characteristic parameter, and transmits the sleep characteristic parameter to a control module; afterwards, the control module judges the sleep state according to the parameters, outputs matched audio parameters to the audio player, and plays corresponding binaural beat audio; if the sleep characteristic parameter shows that the user enters the deep sleep state, the control module stops audio playing and starts timing; when timing reaches a preset time threshold value, the brain wave detection module detects sleep characteristic parameters again, and if the user does not sleep deeply, audio playing is started again; according to the technical scheme, sleep interruption caused by human intervention is effectively avoided, and lasting sleep-aiding experience is provided for the user; in addition, the invention further discloses a device capable of realizing the brain wave audio sleep aiding method, and the sleep aiding quality of the user is further improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of sleep aid, and in particular relates to a brain wave audio sleep aid method and a brain wave audio sleep aid device. Background Art

[0002] With the rapid development of modern society, people are facing psychological pressure and accelerated pace of life, which leads to many people suffering from insomnia and other sleep disorders. Good sleep is not only essential for physical health, but also helps to improve work efficiency and quality of life. In recent years, brain wave audio sleep aid method has gradually attracted attention. This method is based on the characteristics of EEG activity, and promotes users to fall asleep quickly and prolong sleep time by analyzing and adjusting brain wave frequency. Existing studies have shown that different brain wave frequencies are closely related to sleep stages. For example, alpha waves are related to relaxation, theta waves often appear in the light sleep stage, and delta waves are characteristics of deep sleep. By matching the corresponding audio parameters with the user's current brain wave state, the best sleep state can be promoted. Many existing brain wave audio sleep aid methods are mainly based on monitoring the user's sleep stage, and adjusting the playback audio in real time according to the monitoring results to help users fall asleep.

[0003] Although the existing brainwave audio sleep aid method can help users sleep to a certain extent, there are some significant shortcomings. First, many insomnia patients often wake up after a period of deep sleep, and then lose the opportunity to enter a deep sleep state, resulting in a decrease in sleep quality. At this time, the existing brainwave audio sleep aid device often stops working, and the user must manually restart the device, which interrupts the sleep cycle and increases the user's fatigue and anxiety.

[0004] Secondly, many brainwave audio sleep aid devices rely on manual intervention by the user and lack an automatic adjustment mechanism, which makes it impossible for users to get continuous audio support, ultimately affecting the sleep aid effect.

[0005] Therefore, there is an urgent need for a brainwave audio sleep-aiding method and device that can intelligently monitor the user's sleep state and automatically adjust the audio output when necessary to improve the sleep-aiding effect. Summary of the invention

[0006] One of the purposes of the present invention is to address the deficiencies in the prior art and to provide a brain wave audio sleep aid method that can intelligently monitor the user's sleep state and reduce the need to re-use brain wave audio sleep aid due to the user's short deep sleep cycle.

[0007] In order to achieve the above technical objectives, this application implements the following technical solutions:

[0008] A brain wave audio sleep aid method comprises the following steps:

[0009] S101, detecting the user's brain wave signal according to the brain wave detection module to obtain a brain wave data signal, preprocessing the brain wave data signal through the preprocessing module to obtain sleep characteristic parameters and transmitting the sleep characteristic parameters to the control module;

[0010] S201, the control module determines the sleep state of the user according to the sleep characteristic parameters, and outputs audio parameters matching the sleep state to the audio player according to the sleep state, and the audio parameters are used to drive the audio player to output binaural beat audio corresponding to the audio parameters;

[0011] S301, when the sleep characteristic parameter is less than or equal to the deep sleep threshold parameter, the control module drives the audio player to stop working, and at the same time drives the timing unit of the control module to start timing;

[0012] S401, when the timing parameter of the timing unit is the same as the first time threshold received by the control module through the control terminal or the second time threshold stored in the storage unit of the control module, the control module drives the brain wave detection module to re-collect the sleep characteristic parameter of the user and determine whether it is less than or equal to the deep sleep threshold parameter;

[0013] When the re-collected sleep characteristic parameter of the user is greater than the deep sleep threshold parameter, the process re-enters step S201.

[0014] The above technical solution produces the following technical effects:

[0015] This application proposes an improved brainwave audio sleep-aiding method, aiming to overcome the above-mentioned defects. First, the brainwave detection module monitors the user's brainwave signals in real time, and the control module can judge the user's sleep state in time in combination with the changes in the sleep characteristic parameters. When the user's sleep characteristic parameters are lower than the deep sleep threshold, the system automatically stops the audio playback, and then starts the timing unit to record the time. Once the preset time threshold is met, the control module will re-collect the user's brainwave data and judge its status. This design effectively avoids sleep interruptions caused by human intervention and provides users with a lasting sleep-aiding experience.

[0016] As a further improvement to the brain wave audio sleep-aiding method of the present application, the sleep characteristic parameter value is the frequency value, the audio parameter is the binaural playback frequency difference; and the deep sleep threshold parameter is 3Hz.

[0017] As a further improvement to the brainwave audio sleep aid method of the present application, the sleep state is divided into a first sleep state, a second sleep state and a third sleep state;

[0018] The value range of the sleep characteristic parameters corresponding to the first sleep state is: 8-12Hz;

[0019] The value range of the sleep characteristic parameters corresponding to the second sleep state is: 4-8Hz;

[0020] The value range of the sleep characteristic parameter corresponding to the third sleep state is: 3-4Hz.

