Neural feedback method and system based on adjustable binaural wave difference

Abstract

The invention relates to the technical field of electroencephalogram processing, in particular to a neural feedback method and system based on adjustable binaural wave difference, and the method comprises the steps: collecting brain waves of a user, and carrying out the user mental state classification of an original brain wave signal through a machine learning algorithm; after the mental state of the user is evaluated, different intervention modes are selected, and audios or videos and audios with binaural wave differences are given to a certain application scene in the different intervention modes; the user adjusts a wave difference parameter group through an intervention mode, and the mental state of the user is improved by matching with self-selected audios and / or videos; and according to the intervention mode selected by the user, performing acousto-optic module or video and audio, and intervening the mental state of the current user by outputting acousto-optic stimulation. According to the method, the mental state is regulated and controlled to the intervention mode, personalized requirements of users are met, the mental state is evaluated and regulated and controlled, the binaural wave difference can be adjusted by utilizing a regulation and control function, the method can be operated under different hardware conditions, and the problem that the effect of existing binaural waves is poor due to temporal performance is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electroencephalogram processing, and particularly relates to a neural feedback method and system based on adjustable binaural beat difference. BACKGROUND

[0002] With the gradual increase of life pressure of modern people due to high-intensity work and excessive socialization, anxiety, overwork, insomnia and other conditions occur, and if the mental state is not improved in time, it may have a long-term impact on the brain, such as memory loss, depression and the like; therefore, the mental state needs to be regulated to meet specific mental indicators, such as adjusting fatigue, tension, distraction state to a relaxed and focused state.

[0003] The regulation principle of the mental state comes from the corresponding frequency generated in the brain after the brain receives external stimulation through the sensory nerve, and the effect brought by the specific frequency can improve the mental indicators of the brain, thereby achieving the effect of brain closed loop.

[0004] An existing patent is an attention regulation method and system based on binaural beat brain wave audio, which emphasizes the realization of binaural beat hardware or the regulation of binaural beat concentration, and only audio control belongs to single control; when there is no corresponding regulation value when adjusting the preset mode in the database, there is a problem that regulation cannot be realized. SUMMARY

[0005] In view of the deficiencies of the existing method, the present application regulates the mental state to an intervention mode, meets the individual needs of users, evaluates and regulates the mental state, and realizes the adjustability of the binaural beat difference by using a regulation function, which can run under different hardware conditions and solve the problem that the existing binaural beat is time-dependent and has poor effect.

[0006] The technical scheme adopted by the present application is: a neural feedback method based on adjustable binaural beat difference, comprising the following steps:

[0007] Step 1: Collecting the brain waves of the user, using a machine learning algorithm to classify the original brain wave signals according to the mental state of the user;

[0008] As a preferred embodiment of the present application, the brain wave signal comprises: frequency domain features, time domain features and brain connectivity features.

[0009] Through the frequency domain, time domain and brain connectivity indicators, the mental state of the user is fully reflected.

[0010] Step 2: After evaluating the mental state of the user, different intervention modes are selected, and in different intervention modes, audio or audio-visual with binaural beat difference is given to a certain application scenario;

[0011] As a preferred embodiment of the present application, the intervention mode is divided into a built-in mode and a custom mode, and each intervention mode corresponds to at least one user mental state.

[0012] As a preferred embodiment of the present application, the built-in mode includes a wakefulness mode, a relaxation mode, a concentration mode, and an emotional state mode; wherein the formula of the concentration mode is Fz_theta / (avg(C3_SMR+C4_SMR)^2); the formula of the relaxation mode is Fz_theta / Pz_alpha; and the formula of the emotional state is log(C3_alpha / C4_alpha); wherein Fz is the frontal lobe, Pz is the parietal lobe region, C3 and C4 are the two sides of the parietal lobe, and SMR is self-myofascial release.

[0013] As a preferred embodiment of the present application, the built-in mode corresponds to at least one application scenario.

[0014] As a preferred embodiment of the present application, the application scenario includes an open space and a non-open space.

[0015] Step three, the user adjusts the wave difference parameter group through the intervention mode, and matches the self-selected audio and / or video to improve the user mental state;

[0016] As a preferred embodiment of the present application, the user uses a default frequency of a certain frequency domain feature, including a frequency range, a starting parameter, and an intensity parameter, to output the binaural wave difference value.

[0017] As a preferred embodiment of the present application, when the intervention mode is the custom mode, the binaural wave difference value is adjusted by using the control function: y=A*sin((W*t)+x)+N; wherein y is the final target wave difference value, A is the oscillation amplitude, W is the angular frequency, t is the time, x is the initial angle, and N is the frequency under the intervention mode.

[0018] Compared with the built-in mode, the custom mode conforms to the individualized setting and can be adjusted according to the personal situation.

