阅读说明：本技术 一种基于正念冥想的神经反馈干预系统及方法 (Neural feedback intervention system and method based on memorial meditation ) 是由 胡斌 蔡涵书 张健 于 2021-10-12 设计创作，主要内容包括：本申请提供一种基于正念冥想的神经反馈干预系统及方法,所述系统包括脑电信号采集模块、脑电信号预处理模块、脑电信号特征提取模块、归一化神经反馈指标计算模块和归一化神经反馈指标与听觉神经反馈映射模块；其中,脑电信号采集模块包括便携式三导脑电信号传感器以及脑电信号接收子模块；本申请通过便携式三导脑电信号传感器收集脑电信号并计算第一情绪状态值,通过神经反馈指标归一化模型得到第二情绪状态值以降低脑电信号个体差异所造成的负面影响,从而将归一化神经反馈指标映射为基于自然声音的听觉神经反馈实现实时给予训练者反馈的目的,进而改善正念冥想训练效果。(The application provides a neural feedback intervention system and method based on the memorial meditation, wherein the system comprises an electroencephalogram signal acquisition module, an electroencephalogram signal preprocessing module, an electroencephalogram signal characteristic extraction module, a normalized neural feedback index calculation module and a normalized neural feedback index and auditory neural feedback mapping module; the electroencephalogram signal acquisition module comprises a portable three-lead electroencephalogram signal sensor and an electroencephalogram signal receiving submodule; according to the method, the electroencephalogram signals are collected through the portable three-lead electroencephalogram signal sensor, the first emotion state value is calculated, the second emotion state value is obtained through the nerve feedback index normalization model so as to reduce negative effects caused by individual differences of the electroencephalogram signals, and therefore the purpose of giving feedback to a trainer in real time is achieved by mapping the normalization nerve feedback index into auditory nerve feedback based on natural sound, and the training effect of the memorial meditation is improved.)

1. A positive meditation-based neurofeedback intervention system, comprising:

the device comprises an electroencephalogram signal acquisition module, an electroencephalogram signal preprocessing module, an electroencephalogram signal feature extraction module, a normalized nerve feedback index calculation module and a normalized nerve feedback index and auditory nerve feedback mapping module;

the electroencephalogram signal acquisition module comprises a sensor and an electroencephalogram signal receiving submodule, and the sensor is connected with the electroencephalogram signal receiving submodule through Bluetooth;

the electroencephalogram signal acquisition module is configured to acquire electroencephalogram signals of a user and send the acquired electroencephalogram signals to the electroencephalogram signal preprocessing module; the electroencephalogram signal acquisition module is wirelessly connected with the electroencephalogram signal preprocessing module;

the electroencephalogram signal preprocessing module is configured to receive the electroencephalogram signals, perform denoising processing on the electroencephalogram signals to obtain processed first electroencephalogram signals, and send the first electroencephalogram signals to the electroencephalogram signal feature extraction module;

the electroencephalogram signal feature extraction module is configured to perform feature extraction on the received first electroencephalogram signal to obtain linear and nonlinear features about a plurality of wave bands, calculate a first emotional state value according to the linear and nonlinear features of different wave bands, and send the first emotional state value to the normalization nerve feedback index calculation module;

the normalized nerve feedback index calculation module is configured to substitute the first emotional state value into a normalization model, scale the first emotional state value by a normalization operation to obtain a second emotional state value, and send the second emotional state value to the normalized nerve feedback index and auditory nerve feedback mapping module;

the normalized nerve feedback index and auditory nerve feedback mapping module is configured to perform sound mapping on the second emotion state value through a natural sound volume mapping model and a natural sound type mapping model to obtain a mapped first audio, and send the first audio to a user.

2. The system for neural feedback intervention based on memorial meditation according to claim 1, wherein the sensor is a portable three-lead brain electrical signal sensor, the brain electrical signal of the user is acquired through three lead potentials of Fp1, Fp2 and Fpz which are placed in a forehead brain area, and the acquired brain electrical signal is transmitted to a brain electrical signal receiving submodule through Bluetooth; the sensor takes A1 or A2 as a reference potential, adopts a medical semi-wet electrode as a conducting medium, and is connected with a user through an ear clip electrode.

3. The system according to claim 1, further comprising an electroencephalogram signal storage module configured to store electroencephalogram signals generated during the training of the meditation and processed data thereof;

the electroencephalogram signal storage module is respectively connected with the electroencephalogram signal acquisition module, the electroencephalogram signal preprocessing module, the electroencephalogram signal feature extraction module, the normalized nerve feedback index calculation module and the normalized nerve feedback index and auditory nerve feedback mapping module; the electroencephalogram signal storage module is divided into four sub-modules: an electroencephalogram signal buffer zone submodule, an electroencephalogram signal data file submodule, an electroencephalogram signal characteristic buffer zone submodule and a normalized nerve feedback index buffer zone submodule; different sub-modules respectively store the EEG signals corresponding to the buffer zones and the processed data of the EEG signals.