[0021] As a further improvement to the brainwave audio sleep aid method of the present application, the audio parameters include a first binaural audio parameter matching the first sleep state, a second binaural audio parameter matching the second audio state, and a third binaural audio parameter matching the third sleep state;

[0022] The value range of the first binaural audio parameter is 8-10Hz;

[0023] The value range of the second binaural audio parameter is 4-6Hz;

[0024] The value range of the third binaural audio parameter is 0.5-4 Hz.

[0025] As a further improvement to the brainwave audio sleep aid method of the present application, step S201 further includes the following steps:

[0026] S202: When the user's sleep state satisfies the first sleep state, and the sleep characteristic parameter increases or decreases by a first difference, the audio parameter increases or decreases by a first change value; wherein the sleep characteristic parameter still satisfies the first sleep state after increasing or decreasing;

[0027] S203: When the user's sleep state satisfies the second state, and the increase or decrease amount of the sleep characteristic parameter satisfies the second difference value, the audio parameter increases or decreases by a second change value; wherein the sleep characteristic parameter still satisfies the second sleep state after the increase or decrease;

[0028] S204: When the user's sleep state satisfies the second state and the increase or decrease amount of the sleep characteristic parameter satisfies the third difference value, the audio parameter increases or decreases by a third change value; wherein the sleep characteristic parameter still satisfies the third sleep state after increasing or decreasing.

[0029] As a further improvement to the brain wave audio sleep aid method of the present application, the first difference is 1 Hz, and the first change value is 0.5 Hz; the second difference is 1 Hz, and the first change value is 0.5 Hz; the third difference is 0.25 Hz, and the third change value is 0.25 Hz.

[0030] As a further improvement of the brain wave audio sleep aid method of the present application, the preprocessing module divides the brain wave data signal transmitted by the brain wave detection module into different time windows, and performs bandpass filtering, detrending and de-artifacting processing on the brain wave data signal in each time window in turn.

[0031] As a further improvement to the brainwave audio sleep aid method of the present application, during the bandpass filtering process, the low-frequency cutoff frequency is 0.5 Hz and the high-frequency cutoff frequency is 30 Hz;

[0032] In the detrending process, the mean value of the brain wave data signal in the time window is calculated and the mean value of the brain wave data signal calculated by the mean value in the time window is removed to obtain the detrended brain wave data signal;

[0033] The mean calculation formula is:

[0034]

[0035] in, is the mean value of the brain wave data signal in a single time window; N is the total number of brain wave data signals in a single time window; X i It is the voltage value sampled at the ith brain wave data signal within a single time window.

[0036] As a further improvement to the brain wave audio sleep aid method of the present application, in the process of removing artifacts, the brain wave data signals within the time window that are greater than the artifact threshold are removed to obtain sleep characteristic parameters;

[0037] The calculation formula of artifact threshold T is as follows:

[0038] T=μ+k·σ

[0039] Among them, μ is the mean difference of the brain wave data signal in a single time window; k is an adjustable parameter; σ is the standard deviation of the brain wave data signal in a single time window; and the artifact threshold T is the voltage amplitude threshold.

[0040] The second purpose of the present invention is to provide a brain wave audio sleep aid device that can intelligently monitor the user's sleep state in order to address the deficiencies of the prior art.

[0041] In order to achieve the above technical objectives, this application implements the following technical solutions:

[0042] A brain wave audio sleep aid device, comprising:

[0043] A brain wave detection module, comprising at least one electrode for collecting brain wave signals, the electrode being placed in the frontal lobe area of ​​the user's head;

[0044] A preprocessing module, including a filter and a processing unit, wherein the filter is used to filter the brain wave data signal generated by electrode collection, and the processing unit is used to perform detrending and de-artifacting processing on the filtered brain wave data signal to obtain sleep characteristic parameters;

[0045] An audio player for playing binaural beat audio, including left and right earphones with different channels;

[0046] A control module, which drives the working state of the audio player using any of the above-mentioned brain wave audio sleep aid methods, including a storage unit and a timing unit, wherein the storage unit is used to store the sleep characteristic parameters detected by the brain wave detection module, the audio parameters for driving the audio player to play the binaural beat audio, and a second time threshold for determining whether the audio player stops working;

[0047] The control module is connected to the control terminal via a communication network, and the control terminal is used to transmit a first time threshold for determining whether the audio player stops working.

[0048] The above technical solution produces the following technical effects:

[0049] The brainwave audio sleep aid device of this application further improves sleep quality and adapts to user needs through intelligent and automated processing methods combined with precise adjustment of binaural beat audio. The intelligent judgment function of the control module can automatically adjust the audio playback according to the user's sleep state, ensuring that the user has a continuous sleep aid experience in the best sleep state. At the same time, through the communication between the control terminal and the control module, the user can remotely set and adjust the sleep aid parameters, which increases the flexibility and convenience of use. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0051] Figure 1 This is a flow chart of the brainwave audio sleep aid method according to Example 1 of the present invention;

[0052] Figure 2 This is a flowchart of the brain wave audio sleep aid method according to Example 1 of the present invention;

[0053] Figure 3 This is a flow chart of the brainwave audio sleep aid method according to Example 2 of the present invention;

[0054] Figure 4 This is a schematic diagram of the structure of a brain wave audio sleep aid device according to Example 3 of the present invention;

[0055] Figure 5 This is a schematic diagram of the structure of the preprocessing module of Example 3 of the present invention;

[0056] Figure 6 This is a flowchart of the pre-processing module of Example 3 of the present invention;

[0057] in:

[0058] 1- Brainwave detection module;

[0059] 11-electrode;

[0060] 2- Preprocessing module;

[0061] 21- filter;

[0062] 22-processing unit;

[0063] 3- Audio player;

[0064] 4- Control module;

[0065] 41- storage unit;

[0066] 42-timing unit;

[0067] 5-Control terminal. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application. The terms used herein in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0071] It should be clear to those skilled in the art that the embodiments of the present invention may be expressed as methods, systems or computer program products. Therefore, the present invention may be implemented entirely in hardware, entirely in software, or in a combination of hardware and software.