[0019] Step four, according to the intervention mode selected by the user, a sound and light module or a video and audio is played to output sound and light stimulation to intervene in the current user's mental state.

[0020] As a preferred embodiment of the present application, the system adopting the neural feedback method based on the controllable binaural wave difference includes a brain wave instrument, a server, a data processor, a display, and an audio playing device; wherein the brain wave instrument is used to collect the electroencephalogram signal, the server is used for user mental state classification processing, intervention mode calculation, and matching the corresponding intervention mode and application scenario according to different user mental states.

[0021] The present application has the following advantages:

[0022] 1. By adjusting the difference between the two ears, it reduces the nerve fatigue of the brain caused by prolonged exposure to sound stimulation. Unlike the previous brain intervention effect formed by a specific difference, which weakens over time, it enables users to achieve specific effects more efficiently in specific scenarios and has more flexibility to make personalized adjustments to enhance the neural feedback effect.

[0023] 2. Unlike previous patents that attempted to use hardware processing to achieve binaural waveform output, emphasizing the circuit design or mechanism of the playback device, this system will use software processing methods to process audio. It is not limited to a specific player. As long as there is a terminal processor, it can be used with an audio player. At the same time, it can be used with a display as an auxiliary tool, so that the sound stimulation combined with the visual scene makes it easier for the user to get into the situation, thereby improving the intervention effect.

[0024] 3. The system processes the user's raw brainwave signals and provides a mental state assessment and pattern suggestions. After the assessment, the user can choose different intervention modes based on their personal preferences or choose the intervention mode suggested by the system. It is not limited to modes that meet specific mental indicators. Unlike most previous methods that focus on regulating attention mechanisms, this invention focuses more on how to intervene in different mental states to achieve the expected results. At the same time, it allows users to choose their own suggestions, preferences, and customized modes, thereby improving the user's flexibility and enabling users to use it more in accordance with their own situation, so as to achieve the goal of the system being effective and easy to use. Attached Figure Description

[0025] Figure 1 This is a flowchart of the neural feedback method based on adjustable binaural wave difference of the present invention;

[0026] Figure 2 It is a diagram illustrating the user's mental state, intervention mode, and application scenarios;

[0027] Figure 3 These are flowcharts illustrating the adjustment process of user mental state under different modes;

[0028] Figure 4 This is a system connection diagram;

[0029] Figure 5 This is a schematic diagram of the operation of the control function. Detailed Implementation

[0030] The present invention will be further described below with reference to the accompanying drawings and embodiments. The drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0031] like Figure 1As shown, a neural feedback method based on adjustable binaural wave difference includes the following steps:

[0032] Step one, collect the user's brain waves, use machine learning algorithm to classify the original brain wave signal of user's mental state;

[0033] Using wired or wireless brain wave instrument to collect the user's brain waves, after filtering the original brain wave signal, time domain, frequency domain and brain connectivity analysis, get the characteristic value of brain wave, through statistics and machine learning to classify the user's mental state; according to the mental state classification to evaluate the user's state.

[0034] The characteristic value of brain wave includes: frequency domain characteristics of delta wave, theta wave, alpha wave, SMR wave, low beta wave, middle beta wave, high beta wave and gamma wave; time domain characteristics of P100(P1), P300(P3), MMN, N100(N1), N200(N2); brain connectivity characteristics of GC, PLV, PLD.

[0035] Machine learning algorithm according to the characteristic value of brain wave will divide the user's mental state into fatigue, distraction, sleep, tension and other states.

[0036] Machine learning includes but not limited to supervised and unsupervised algorithm, such as K-means clustering, decision tree, logistic regression, etc.

[0037] Among them, Delta(δ) wave belongs to the slow wave of unconscious level;

[0038] Theta(θ) wave belongs to the slow wave of subconscious level, which contains memory, perception and emotion; influence degree, expectation, belief, behavior; Alpha(α) wave belongs to transition wave also known as bridge wave; SMR(sensory motor rhythm) wave also known as sensory motor frequency is 12-15Hz brain wave; Beta(β) wave also known as activity wave, which contains low beta wave, middle beta wave and high beta wave; Gamma(γ) wave and large-scale brain network activity and cognitive phenomenon.

[0039] Step two, after evaluating the user's mental state, select different intervention mode, in different intervention mode, give the audio or audio-visual with binaural wave difference to certain application scene;

[0040] The existing method for setting intervention mode is too single and is inferred according to a certain frequency; the present application sets the built-in mode in the intervention mode by combining the frequencies; wherein the formula of the concentration mode is Fz_theta / (avg(C3_SMR+C4_SMR)^2); the formula of the relaxation mode is Fz_theta / Pz_alpha; and the formula of the emotional state is log(C3_alpha / C4_alpha); wherein Fz is the frontal lobe, Pz is the parietal lobe area, C3 / C4 is the parietal lobe on both sides, and SMR is self-myofascial relaxation.