4. The system according to claim 1, further comprising a feedback stop control module connected to the electroencephalogram signal acquisition module, the normalized neurofeedback index and auditory neurofeedback mapping module, respectively; the feedback stop control module comprises a monitoring submodule and a feedback cut-off submodule;

the feedback stop control module is configured to acquire a second electroencephalogram signal and judge whether an intervention ending condition is met; if the condition for ending intervention is not met, sending an instruction to the electroencephalogram signal acquisition module to continue the next round of neural feedback intervention;

stopping the neural feedback intervention if the intervention ending condition is met; the second electroencephalogram signal is the electroencephalogram signal after the user listens to the first audio.

5. A method for neural feedback intervention based on memorial meditation, comprising:

collecting electroencephalogram signals of a user, and storing the collected electroencephalogram signals; the electroencephalogram signals are stored in an electroencephalogram signal buffer area;

reading an electroencephalogram signal of the electroencephalogram signal buffer area, and performing denoising processing to obtain a first electroencephalogram signal after denoising processing;

performing feature extraction on the first electroencephalogram signal to obtain linear and nonlinear features of the first electroencephalogram signal about a plurality of wave bands;

calculating a first emotional state value according to the linear and nonlinear characteristics;

substituting the first emotional state value into a normalization model, and scaling the first emotional state value according to a proportion through normalization operation to obtain a second emotional state value;

performing sound mapping on the second emotion state value through a natural sound volume mapping model and a natural sound type mapping model to obtain a mapped first audio, and sending the first audio to a user;

collecting a second electroencephalogram signal, and judging whether an intervention ending condition is met; the intervention ending condition comprises a first intervention condition and a second intervention condition; the second electroencephalogram signal is an electroencephalogram signal obtained after a user listens to a first audio frequency;

and if the condition for ending the intervention is not met, acquiring the current electroencephalogram signal of the user and continuing the next round of neural feedback intervention.

6. The method for neural feedback intervention based on the memorial meditation according to claim 5, wherein the step of reading the electroencephalogram signal of the electroencephalogram signal buffer zone and performing denoising processing to obtain a first electroencephalogram signal after denoising processing comprises:

and performing electromyographic noise, ocular electrical noise, power frequency interference and baseline drift noise removal operation on the original electroencephalogram signal to obtain a preprocessed first electroencephalogram signal.

7. The method of normative meditation-based neurofeedback intervention according to claim 6, wherein the step of performing feature extraction on the first brain electrical signal to obtain linear and non-linear features of the first brain electrical signal with respect to a plurality of bands comprises:

and calculating linear characteristics of Alpha, Beta, Delta, Theta and Gamma wave bands of the first electroencephalogram signal by a frequency domain analysis method, and calculating nonlinear characteristics of the Alpha, Beta, Delta, Theta and Gamma wave bands by a nonlinear dynamics method.

8. The method for normative meditation-based neurofeedback intervention according to claim 7, wherein the substitution of the first emotional state value into the normalization model in the step of normalizing the model is:

M＝{m₁，m₂，...，m_i,. } is a time series of first emotional state values, wherein m_iA first emotional state value representing the ith second, a sliding window of 20 data points in length, M, being superimposed on the time series_maxIs the maximum value of the first emotional state value in the current sliding window, M_minIs the minimum value of the first emotional state value in the current sliding window, M_meanIs the average value of the first emotional state value in the current sliding window, then n_iA second emotional state value of i second, and n_i∈[-1，1]，n_i∈R。

9. The method of normative meditation-based neurofeedback intervention according to claim 8, wherein the natural sound volume mapping model is:

OutputVolume＝W*SystemVolume

Volume＝-0.5*N+0.5；

wherein, OutputVolume is the Volume finally received by the user, systemlvolume is the Volume of the current device, W is the weight adjusting parameter, dBStep is the decibel adjusting step length, Volume is the normalized neuro feedback index mapping function, and N is the normalized neuro feedback index time sequence.

10. The method of normative meditation-based neurofeedback intervention of claim 9, wherein the step of acquiring a second electroencephalogram signal and determining whether the end-of-intervention condition is satisfied further comprises:

acquiring a second emotion state value of the user, judging whether the second emotion state value is within a preset threshold interval, and if so, meeting a first intervention condition;

judging whether the training time of the user reaches the preset training time or not, and if so, meeting a second intervention condition;

and when the first intervention condition and the second intervention condition are simultaneously met, judging that the intervention ending condition is met, and stopping the neural feedback intervention.

Technical Field

The application relates to the technical field of medical auxiliary intervention systems, in particular to a neural feedback intervention system and method based on the memorial meditation.

Background

The electroencephalogram signals have wide application in the fields of emotion recognition, emotion calculation, medical auxiliary intervention and the like, and recognition and intervention aiming at mental disorders are one of the main application fields of the electroencephalogram signals. Research shows that the electroencephalogram signals have strong relevance with symptoms such as pressure, anxiety, depression and the like, so that the electroencephalogram signals are mostly adopted as quantitative indexes when intervention is carried out on mental disorders.