[0072] In the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected" and "fixed" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0073] In order to facilitate those skilled in the art to understand the technical solution of this application, this application will further explain the following technical terms:

[0074] Electroencephalogram (EEG) signals refer to electrical signals generated by placing electrodes on the scalp to record the electrical activity of brain neurons. The characteristic frequency of brain waves can reflect a person's state of consciousness, emotional changes, and brain activity.

[0075] Delta waves are a major type of brain wave in an electroencephalogram (EEG), and their frequency range is usually between 0.5 Hz and 4 Hz. Some scholars or research institutions define the frequency range of Delta waves as 0.5 Hz to 3 Hz. It is the lowest frequency of all brain waves, representing that the brain is in a deep sleep and very relaxed state. The appearance of Delta waves usually indicates that the body has entered a deep sleep stage, especially deep NREM sleep. In this stage, people's consciousness is almost completely inactive, their ability to respond to external stimuli is reduced, and the whole person is in a highly restorative state.

[0076] Taking into account the patent entitled Brainwave Audio Sleep Aid Method, Device, Equipment and Storage Medium with publication number CN114099894A, Delta wave (δ wave in this document), in order to avoid the mismatch between the technology of this application and the existing electroencephalogram (EEG) signal band, this application will limit the parameter for judging whether the user has entered deep sleep to 3Hz, but it does not deny that this application identifies Delta wave of 0.5 Hz to 4 Hz.

[0077] Theta waves are brain waves that fall within the 4 Hz to 8 Hz frequency range on an electroencephalogram (EEG). Theta waves are often associated with states of relaxation, dreaming, light sleep, and specific cognitive processes. The signals generated by synchronized neuronal activity in different areas of the brain are often significantly increased during mild relaxation, meditation, creative thinking, or light sleep. Its waveform is usually wider and may be lower in amplitude, and the vibration frequency of theta waves is slightly higher than that of delta waves.

[0078] Alpha waves are brain waves in the frequency range of 8 Hz to 12 Hz in the electroencephalogram (EEG). It is often associated with a relaxed state of wakefulness, inner peace, and light concentration. Alpha waves are most prominent when the user closes their eyes and relaxes brain activity. Their activity usually occurs in awake but relaxed states, such as relaxed thinking, meditative states, or when the eyes are closed. It appears as a regular fluctuation on the EEG. When completing tasks that require high concentration or hard thinking, the activity of Alpha waves usually decreases, and increases when relaxing. In biofeedback and neurofeedback techniques, Alpha waves can also be used as a regulatory target to help individuals achieve a more peaceful state.

[0079] A control terminal refers to a point in a network system that is used to control, monitor or manage various devices or functions. Typically, a control terminal can be a mobile device (such as a mobile phone, tablet), a computer, dedicated hardware or other terminal device that allows users to interact with the system through a certain interface. From the user's perspective, the control terminal is responsible for sending instructions, receiving status feedback, and displaying relevant data. The control terminal allows users to set it up according to personal needs and preferences, including adjusting the "first time threshold." This threshold usually refers to the minimum standard for detecting a suitable frequency or characteristic signal for evaluating deep sleep brain waves within a specific time to activate the sleep aid function.

[0080] Binaural beats are audio signals of different frequencies played to the left and right ears through headphones, and the brain produces corresponding frequency perception, i.e. "beats". For example, if 100Hz is played to the left ear and 104Hz is played to the right ear, the brain will perceive a frequency difference of 4Hz. This frequency difference can guide brain waves into a specific frequency state to achieve relaxation, enhance concentration, help sleep, etc.

[0081] Time Window is an important concept in signal processing and analysis, which refers to a segment of signal data selected within a specific time interval. Time windows are usually used to perform segmented analysis on signals to extract features or perform certain operations. A time window is a fixed time period that contains all sample data of the signal within this time period. It is usually defined by a start time and an end time, or specified in a fixed size. In the preprocessing process of the brain wave data signal in this application, the time window is used in the following aspects:

[0082] First, by setting windows on the time axis, long-term brain wave data signals (note that there are multiple brain wave signals in the specific acquisition process) can be divided into multiple small segments, making processing and analysis easier to manage. For example, the signal can be divided into multiple small segments of 1 second in length.

[0083] Secondly, feature extraction is performed on the signal data in each time window, and the mean, standard deviation, amplitude spectrum, frequency domain features, etc. of each window can be calculated. These features are helpful for subsequent classification and analysis. When performing artifact detection, the mean and standard deviation of the signal can be calculated within the specified time window, so as to set the corresponding artifact threshold.