[0041] As shown in Figure 2 According to the classification of the user's mental state, the user intervention mode is suggested, and one mode can correspond to at least one state, a relaxation mode corresponds to a user who is tired or nervous, a concentration mode corresponds to a user who is over-relaxed, and a sober mode corresponds to a user who wants to sleep.

[0042] For example, if the user's mental state assessment is fatigue, the system will default to recommend a built-in mode or a user-defined mode as the regulation method; for example, in the user-defined mode, a user whose mental state assessment is nervous can also choose concentration, not limited to the relaxation mode recommended by the system.

[0043] The application scenarios include open space and non-open space; wherein the application scenario refers to the virtual space constructed by the video display and the audio player, and a visual space is set; the open space scenario includes natural jungle, lakeside cottage, night desert, and night starry sky; the non-open space includes the visual field of classroom, bedroom, living room, and dining room, so as to increase the user's sense of immersion and make the individual's feelings abstract from the current scene, which is beneficial to the increase of the intervention effect.

[0044] The application scenario includes at least one scene; for example, in the relaxation mode, there is relaxing audio and video; wherein the audio is the alpha wave automatically regulated by the mode, and the video is a relaxing picture such as the seaside and the mountain forest.

[0045] The user can select the application scenario corresponding to the different intervention mode after the assessment, and according to the mode selected by the user, an audio or a video and audio with adjustable binaural wave difference is given to an application scenario; wherein different application scenarios give relatively background music with adjustable binaural wave difference, without the need to add new audio, which is convenient and fast for the user to use.

[0046] Step three, the user adjusts the wave difference parameter group through the intervention mode, and selects the audio and / or video to improve the user's mental state;

[0047] As shown in Figure 3, the user can customize the mode to adjust the wave difference parameter set to match the selected audio and video to achieve adaptive improvement effect; in addition to the built-in mode easy for users to quickly use, the user can also load audio and video according to personal preferences and input personalized adjustable binaural wave difference parameter set; it should be noted that no matter what mode the final output of the audio and video will be according to the pre-set or personal input parameter set for periodic neural regulation.

[0048] The built-in mode wave difference parameter set includes: starting parameter, period parameter and intensity parameter, or specifies a specific wave band to use, such as intensity is selected, the system will not automatically regulate the parameter; for example, the user wants to use an alpha wave between 8-12Hz, the default starting parameter is 10Hz, and the intensity parameter is 1Hz, the adjusted binaural wave difference will output 10±1Hz.

[0049] From the principle of binaural wave difference, it can be understood that sending two pieces of music with specific wave difference can make the brain form corresponding wave difference frequency and then affect the brain, but long-term fixed frequency difference may make the effect worse, in order to keep the effect or strengthen, the self-defined mode uses the control function, the control function uses the sin control function: y=A*sin((W*t)+x)+N, where y is the final target wave difference value, A is the oscillation amplitude, W is the angular frequency, t is the time, x is the initial angle, and N is the frequency under the intervention mode; the user can adjust it, if not, the preset value will be substituted, the sin control function is as shown in Figure 5 , A=2, W=3.14 / 2, N=10, t is between 0 and 10, and x=-3.14.

[0050] If alpha wave is selected in the relaxation mode, the wave difference oscillation range is between 8-12hz, and how to adjust the wave difference within 10 seconds is explained by the wave difference parameter.

[0051] Step four, according to the user's selected intervention mode, the sound and light module or audio and video are output to intervene the current user's mental state through sound and light stimulation;

[0052] The system will configure the sound and light module according to the user's selected mode; the processed audio and video are output to intervene the current user's mental state, including: after the user selects the mode, the sound and light configuration can be performed; among them, the configuration includes that the user can import personal preference audio or video; also can browse through the system to the third party server or website, the system uses embedded browser to process the audio and video content of the third party server.

[0053] The audio is processed by frequency reduction and synchronization to achieve binaural synchronization, and the sound and picture are further presented in the scene at the same time, creating an immersive experience.

[0054] According to the mental state of the user, the audio or audio-visual with binaural wave difference is automatically regulated, and the mental state of the user is continuously improved to achieve the expected effect; the target wave difference value is used to change the wave difference parameter; for example, the system regulates within 5 seconds according to the intensity adjustment in the parameter and the mental state of the brain, the angular frequency W is the rising amplitude of the y value each time, and the angular frequency can also be regarded as the intensity; wherein the period is not specific, but is determined by the period parameter in the wave difference parameter.