Electroencephalogram signal indexes (nerve feedback indexes) obtained by preprocessing and feature extraction of an existing nerve feedback system are generally used as means for quantifying certain mental disorders, such as forehead asymmetry, power ratios of different wave bands and the like. However, the existing neurofeedback system obtains the electroencephalogram signal subjected to noise reduction preprocessing for feature extraction, obtains the neurofeedback index without considering the individual difference of the electroencephalogram signal, does not perform any processing on the neurofeedback index, and the mapping feedback form of the neurofeedback system cannot meet the differentiation requirements of different crowds, so that different crowds cannot be accurately met due to the existence of the individual difference, and the neurofeedback effect is poor.

The positive meditation is a pressure intervention means, and the existing positive meditation systems do not usually have any form of feedback; there is a pause interval in the whole process of the training of the memorial meditation, and the user experiences no good experience in the period because of no feedback in any form, thereby reducing the training effect of the memorial meditation.

Disclosure of Invention

The application provides a neural feedback intervention system and method based on the memorial meditation, which aim to solve the problems that the existing neural feedback does not consider the individual difference of electroencephalogram signals, the memorial meditation training lacks feedback to a trainer, and the memorial meditation has pause intervals.

In one aspect, the present application provides a positive meditation-based neurofeedback intervention system, comprising:

the device comprises an electroencephalogram signal acquisition module, an electroencephalogram signal preprocessing module, an electroencephalogram signal feature extraction module, a normalized nerve feedback index calculation module and a normalized nerve feedback index and auditory nerve feedback mapping module;

the electroencephalogram signal acquisition module comprises a sensor and an electroencephalogram signal receiving submodule, and the sensor is connected with the electroencephalogram signal receiving submodule through Bluetooth;

the electroencephalogram signal acquisition module is configured to acquire electroencephalogram signals of a user and send the acquired electroencephalogram signals to the electroencephalogram signal preprocessing module; the electroencephalogram signal acquisition module is wirelessly connected with the electroencephalogram signal preprocessing module;

the electroencephalogram signal preprocessing module is configured to receive the electroencephalogram signals, perform denoising processing on the electroencephalogram signals to obtain processed first electroencephalogram signals, and send the first electroencephalogram signals to the electroencephalogram signal feature extraction module;

the electroencephalogram signal feature extraction module is configured to perform feature extraction on the received first electroencephalogram signal to obtain linear and nonlinear features about a plurality of wave bands, calculate a first emotional state value according to the linear and nonlinear features of different wave bands, and send the first emotional state value to the normalization nerve feedback index calculation module;

the normalized nerve feedback index calculation module is configured to substitute the first emotional state value into a normalization model, scale the first emotional state value by a normalization operation to obtain a second emotional state value, and send the second emotional state value to the normalized nerve feedback index and auditory nerve feedback mapping module;

the normalized nerve feedback index and auditory nerve feedback mapping module is configured to perform sound mapping on the second emotion state value through a natural sound volume mapping model and a natural sound type mapping model to obtain a mapped first audio, and send the first audio to a user.

In another aspect, the present application provides a method for positive meditation-based neurofeedback intervention, comprising:

collecting electroencephalogram signals of a user, and storing the collected electroencephalogram signals; the electroencephalogram signals are stored in an electroencephalogram signal buffer area;

reading an electroencephalogram signal of the electroencephalogram signal buffer area, and performing denoising processing to obtain a first electroencephalogram signal after denoising processing;

performing feature extraction on the first electroencephalogram signal to obtain linear and nonlinear features of the first electroencephalogram signal about a plurality of wave bands;

calculating a first emotional state value according to the linear and nonlinear characteristics;

substituting the first emotional state value into a normalization model, and scaling the first emotional state value according to a proportion through normalization operation to obtain a second emotional state value;

performing sound mapping on the second emotion state value through a natural sound volume mapping model and a natural sound type mapping model to obtain a mapped first audio, and sending the first audio to a user;

collecting a second electroencephalogram signal, and judging whether an intervention ending condition is met; the intervention ending condition comprises a first intervention condition and a second intervention condition; the second electroencephalogram signal is an electroencephalogram signal obtained after a user listens to a first audio frequency;

and if the condition for ending the intervention is not met, acquiring the current electroencephalogram signal of the user and continuing the next round of neural feedback intervention.

According to the technical content, the application provides a neural feedback intervention system and method based on the memorial meditation, wherein the system comprises an electroencephalogram signal acquisition module, an electroencephalogram signal preprocessing module, an electroencephalogram signal characteristic extraction module, a normalized neural feedback index calculation module and a normalized neural feedback index and auditory neural feedback mapping module; the electroencephalogram signal acquisition module comprises a portable three-lead electroencephalogram signal sensor and an electroencephalogram signal receiving submodule; according to the method, the electroencephalogram signals are collected through the portable three-lead electroencephalogram signal sensor, the first emotion state value is calculated, the second emotion state value is obtained through the nerve feedback index normalization model so as to reduce negative effects caused by individual differences of the electroencephalogram signals, and therefore the purpose that the second emotion state value is fed back to a trainer in real time for auditory nerve feedback based on natural sound is achieved, and the training effect of the memorial meditation is improved.