[0084] Band-pass filtering is a signal processing technique that allows signals within a specific frequency range to pass through while suppressing frequency signals below and above that range. The design of a band-pass filter usually involves selecting a lower frequency (low-frequency cutoff frequency) and an upper frequency (high-frequency cutoff frequency). Frequency signals between the ranges will be allowed to pass, while signals outside the range will be attenuated or filtered out. Band-pass filtering can control which frequency signals are retained and which signals are suppressed, which makes it important in various types of signal processing. For example, in electroencephalogram (EEG) signal processing, band-pass filters can be used to extract activities within the frequency range of specific brain waves (such as Delta waves, Theta waves, Alpha waves, and Beta waves), which helps analyze different states of the brain.

[0085] Detrending is a technique in data preprocessing that aims to remove long-term trend components in time series data, thereby making the data more stable and highlighting periodic or short-term fluctuations. This process usually involves identifying and removing trend components through some method (such as linear regression or moving average), making the remaining data more conducive to subsequent analysis.

[0086] Artifact removal is the act of removing artifacts from EEG signals, such as interference caused by eye movement (EOG), heartbeat (ECG) or electromyography (EMG). In the specific implementation process, according to the statistical characteristics of the signal, the threshold of the artifact is set by interpolating or setting the artifact segment to zero to remove the brain wave data signal greater than the artifact threshold (specifically the voltage amplitude) and obtain the pre-processed sleep characteristic parameters.

[0087] Although the present application is disclosed as above with preferred embodiments, it is not intended to limit the claims. Any technical personnel in this field may make several possible changes and modifications without departing from the concept of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

[0088] The present invention is further described in detail below in conjunction with specific embodiments, but the embodiments of the present invention are not limited thereto.

[0089] Example 1

[0090] As we all know, the current brainwave audio sleep-aiding technology can help users fall asleep to a certain extent. However, this technology has several obvious defects. First, many insomnia patients often wake up after a period of deep sleep, thus losing their deep sleep state, which leads to a decrease in sleep quality. In this case, existing brainwave audio sleep-aiding devices usually stop working, and users need to manually restart the device. This process interferes with the sleep cycle, thereby increasing the user's fatigue and anxiety. Secondly, many brainwave audio sleep-aiding devices require users to perform manual operations and lack automatic adjustment functions, which prevents users from obtaining continuous audio assistance, ultimately affecting the stability of the sleep-aiding effect.

[0091] In order to solve the above technical defects, Figure 1-2 As shown, the present application improves the existing brain wave audio sleep aid method. Specifically, the steps of the brain wave audio sleep aid method of the present application are as follows:

[0092] S101, detecting the user's brain wave signal according to the brain wave detection module to obtain a brain wave data signal, preprocessing the brain wave data signal through the preprocessing module to obtain sleep characteristic parameters and transmitting the sleep characteristic parameters to the control module;

[0093] S201, the control module determines the sleep state of the user according to the sleep characteristic parameters, and outputs audio parameters matching the sleep state to the audio player according to the sleep state, and the audio parameters are used to drive the audio player to output binaural beat audio corresponding to the audio parameters;

[0094] S301, when the sleep characteristic parameter is less than or equal to the deep sleep threshold parameter, the control module drives the audio player to stop working, and at the same time drives the timing unit of the control module to start timing;

[0095] S401, when the timing parameter of the timing unit is the same as the first time threshold received by the control module through the control terminal or the second time threshold stored in the storage unit of the control module, the control module drives the brain wave detection module to re-collect the sleep characteristic parameter of the user and determine whether it is less than or equal to the deep sleep threshold parameter;

[0096] When the re-collected sleep characteristic parameter of the user is greater than the deep sleep threshold parameter, the process re-enters step S201.

[0097] Specifically, the working principle of the above-mentioned brain wave audio sleep-aiding method is to monitor the user's brain wave activity in real time through the brain wave detection module. When it is detected that the user enters a deep sleep state, the control module will automatically stop the audio playback to avoid disturbing the user's sleep cycle. At the same time, the timing unit starts timing, and when the preset time threshold is reached, the brain wave detection module starts again to re-evaluate the user's sleep state. If the user is still in a deep sleep state, the system will restart the audio playback to ensure that the user can continue to enjoy high-quality sleep. If the user has left the deep sleep state, the system will select appropriate audio parameters according to the user's current sleep state, continue to provide sleep-aiding audio, and help the user enter deep sleep again. In this way, the brain wave audio sleep-aiding method of the present invention can intelligently adapt to the user's sleep pattern and provide a more personalized and effective sleep-aiding service.

[0098] In the above-mentioned brain wave audio sleep aid method, the brain wave audio sleep aid method of the present application can realize real-time monitoring and automatic adjustment of the user's sleep state, thereby providing a more personalized and continuous sleep aid experience. Specifically, when the user enters a deep sleep state (specifically less than or equal to the deep sleep threshold parameter), the system will automatically stop the audio playback to avoid interfering with the user's sleep cycle. At the same time, by setting a timing unit, the system can timely re-detect the user's brain wave signal during the user's sleep, ensuring that when the user wakes up from a deep sleep, the sleep aid audio can be promptly re-provided to help the user enter a deep sleep state again. In addition, the brain wave audio sleep aid method of the present application can also automatically adjust the audio parameters according to the user's sleep characteristic parameters to match the user's sleep state, thereby providing a more accurate sleep aid effect. In this way, the brain wave audio sleep aid method of the present application not only improves the efficiency of sleep aid, but also further improves the user's sleep quality.