[0055] The pre-prepared audio, the frequency-reduced and synchronized multiple frequency-modulated audio, according to the principle of binaural wave difference, the system outputs two pieces of music with frequency difference, and the time axis of the two pieces of music must be synchronized. It can be imagined that if the music speed of the left and right ears is different, the brain cannot form a piece of music with frequency difference, the output of the regulated audio is more convenient, and the sound and picture are presented in the scene at the same time, creating an immersive experience.

[0056] At the same time, according to the mental state of the user, the audio or audio-visual with binaural wave difference is automatically regulated, and the mental state of the user is continuously improved to achieve the expected effect; automatic regulation refers to evaluating the mental state of the user according to each period, using the intensity parameter in the wave difference parameter in the selected mode to modulate the wave difference value of the next period, so that each period can affect the frequency resonated by the brain within the range of the starting parameter; the concept of automatic regulation intervenes in function regulation at a specific time, and the automatic regulation period is 2 times of t, such as t is 5, the automatic regulation period is 10 seconds, such as 5 seconds without frequency difference, 5 seconds of function regulation, and if no setting is set, the preset value is used.

[0057] For example, Figure 4 A neurofeedback system based on adjustable binaural wave difference, comprising: an electroencephalograph, a server, a data processor, a display and an audio playback device; wherein the electroencephalograph is used for collecting electroencephalogram signals, the server is used for user mental state classification processing, intervention mode calculation, and matching corresponding intervention mode and application scenario according to different user mental state.

[0058] The electroencephalograph is an electroencephalogram signal acquisition device, the data processor outputs external devices such as audio playback devices and displays, and the electroencephalogram signal acquisition device is in communication connection with the server.

[0059] The electroencephalogram signal acquisition device includes single or double, wired or wireless electroencephalograph; the server includes local server, cloud server, third-party server and third-party website; the data processor and the display include: mixed reality helmet with processor, personal computer host, personal tablet, mobile laptop, mobile phone, smart watch with processor; the audio playback device includes multi-channel speaker, double-channel speaker, Bluetooth speaker and wearable speaker.

[0060] With the above ideal embodiments according to the present application as the inspiration, through the above description, relevant staff can make various changes and modifications without deviating from the technical idea of the present application. The technical scope of the present application is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims

1. A neural feedback method based on adjustable binaural wave difference, characterized in that, It comprises the following steps: Step one, collect the user's brain waves, use machine learning algorithm to classify the original brain wave signal of user's mental state; Step two, select different intervention modes after user mental state evaluation, in different intervention modes, some application scenarios will be given audio or audio-visual with binaural wave difference; Step three, the user adjusts the wave difference parameter group through the intervention mode, and selects the audio and / or video to improve the user's mental state; Step four, according to the user's selected intervention mode, sound and light module or audio-visual, through the output of sound and light stimulation to intervene the current user's mental state.

2. The controllable binaural wave difference based neurofeedback method of claim 1, wherein, The intervention mode is divided into built-in mode and custom mode, and each intervention mode corresponds to at least one user mental state.

3. The controllable binaural wave difference based neurofeedback method of claim 1, wherein, When the intervention mode is built-in mode, the user uses the default frequency of a certain frequency domain feature, including frequency range, starting parameter and intensity parameter, to output binaural wave difference value.

4. The controllable binaural wave difference based neurofeedback method of claim 1, wherein, When the intervention mode is custom mode, use the control function: y=A*sin((W*t)+x)+N to adjust the binaural wave difference value; Wherein, y is the final target wave difference value, A is the amplitude of the oscillation wave, W is the angular frequency, t is the time, x is the initial angle, and N is the frequency under the intervention mode.

5. The controllable binaural wave difference based neural feedback method of claim 2, wherein, The built-in mode includes: wakefulness mode, relaxation mode, concentration mode, and emotional state mode.

6. The controllable binaural wave difference based neural feedback method of claim 5, wherein, The formula of relaxation mode is: Fz_theta / Pz_alpha; The formula of emotional state is: log(C3_alpha / C4_alpha); Wherein, Fz is frontal lobe, Pz is parietal lobe area, C3 / C4 is parietal lobe, and SMR is self myofascial release.

7. The controllable binaural wave difference based neural feedback method of claim 5, wherein, The built-in mode corresponds to at least one application scenario.

8. The controllable binaural wave difference based neural feedback method of claim 7, wherein, The application scenarios include open space and non-open space.

9. The controllable binaural wave difference based neurofeedback method of claim 1, wherein, The brain wave signal includes: frequency domain feature, time domain feature and brain connectivity feature.

10. A system employing a neural feedback method based on adjustable binaural wave disparity, comprising: Brain wave instrument, server, data processor, display and audio playing device; Wherein, the brain wave instrument is used to collect the brain electrical signal, the server is used to classify the user's mental state, process, calculate the intervention mode, and match the corresponding intervention mode and application scenario according to different user's mental state.

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