The beneficial effect of this application does:

firstly, the neural feedback intervention system based on the memorial meditation adopts the portable three-lead electroencephalogram signal sensor to collect electroencephalograms of a user, the equipment adopts forehead three-lead to calculate a first emotional state value (neural feedback index), compared with a traditional electrode cap, the data processing amount is greatly reduced, and meanwhile, the calculation integrity of the first emotional state value can be guaranteed. Compared with the data preprocessing by using software, the device has the advantages that the calculation efficiency is greatly improved, and the real-time requirement of neural feedback can be effectively met; the equipment adopts a Bluetooth protocol to transmit electroencephalogram signals, so that the portability of the equipment is improved; and the medical semi-wet electrode is used as a conducting medium, so that the operation complexity is reduced, and the signal transmission quality is improved.

Secondly, the neural feedback intervention system based on the memorial meditation fully considers the influence caused by the individual difference of the electroencephalogram signals, so that the neural feedback intervention system based on the memorial meditation can receive the first emotion state value through a uniform interface and perform mapping of a feedback form in a uniform form; the normalization processing is carried out through a normalization model arranged by a normalization nerve feedback index calculation module in the system, the model has strong calculation performance, normalization can be carried out in real time, and the performance requirement of nerve feedback can be met.

Finally, the normalized nerve feedback index and auditory nerve feedback mapping module in the nerve feedback intervention system based on the formal meditation compensates the defect that the training of the formal meditation is lack of feedback, and can give feedback to a trainer in real time in the training process of the formal meditation so that the trainer can perform self-regulation according to the received feedback to achieve the purpose of improving the training effect of the formal meditation; and the pause interval of the direct memory meditation training guide is filled, and auditory stimulation is still provided when the direct memory meditation training guide is kept silent, so that the occurrence probability of mind wandering is effectively reduced, and the probability of the reduction of the training effect caused by the passivity interruption of the training is reduced.

Drawings

In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a prior art neural feedback flow chart;

fig. 2 is a schematic diagram of an electroencephalogram signal acquisition potential and a reference potential provided in the embodiment of the present application;

FIG. 3 is a diagram of a portable three lead brain electrical signal sensor for use in the present application;

FIG. 4 is a schematic view of a sliding window in an embodiment of the present application;

FIG. 5 is a diagram of a neural feedback indicator normalized model mapping function in an embodiment of the present application;

FIG. 6 is a diagram illustrating a natural sound type mapping model according to an embodiment of the present invention;

figure 7 is a block diagram of the positive meditation-based neurofeedback intervention system provided by the present application.

Detailed Description

Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following examples do not represent all embodiments consistent with the present application. But merely as exemplifications of systems and methods consistent with certain aspects of the application, as recited in the claims.

Referring to fig. 1, a flow chart of neural feedback in the prior art is shown.

The existing nerve feedback does not consider the individual difference of electroencephalogram signals. Researches indicate that stronger individual difference exists in electroencephalogram signals, particularly in nerve feedback indexes, and the influence caused by the individual difference of different groups is fully considered when feedback form mapping is carried out. Because of the existence of individual differences, the same neurofeedback index value of different people corresponds to different emotional states, and therefore the use of the neurofeedback index without consideration causes the difference of the intervention effect.

Based on this, the present application provides a positive meditation-based neurofeedback intervention system, comprising:

the device comprises an electroencephalogram signal acquisition module, an electroencephalogram signal preprocessing module, an electroencephalogram signal feature extraction module, a normalized nerve feedback index calculation module and a normalized nerve feedback index and auditory nerve feedback mapping module;

the electroencephalogram signal acquisition module comprises a sensor and an electroencephalogram signal receiving submodule, and the sensor is connected with the electroencephalogram signal receiving submodule through Bluetooth;

the electroencephalogram signal acquisition module is configured to acquire electroencephalogram signals of a user and send the acquired electroencephalogram signals to the electroencephalogram signal preprocessing module; the electroencephalogram signal acquisition module is wirelessly connected with the electroencephalogram signal preprocessing module;

the electroencephalogram signal preprocessing module is configured to receive the electroencephalogram signals, perform denoising processing on the electroencephalogram signals to obtain processed first electroencephalogram signals, and send the first electroencephalogram signals to the electroencephalogram signal feature extraction module;

the electroencephalogram signal feature extraction module is configured to perform feature extraction on the received first electroencephalogram signal to obtain linear and nonlinear features about a plurality of wave bands, calculate a first emotional state value according to the linear and nonlinear features of different wave bands, and send the first emotional state value to the normalization nerve feedback index calculation module;

the normalized nerve feedback index calculation module is configured to substitute the first emotional state value into a normalization model, scale the first emotional state value by a normalization operation to obtain a second emotional state value, and send the second emotional state value to the normalized nerve feedback index and auditory nerve feedback mapping module;

the normalized nerve feedback index and auditory nerve feedback mapping module is configured to perform sound mapping on the second emotion state value through a natural sound volume mapping model and a natural sound type mapping model to obtain a mapped first audio, and send the first audio to a user.