[0099] Furthermore, in the above method, the sleep characteristic parameter value is a frequency value, and the audio parameter is a binaural playback frequency difference. Among them, the setting of the binaural playback frequency difference is based on the binaural beat effect (Binaural Beat Effect), which means that when two sounds of different frequencies are transmitted to the left and right ears respectively, the brain will perceive a virtual tone with a frequency equal to the difference between the two sound frequencies. By adjusting this frequency difference, the brain's brain wave activity can be affected, thereby promoting the user to enter a specific sleep stage. For example, when the binaural playback frequency difference is set to 10Hz, it can promote the user to enter the Theta wave state (at this time the user's brain wave signal is in the Alpha wave state), which helps to relax and relieve stress; when the difference is set to 4Hz, it helps the user enter the Delta wave state.

[0100] Furthermore, the deep sleep threshold parameter is 3Hz. In this application, it is considered that in existing studies, when the sleep characteristic parameter is less than or equal to 3Hz, the brain wave signal also meets the frequency band of the Theta wave, proving that the user has entered a deep sleep state and no longer needs further brain wave sleep aid, and the audio playback can be stopped. The control module ensures that the user will not be disturbed by unnecessary audio stimulation during deep sleep through preset logical judgment, thereby protecting the user's sleep cycle from interference.

[0101] Furthermore, as a further improvement to the brainwave audio sleep aid method of the present application, the sleep state is divided into a first sleep state, a second sleep state and a third sleep state;

[0102] The value range of the sleep characteristic parameters corresponding to the first sleep state is: 8-12Hz;

[0103] The value range of the sleep characteristic parameters corresponding to the second sleep state is: 4-8Hz;

[0104] The value range of the sleep characteristic parameter corresponding to the third sleep state is: 3-4Hz.

[0105] The first sleep state corresponds to the Alpha wave of the user's brain wave. At this time, it is judged that the user is in a relaxed state and is suitable for meditation or light relaxation activities. The second sleep state corresponds to the Theta wave of the user's brain wave, indicating that the user is in a deeper state of relaxation and is suitable for deep relaxation or sleep induction. The third sleep state corresponds to the Delta wave of the user's brain wave. At this time, the user is in a deep sleep state, and brain wave activity slows down. It is a critical period for restoring physical strength and repairing the body. By identifying these different sleep states and providing corresponding audio stimulation, the brain wave audio sleep aid method of the present invention can more effectively help users achieve ideal sleep quality. It should be noted that although the third sleep state corresponds to the Delta wave of the user's brain wave signal, it does not represent the entire frequency range of the Delta wave. In this application, the frequency range of the Delta wave is defined as 0.5 Hz to 4 Hz, of which the frequency range of 3 Hz to 4 Hz is the transition range set in this application. On the one hand, it is to stabilize the user's smooth transition from the second sleep state to the third sleep state. On the other hand, it is also to ensure that the user reaches a stable deep sleep state during deep sleep (the specific judgment standard is whether the brain wave data signal reaches 3 Hz), and then the audio player stops working to prevent the user from quickly losing the deep sleep state. In the third sleep state, the system will adjust the audio parameters according to the user's brain wave activity to ensure that the audio output matches the user's sleep depth. For example, when the user enters the third sleep state, the audio player will output audio that matches the Delta wave frequency range to help the user maintain a deep sleep state while avoiding excessive audio stimulation that affects sleep quality.

[0106] In summary, the method of the present application can effectively guide the user into an ideal sleep state by monitoring the user's brain wave activity in real time and dynamically adjusting the binaural playback frequency difference according to the user's sleep characteristic parameters. For example, when the user is in a light sleep state, the system can automatically adjust the binaural playback frequency difference to a frequency close to the Theta wave, helping the user to gradually transition to a deep sleep state (Delta wave). In addition, the deep sleep threshold parameter is set to ensure that audio playback is stopped when the user is in deep sleep to avoid disturbing the user's natural sleep cycle. When the user wakes up from deep sleep, the system will re-evaluate the user's sleep state and adjust the audio parameters as needed to help the user enter deep sleep again.

[0107] Example 2

[0108] like Figure 3 As shown, unlike Example 1, this application is further optimized to achieve real-time adjustment of binaural beat audio and user status. During the adjustment process, the control module can dynamically adjust the audio parameters of different frequencies according to the various fluctuations in the user's sleep state, thereby achieving a more accurate sleep-aiding effect. This method not only effectively matches the user's brain wave characteristics, but also guides the user to fall asleep quickly and maintain deep sleep by adjusting the frequency difference of the binaural audio.

[0109] Specifically, the technical solution for implementing the above dynamic adjustment is a further improvement of step S201 in Example 1, and the specific steps are as follows:

[0110] S202: When the user's sleep state satisfies the first sleep state, and the sleep characteristic parameter increases or decreases by a first difference, the audio parameter increases or decreases by a first change value; wherein the sleep characteristic parameter still satisfies the first sleep state after increasing or decreasing;

[0111] S203, when the user's sleep state satisfies the second state, and the sleep characteristic parameter increases or decreases by a second difference, the audio parameter increases or decreases by a second change value; wherein the sleep characteristic parameter still satisfies the second sleep state after increasing or decreasing;

[0112] S204: When the user's sleep state satisfies the second state and the increase or decrease amount of the sleep characteristic parameter satisfies the third difference value, the audio parameter increases or decreases by a third change value; wherein the sleep characteristic parameter still satisfies the third sleep state after increasing or decreasing.

[0113] Specifically, when the user enters the first sleep state, the second sleep state, and the third sleep state, the brain wave signal usually increases or decreases slightly in a specific state, and it is precisely because of such small changes that the binaural beat frequency changes the audio that changes accordingly, causing the user to feel uncomfortable, which has an adverse effect on the user entering the next sleep state or stabilizing the current sleep state. The binaural beat audio of the prior art only switches when the brain wave signal amplitude is too large to cross the sleep state, and does not solve the above technical problems found in this application.

[0114] Therefore, the present application can effectively avoid this discomfort through real-time monitoring and dynamic adjustment, thereby improving the sleep-aiding effect. For example, in the first sleep state, if the user's brain wave signal increases or decreases slightly, the system will adjust the audio parameters accordingly to maintain the user's relaxation and meditation state in the Alpha wave state. In the second sleep state, the system will also adjust the audio parameters according to slight changes in the brain wave signal to help the user maintain deep relaxation in the Theta wave state. In the third sleep state, the system will pay special attention to the stability of the brain wave signal to ensure that the user can maintain deep sleep in the Delta wave state, while avoiding the impact of excessive audio stimulation on sleep quality. Through this fine adjustment, the brain wave audio sleep-aiding method of the present invention can better adapt to the individual differences of users and provide a personalized sleep-aiding experience.

[0115] Furthermore, the first difference is 1Hz, and the first change value is 0.5Hz; the second difference is 1Hz, and the first change value is 0.5Hz; the third difference is 0.25Hz, and the third change value is 0.25Hz. The setting of the above parameters is based on extensive research and experiments to ensure that the audio stimulation is synchronized with the user's brain wave activity to achieve the best sleep-aiding effect. In actual applications, these parameters can be fine-tuned based on user feedback and sleep quality to achieve more personalized sleep assistance. For some users, the frequency difference of audio stimulation needs to be slightly reduced to help them enter a deep sleep state more easily.

[0116] The other details that are the same as those in Example 1 are not described in detail in this example.

[0117] Example 3

[0118] like Figure 1-3 As shown, the difference from Example 1 is that in order to further improve the preprocessing of the brain wave signal of the present application and improve the accuracy of the sleep characteristic parameters. Specifically, the preprocessing module of the present application divides the brain wave data signal transmitted by the brain wave detection module into different time windows, and performs bandpass filtering, detrending and de-artifacting processing on the brain wave signal in each time window in turn.

[0119] Furthermore, the bandpass filtering process is to remove low-frequency drift and high-frequency noise in the EEG signal and retain a specific brain wave frequency band (usually between 0.5 Hz and 30 Hz); in the bandpass filtering process of the present application, the low-frequency cutoff frequency is 0.5 Hz and the high-frequency cutoff frequency is 30 Hz;

[0120] Furthermore, the essence of detrending is to eliminate the linear trend and low-frequency drift in the brain wave data signal to avoid adverse effects on the analysis results. The brain wave data signal after detrending is obtained by calculating the mean value of the brain wave data signal in the time window and removing the mean value of the brain wave data signal calculated by the mean value in the time window. In the specific implementation process, the mean value is subtracted from the signal to obtain the method of obtaining the brain wave data signal after detrending. The mathematical expression is:

[0121]

[0122] Among them, X detrended (t) is the brain wave data signal after detrending in a single time window, X(t) is the brain wave data signal collected in a single time window or the brain wave data signal after bandpass filtering, It is the mean value of the brain wave data signal within a single time window.

[0123] Furthermore, the mean calculation formula is:

[0124]

[0125] Where N is the total number of brain wave data signals in a single time window; X i It is the voltage value sampled at the ith brain wave data signal within a single time window.

[0126] Furthermore, in the artifact removal process, the sleep characteristic parameters are obtained by removing the brain wave data signals within the time window that are greater than the artifact threshold; wherein the artifact identification formula is:

[0127] |X i |>T(determine the artifact segment)

[0128] Among them, |X i | is the absolute value of the amplitude of the brain wave data signal, and the artifact threshold T is the voltage amplitude threshold. At the same time, based on the identification of the artifact, the artifact segment is removed by interpolating or setting it to zero, and its mathematical expression is:

[0129]

[0130] Among them, Xclean(i) is the sleep characteristic parameter that has been processed by bandpass filtering, detrending and artifact removal in sequence.

[0131] Furthermore, the calculation formula of the artifact threshold T is as follows:

[0132] T=μ+k·σ

[0133] Wherein, μ is the mean difference of the brain wave data signal in a single time window; σ is the standard deviation of the brain wave data signal in a single time window. In the specific implementation process, k is an adjustable parameter. The k threshold is usually between 2 and 3, and when the signal exceeds this threshold, it is regarded as an artifact. For example, if k=3k in the calculation formula of the above artifact threshold T, then the signal is considered an artifact only when it is higher than the mean plus 3 standard deviations.

[0134] In summary, through the above preprocessing steps, the brain wave characteristic parameters related to the sleep state can be effectively extracted.

[0135] The other details that are the same as those in Example 1 are not described in detail in this example.