The electroencephalogram signal acquisition module in the neural feedback intervention system based on the memorial meditation is composed of a portable three-lead electroencephalogram signal sensor and a corresponding electroencephalogram signal receiving sub-module, electroencephalogram signals are collected by the portable three-lead electroencephalogram signal sensor, transmitted to the corresponding electroencephalogram signal receiving module through Bluetooth, and transmitted into the electroencephalogram signal preprocessing module.

The electroencephalogram signal preprocessing module in the neural feedback intervention system based on the memorial meditation carries out noise preprocessing on the acquired electroencephalogram signal to obtain a relatively pure electroencephalogram signal. Common noises include myoelectric noise, electrooculogram noise, power frequency interference, and baseline drift. The preprocessing of the brain electrical signals needs to eliminate the noise from the mixed signals.

The electroencephalogram signal preprocessing module removes electromyographic noise by using a finite length single-bit impulse response filter based on a Blackman window. The EEG signal preprocessing module uses a wavelet transform and Kalman filtering combined model to remove the electro-ocular noise: the ocular artifact is constructed by locating the ocular region in the electroencephalogram signal through wavelet transformation, so that the pure electroencephalogram signal is extracted by utilizing Kalman filtering. The EEG signal preprocessing module adopts a 50Hz wave trap to remove power frequency interference. The electroencephalogram signal preprocessing module adopts direct current baseline calibration and removes linear trend items in a segmented mode.

The electroencephalogram signal feature extraction module in the neural feedback intervention system based on the memorial meditation carries out feature extraction operation according to the obtained relatively pure electroencephalogram signal. Linear characteristics of Alpha, Beta, Delta, Theta and Gamma wave bands are calculated by a frequency domain analysis method, and nonlinear characteristics of the Alpha, Beta, Delta, Theta and Gamma wave bands are calculated by a nonlinear dynamics method. And calculating a first emotional state value according to the linear and nonlinear characteristics of different wave bands.

The first emotional state value is the original nerve feedback index, and the second emotional state value is the normalized nerve feedback index. The first emotional state value is the same concept as the original feedback index, and the second emotional state value is the same concept as the normalized neurofeedback index.

The first emotional state value is calculated by linear and nonlinear characteristics of different bands, and can reflect different emotional states according to a calculation method and different bands and characteristics, for example, the first emotional state value calculated by Theta band absolute power/Beta band absolute power can reflect an attention state. The first emotional state value is calculated original data, and due to the existence of the individual difference of the electroencephalogram signals, the same first emotional state value of different people may have larger difference in value, which is not beneficial to neural feedback mapping.

The second emotion state value is scaled in equal proportion by the first emotion state value according to the normalization model, the value range of the second emotion state value is-1 to 1, and the influence caused by the individual difference of the electroencephalogram signals is effectively avoided.

The normalization nerve feedback index calculation module in the nerve feedback intervention system based on the memorial meditation improves the traditional nerve feedback by adopting a nerve feedback normalization model, namely, the feedback form mapping is not directly carried out by adopting a first emotion state value, but the original signal is input into the normalization model firstly, so that the feedback form mapping is carried out on an obtained second emotion state value. Through normalization operation, the first emotion state values of all people are scaled to the same interval in proportion, influence caused by individual difference of electroencephalogram signals can be effectively avoided, the system can receive the second emotion state values through a uniform interface, and feedback form mapping is carried out through a uniform format.

Wherein the normalization model is:

M＝{m₁,m₂,…,m_i… is a time series of first emotional state values, where m_iA first emotional state value representing the ith second, a sliding window of 20 data points in length being superimposed on the time seriesMouth, M_maxIs the maximum value of the first emotional state value in the current sliding window, M_minIs the minimum value of the first emotional state value in the current sliding window, M_meanIs the average value of the first emotional state value in the current sliding window, then n_iA second emotional state value of i second, and n_i∈[-1,1],n_iE.g. R. With a sliding window as shown in figure 4.

And obtaining a second emotion state value through the nerve feedback index normalization model, and transmitting the second emotion state value into the normalized nerve feedback index and auditory nerve feedback mapping module.

Note that M ═ M here₁,m₂,…,m_i…, which is a substitute for the first emotional state value time series, different neurofeedback indexes can be adopted according to actual needs to adapt to different intervention needs. For example, FAA (Frontal Alpha Asymmetry) may be used for pressure intervention, which is calculated as FAA — LnR-LnL, where R is Fp2 electrode Alpha band power and L is Fp1 electrode Alpha band power; ABR (alpha wave to beta wave power ratio) can be used for anxiety intervention and is calculated by the formulaWherein Alpha₁For Fp1 electrode Alpha band power, Beta₁Beta band power for Fp1 electrode, Alpha₂For Fp2 electrode Alpha band power, Beta₂Is the power of Fp2 electrode Beta wave band.