[0136] Example 4

[0137] Figure 1-6 As shown, in order to further improve the auxiliary effect of the brain wave audio sleep aid device, the present application makes the following improvements to the existing brain wave audio sleep aid device. Specifically, the brain wave audio sleep aid device of the present application includes:

[0138] The brain wave detection module 1 includes at least one electrode 11 for collecting brain wave signals, and the electrode 11 is placed in the frontal lobe area of ​​the user's head; wherein the electrodes 11 are arranged in a plurality to ensure the comprehensiveness and accuracy of signal collection. The brain wave detection module 1 is connected to the control module 4 in a wireless or wired manner to transmit brain wave data signals in real time.

[0139] The preprocessing module 2 includes a filter 21 and a processing unit 22. The filter 21 is used to filter the brain wave data signal, and the processing unit 22 is used to detrend and remove artifacts on the filtered brain wave data signal to obtain sleep characteristic parameters; wherein the preprocessing module 2 works in conjunction with the brain wave detection module 1 to ensure high-quality acquisition and processing of brain wave signals.

[0140] Furthermore, the processing unit 22 identifies the user's sleep stage by analyzing the filtered brain wave data signal, and dynamically adjusts the audio output according to this information. The control module 4 receives the sleep characteristic parameters of the preprocessing module 2, and controls the audio generator to generate corresponding audio signals according to these parameters.

[0141] The audio player 3 is used to play binaural beat audio, including left earphones and right earphones of different channels; specifically, the audio generator is responsible for generating audio signals of specific frequencies, which are synchronized with the user's brain wave frequency to promote sleep. The audio player 3 is connected to the control module 4 wirelessly or wired to ensure the stable output of the audio signal. The control module 4 dynamically adjusts the frequency, volume and rhythm of the audio signal generated by the audio generator according to the sleep characteristic parameters provided by the preprocessing module 2 to adapt to the changes in the user's sleep state. In addition, the audio player 3 also has an environmental noise elimination function, which can automatically adjust the audio output according to the environmental noise level.

[0142] The control module 4 drives the working state of the audio player 3 using any of the brain wave audio sleep aid methods of Examples 1-3, including a storage unit 41 and a timing unit 42. The storage unit 41 is used to store the sleep characteristic parameters detected by the brain wave detection module 1, the audio parameters driving the audio player 3 to play binaural beats audio, and the second time threshold for determining whether the audio player 3 stops working; wherein the timing unit 42 is used to control the stop time of the audio player 3. When the timing data of the timing unit 42 reaches the first time threshold or the second time threshold, the user's brain wave signal is collected and processed again. The storage unit 41 also contains the user's personal sleep data, which helps the control module 4 to adjust the audio output more accurately through long-term monitoring and analysis to meet the user's personalized sleep needs. During the implementation of the spinous process, the storage unit 41 is an EPPROM structure.

[0143] Furthermore, the control module 4 also has an intelligent learning function, which can automatically optimize the audio stimulation parameters according to the user's feedback and sleep quality records, thereby providing more accurate sleep assistance. Through these improvements, the brainwave audio sleep aid device can provide users with more efficient and personalized sleep solutions.

[0144] The control module 4 is connected to the control terminal 5 via a communication network, and the control terminal 5 is used to transmit the first time threshold for determining whether the audio player 3 stops working. The control module 4 is connected to the control terminal 5 via a communication network, and the control terminal 5 is used to transmit the first time threshold for determining whether the audio player 3 stops working. The interactive processing between the user end and the device of the present application is realized through control, and the user can change and input the first time threshold according to his or her own body information and sleep conditions, thereby realizing personalized control of the sleep assistance process.

[0145] Furthermore, the control terminal 5 can be a smart phone, a tablet computer or a dedicated control device, through which the user can monitor his or her sleep state in real time and adjust the parameters of the sleep-aiding audio as needed. In addition, the control terminal 5 also has data recording and analysis functions, which can record the user's sleep data and display it to the user through a graphical interface to help the user better understand his or her sleep pattern and improve sleep quality. The communication between the control module 4 and the control terminal 5 can use Bluetooth, Wi-Fi or other wireless communication technologies to ensure the stability and real-time performance of signal transmission. Through these improvements, the brainwave audio sleep-aiding device can provide users with more efficient and personalized sleep solutions.

[0146] The control module is connected to the control terminal via a communication network, and the control terminal is used to transmit the first time threshold for determining whether the audio player stops working. The control module is connected to the control terminal via a communication network, and the control terminal is used to transmit the first time threshold for determining whether the audio player stops working. The interactive processing between the user end and the device of the present application is realized through control, and the user can change and input the first time threshold according to his or her own body information and sleep conditions, thereby realizing personalized control of the sleep assistance process.

[0147] Furthermore, the control terminal can be a smart phone, tablet computer or dedicated control device, through which the user can monitor his or her sleep status in real time and adjust the parameters of the sleep-aiding audio as needed. In addition, the control terminal also has data recording and analysis functions, which can record the user's sleep data and display it to the user through a graphical interface, helping the user to better understand his or her sleep pattern and improve sleep quality. The communication between the control module and the control terminal can use Bluetooth, Wi-Fi or other wireless communication technologies to ensure the stability and real-time performance of signal transmission. Through these improvements, the brainwave audio sleep-aiding device can provide users with more efficient and personalized sleep solutions.