The traditional belief meditation training lacks the feedback characteristics, so that a trainer cannot know the training state of the trainer and the emotion state change conditions before and after training, the neural feedback and the belief meditation are combined, the neural feedback is based on the real-time feedback of the trainer, and the emotion change conditions of the trainer before and after training are obtained by calculating the related emotion state values of the trainer before and after training. Considering that the training of the memorial meditation is usually performed by closed-eye sitting, the feedback form should use auditory stimulation rather than visual, tactile, etc. stimulation. Aiming at the defect that the memorial meditation guidance words have pause gaps, the pause gaps are filled with natural sounds, namely the natural sounds are played while the memorial meditation guidance words are played, and the negative effects caused by mental wandering are reduced.

The present application combines natural sound with neurofeedback to present auditory neurofeedback based on natural sound, i.e. the feedback is in the form of a change in natural sound. Mapping the EEG signal of the trainer into the change of natural sound, and mapping the relation between the change of natural sound and the change of emotion according to the relation between the EEG signal and the change of emotion. The trainer can know the current emotion state of the trainer by perceiving the change of natural sound, and the emotion change state of the trainer is obtained by comparing the nerve feedback indexes before and after training so as to be presented in a mode acceptable by the trainer.

The normalized nerve feedback index and auditory nerve feedback mapping module in the neural feedback intervention system based on the memorial meditation maps the received second emotional state value to auditory nerve feedback based on natural sound. The feedback form of the auditory nerve feedback based on the natural sound is the change of the natural sound, including the change of the volume of the natural sound and the change of the kind of the natural sound played simultaneously.

Aiming at the change of the natural sound volume, the following natural sound volume mapping model is adopted:

OutputVolume＝W*SystemVolume

Volume＝-0.5*N+0.5；

the OutputVolume is the Volume finally received by the user, the systemlvolume is the Volume of the current equipment, W is a weight adjusting parameter, dBStep is a decibel adjusting step length, Volume is a second emotional state value mapping function, and N is a second emotional state value time sequence.

The mapping model performs a mapping of the second emotional state value to the volume that the user ultimately receives. The mapping function of the normalized neurofeedback indicator to the volume ultimately received by the user is shown in fig. 5.

For the change of the kind of the natural sound played at the same time, a natural sound kind mapping model as shown in fig. 6 is adopted. The larger the second emotional state value is, the more natural sound types are played simultaneously; the smaller the second emotional state value, the fewer the kinds of natural sounds are played at the same time. Through the natural sound kind that changes the simultaneous play, the change of cooperation natural sound volume builds quiet or noisy auditory environment to the current emotional state of user is reminded to the form of sound feedback (first audio frequency), specifically as follows: when the second emotional state value is closer to-1, the more noisy the auditory environment is perceived by the user, and the current emotional state is reminded to be worse; when the second emotional state value is closer to 1, the perceived auditory environment is quieter, and the current emotional state is reminded to be better.

The current emotional state of the user can be indicated according to the second emotional state value, and the closer the second emotional state value is to 1, the better the tested state is; the closer the normalized neurofeedback index value is to-1, the worse the test state.

The user carries out self-regulation after receiving first audio frequency, and the EEG that the user produced will be changed to this process, and the EEG after the change is received by EEG collection module to carry out next round of feedback.

Optionally, the sensor is a portable three-lead electroencephalogram signal sensor, acquires electroencephalogram signals of a user through three lead potentials of Fp1, Fp2 and Fpz placed in a forehead brain area, and sends the acquired electroencephalogram signals to the electroencephalogram signal receiving submodule through bluetooth; the sensor takes A1 or A2 as a reference potential, adopts a medical semi-wet electrode as a conducting medium, and is connected with a user through an ear clip electrode.

Most of electroencephalogram signal sensors used for nerve feedback are electrode caps and adopt 32-lead or 64-lead. Although more electroencephalogram signals can be collected, common nerve feedback index calculation only involves 2-4 leads, and the improvement of the number of the leads does not have significant help for the nerve feedback index calculation. Because the number of leads is large, the processing performance of the electroencephalogram signal is poor, and the requirement of nerve feedback on real-time performance cannot be met.

The portable three-lead electroencephalogram signal sensor is developed independently in Lanzhou university pervasive sensing and intelligent system laboratories. The electroencephalogram signal sensor adopts three forehead leads to acquire electroencephalogram data, the dimensionality of the electroencephalogram signal is reduced while the calculation integrity of a nerve feedback index is guaranteed, the calculation performance is improved, meanwhile, part of an electroencephalogram preprocessing program is solidified to hardware, and compared with the method that electroencephalogram preprocessing is carried out by using software, the processing performance is greatly improved. Different from traditional electrode cap adoption wired connection mode, the electroencephalogram signal sensor that this application adopted uses bluetooth transmission electroencephalogram signal, possesses characteristics such as easy operability, portability. By improving the electroencephalogram signal sensor adopted by the neural feedback, the electroencephalogram signal processing efficiency is remarkably improved, and the performance requirement of the neural feedback can be met. The potentials are shown in fig. 2.