[0148] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A brainwave audio sleep aid method, characterized in that: The steps include: S101, detecting the user's brain wave signal according to the brain wave detection module to obtain a brain wave data signal, preprocessing the brain wave data signal through the preprocessing module to obtain a sleep characteristic parameter and transmitting the sleep characteristic parameter to the control module; S201, the control module determines the sleep state of the user according to the sleep characteristic parameters, and outputs audio parameters matching the sleep state to an audio player according to the sleep state, wherein the audio parameters are used to drive the audio player to output binaural beat audio corresponding to the audio parameters; S301, when the sleep characteristic parameter is less than or equal to the deep sleep threshold parameter, the control module drives the audio player to stop working, and drives the timing unit of the control module to start timing; S401, when the timing parameter of the timing unit is the same as the first time threshold received by the control module through the control terminal or the second time threshold stored in the storage unit of the control module, the control module drives the brain wave detection module to re-collect the sleep characteristic parameter of the user and determine whether it is less than or equal to the deep sleep threshold parameter; When the re-collected sleep characteristic parameter of the user is greater than the deep sleep threshold parameter, the process re-enters step S201.

2. The brainwave audio sleep aid method according to claim 1, characterized in that: The sleep characteristic parameter value is a frequency value, the audio parameter is a binaural playback frequency difference; and the deep sleep threshold parameter is 3 Hz.

3. The brainwave audio sleep aid method according to claim 2, characterized in that: The sleep state is divided into a first sleep state, a second sleep state and a third sleep state; The value range of the sleep characteristic parameter corresponding to the first sleep state is: 8-12Hz; The value range of the sleep characteristic parameter corresponding to the second sleep state is: 4-8Hz; The value range of the sleep characteristic parameter corresponding to the third sleep state is: 3-4 Hz.

4. The brainwave audio sleep aid method according to claim 3, characterized in that: The audio parameters include first binaural audio parameters matching the first sleep state, second binaural audio parameters matching the second audio state, and third binaural audio parameters matching the third sleep state; The value range of the first binaural audio parameter is 8-10 Hz; The value range of the second binaural audio parameter is 4-6 Hz; The value range of the third binaural audio parameter is 0.5-4 Hz.

5. The brainwave audio sleep aid method according to claim 3, characterized in that: The step S201 further includes the following steps: S202: When the user's sleep state satisfies a first sleep state, and the increase or decrease amount of the sleep characteristic parameter satisfies a first difference, the audio parameter increases or decreases by a first change value; wherein the sleep characteristic parameter still satisfies the first sleep state after the increase or decrease; S203: When the user's sleep state satisfies the second state, and the increase or decrease amount of the sleep characteristic parameter satisfies the second difference value, the audio parameter increases or decreases by a second change value; wherein the sleep characteristic parameter still satisfies the second sleep state after the increase or decrease; S204: When the user's sleep state satisfies the second state and the increase or decrease amount of the sleep characteristic parameter satisfies the third difference value, the audio parameter increases or decreases by a third change value; wherein the sleep characteristic parameter still satisfies the third sleep state after increasing or decreasing.

6. The brainwave audio sleep aid method according to claim 5, characterized in that: The first difference is 1 Hz, and the first change is 0.5 Hz; the second difference is 1 Hz, and the first change is 0.5 Hz; the third difference is 0.25 Hz, and the third change is 0.25 Hz.

7. The brainwave audio sleep aid method according to claim 1, characterized in that: The preprocessing module divides the brain wave data signal transmitted by the brain wave detection module into different time windows, and performs bandpass filtering, detrending and de-artifacting processing on the brain wave data signal in each time window in turn.

8. The brainwave audio sleep aid method according to claim 7, characterized in that: During the bandpass filtering process, the low frequency cutoff frequency is 0.5 Hz and the high frequency cutoff frequency is 30 Hz; In the detrending process, the mean value of the brain wave data signal in the time window is calculated and the mean value of the brain wave data signal calculated by the mean value in the time window is removed to obtain the detrended brain wave data signal; The mean value calculation formula is: in, is the mean value of the brain wave data signal in the single time window; N is the total number of the brain wave data signals in the single time window; X i It is the voltage value of the i-th brain wave data signal sampled within a single time window.

9. The brainwave audio sleep aid method according to claim 1, characterized in that: In the artifact removal process, the sleep characteristic parameter is obtained by removing the brain wave data signals within the time window whose brain wave data signals are greater than the artifact threshold; The calculation formula of the artifact threshold T is as follows: T=μ+k·σ Among them, μ is the mean difference of the brain wave data signal in the single time window; k is an adjustable parameter; σ is the standard deviation of the brain wave data signal in the single time window; and the artifact threshold T is the voltage amplitude threshold.

10. A brain wave audio sleep aid device, characterized in that: include: A brain wave detection module, comprising at least one electrode for collecting brain wave signals, wherein the electrode is placed in the frontal lobe area of ​​the user's head; A preprocessing module, comprising a filter and a processing unit, wherein the filter is used to filter the brain wave data signal generated by the electrode collection, and the processing unit is used to perform detrending and de-artifacting processing on the filtered brain wave data signal to obtain sleep characteristic parameters; An audio player for playing binaural beat audio, including left and right earphones with different channels; A control module, which drives the working state of the audio player using any of the brain wave audio sleep aid methods of claims 1-9, including a storage unit and a timing unit, wherein the storage unit is used to store the sleep characteristic parameters detected by the brain wave detection module, the audio parameters for driving the audio player to play binaural beat audio, and a second time threshold for determining whether the audio player stops working; The control module is connected to a control terminal via a communication network, and the control terminal is used to transmit a first time threshold for determining whether the audio player stops working.

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