This EEG signal sensor adopts medical semi-wet electrode as conduction medium, effectively reduces experimenter's the operation degree of difficulty and equipment wearing time when improving the electrical conductivity ability, reduces user's conflict psychology.

The EEG signal sensor adopts a Bluetooth 4.0 protocol to transmit EEG signals, the sampling rate of each lead EEG signal is 250Hz, the sampling precision is 24bit, the power consumption of each lead is less than 15mW, the low-noise amplification factor is 24 times, the maximum amplification signal is 1 muV, and the power supply voltage is 3.3V. The electroencephalogram data acquisition equipment is internally composed of a digital part and an analog part. The digital circuit mainly comprises an A/D converter, a DSP, a USB chip, a direct current correction circuit and an alternating current impedance detection circuit. The analog part comprises a preamplifier circuit and a filter circuit, the core of the acquisition system is the preamplifier circuit, the circuit performance is greatly improved, the filter circuit comprises a notch circuit and a low-pass circuit, and power frequency interference during electroencephalogram signal acquisition is processed by the notch circuit. The portable three-lead brain electrical signal sensor is shown in figure 3.

Optionally, the system further comprises an electroencephalogram signal storage module, wherein the electroencephalogram signal storage module is configured to store electroencephalogram signals generated in the process of the memorial meditation training and processed data thereof;

the electroencephalogram signal storage module is respectively connected with the electroencephalogram signal acquisition module, the electroencephalogram signal preprocessing module, the electroencephalogram signal feature extraction module, the normalized nerve feedback index calculation module and the normalized nerve feedback index and auditory nerve feedback mapping module; the electroencephalogram signal storage module is divided into four sub-modules: an electroencephalogram signal buffer zone submodule, an electroencephalogram signal data file submodule, an electroencephalogram signal characteristic buffer zone submodule and a normalized nerve feedback index buffer zone submodule; different sub-modules respectively store the EEG signals corresponding to the buffer zones and the processed data of the EEG signals.

The electroencephalogram signal buffer zone sub-module is a data transmission channel of the electroencephalogram signal acquisition module and the electroencephalogram signal preprocessing module. The module opens up a buffer zone in the memory, namely an electroencephalogram buffer zone. Opening data writing permission to the electroencephalogram signal acquisition module, so that the electroencephalogram signal acquisition module can only write data into the electroencephalogram signal buffer zone but cannot read the data; and opening data reading permission to the electroencephalogram signal preprocessing module, so that the electroencephalogram signal preprocessing module can only read data from the electroencephalogram signal buffer zone and can not write the data.

The electroencephalogram signal data file submodule is a data transmission channel of the electroencephalogram signal preprocessing module and the electroencephalogram signal feature extraction module. The module creates an EEG signal data file in a magnetic disk newly and opens data writing authority to the EEG signal preprocessing module, so that the EEG signal preprocessing module can only write data into an EEG signal buffer zone but cannot read the data; and opening data reading permission to the electroencephalogram signal feature extraction module, so that the electroencephalogram signal feature extraction module can only read data from the electroencephalogram signal buffer zone but cannot write the data.

The electroencephalogram signal feature buffer zone sub-module is a data transmission channel of the electroencephalogram signal feature extraction module and the normalization nerve feedback index calculation module. The module opens up a buffer zone in the memory, namely an electroencephalogram signal characteristic buffer zone. Opening data writing permission to the electroencephalogram signal feature extraction module, so that the electroencephalogram signal feature extraction module can only write data into the electroencephalogram signal feature buffer area and cannot read the data; and opening data reading permission to the normalized nerve feedback index calculation module, so that the normalized nerve feedback index calculation module can only read data from the electroencephalogram signal characteristic buffer zone but cannot write the data.

The normalized nerve feedback index buffer area sub-module is a data transmission channel of the normalized nerve feedback index calculation module and the normalized nerve feedback index and auditory nerve feedback mapping module. The module creates a buffer area in the memory, namely a normalized nerve feedback index buffer area. Opening data writing authority to the normalized nerve feedback index calculation module, so that the normalized nerve feedback index calculation module can only write data into the normalized nerve feedback index buffer area but cannot read the data; and opening data reading permission to the normalized nerve feedback index and auditory nerve feedback mapping module, so that the normalized nerve feedback index and auditory nerve feedback mapping module can only read data from the normalized nerve feedback index buffer area and can not write the data in the normalized nerve feedback index buffer area. In addition, the monitoring submodule in the feedback stop control module also has the data reading permission of the normalized nerve feedback index buffer area.

Optionally, the system further includes a feedback stop control module, and the feedback stop control module is respectively connected to the electroencephalogram signal acquisition module, the normalized nerve feedback index and auditory nerve feedback mapping module; the feedback stop control module comprises a monitoring submodule and a feedback cut-off submodule;

the feedback stop control module is configured to acquire a second electroencephalogram signal and judge whether an intervention ending condition is met; if the condition for ending intervention is not met, sending an instruction to the electroencephalogram signal acquisition module to continue the next round of neural feedback intervention;

stopping the neural feedback intervention if the intervention ending condition is met; the second electroencephalogram signal is the electroencephalogram signal after the user listens to the first audio.

The feedback stop control module is relatively independent of each functional module of the system and is divided into a monitoring submodule and a feedback stop submodule.

The monitoring submodule is responsible for continuously monitoring the second emotional state value. The method comprises the step of judging whether a feedback cutoff condition is met currently or not by acquiring data in a normalized neural feedback index buffer area. When the feedback cut-off condition is met and the current application scene is used by the user at home, the monitoring submodule sends a cut-off signal to the feedback cut-off submodule; when the feedback cutoff condition is met and the current application scene is used by the user under the guidance of the trainer, the monitoring submodule waits for the confirmation of the trainer, and when the trainer confirms that the training is finished, the monitoring submodule sends a cutoff signal to the feedback cutoff submodule, otherwise, the cutoff signal is not sent.

And a feedback cutoff submodule. The submodule is responsible for receiving the cutoff signal and stopping the system from operating. After the sub-module receives the cut-off signal, the Bluetooth conduction between the portable three-lead electroencephalogram signal sensor and the electroencephalogram signal receiving sub-module corresponding to the sensor is interrupted, so that the system stops operating.

The system provided by the application comprises a feedback closed loop consisting of an electroencephalogram signal acquisition module, an electroencephalogram signal preprocessing module, an electroencephalogram signal characteristic extraction module, a normalized nerve feedback index calculation module, a normalized nerve feedback index and an auditory nerve feedback mapping module, and a user continuously adjusts own electroencephalogram signals through multiple rounds of feedback closed loops. According to different use scenes, the following feedback cutoff conditions are set, and when the feedback cutoff conditions are met, the system stops operating.

The user feeds back the cutoff condition when using the designed system at home:

(1) the user's second emotional state value stabilizes within a certain threshold interval. Specifically, when the second emotional state value of the user is stabilized within the threshold range of [0.9,1.0] and lasts for 30 seconds or more, it is considered that the present objective of the normal meditation training is achieved, and the normal meditation training can be terminated. And the feedback stop control module stops the system, and in the application scene, once the feedback stop condition is met, namely that the second emotional state value of the user is stabilized within the threshold interval of [0.9,1.0] and lasts for more than 30s, the monitoring submodule in the feedback stop control module automatically sends a stop signal to the feedback stop submodule. And the feedback cutoff submodule stops the system operation after receiving the cutoff signal.

(2) A predetermined training time is reached. The duration of the positive meditation training is about thirty minutes, and if the user does not meet the requirement (1) in the duration, the positive meditation training is required to be continuously carried out until the training is finished.

The user feeds back the cutoff condition when using the system designed by the invention under the guidance of the trainer:

(1) the user's second emotional state value stabilizes within a certain threshold interval. When the second emotional state value of the user is stabilized within the threshold interval of [0.9,1.0] and lasts for more than 30s, the purpose of the normal meditation training is considered to be achieved, and the normal meditation training can be ended. And in the application scene, once a feedback cutoff condition is met, namely that the second emotion state value of the user is stabilized within a threshold interval of [0.9,1.0] and lasts for more than 30s, waiting for confirmation of a trainer, and if the trainer confirms that the training is finished, sending a cutoff signal to a feedback cutoff submodule by a monitoring submodule in the feedback cutoff control module, otherwise, not sending the cutoff signal. And the feedback cutoff submodule stops the system operation after receiving the cutoff signal.

(2) A predetermined training time is reached. The duration of the candid meditation training is determined by the trainer, and if the user does not meet the condition that the second emotional state value is stable within the threshold interval of [0.9,1.0] and lasts for more than 30s during the duration, the candid meditation training is required to be continuously carried out until the training is finished.

Figure 7 is a block diagram of the positive meditation-based neurofeedback intervention system provided by the present application.

The application provides a neural feedback intervention system based on the memorial meditation, which has the working principle that: the electroencephalogram signal acquisition module acquires electroencephalogram signals of a user, and the electroencephalogram signal preprocessing module carries out noise preprocessing on the acquired electroencephalogram signals; the electroencephalogram signal feature extraction module is used for carrying out feature extraction and calculation on the first electroencephalogram signal after the noise is removed to obtain a first emotional state value; the normalization nerve feedback index calculation module substitutes the first emotion state value into the normalization model, and a second emotion state value is obtained through normalization operation; the normalized nerve feedback index and auditory nerve feedback mapping module performs sound mapping on the second emotion state value to obtain a mapped first audio, and sends the first audio to a user; the feedback stop control module acquires a second electroencephalogram (the electroencephalogram after the user listens to the first audio frequency) and judges whether the intervention condition is met or not, if the intervention condition is not met, the electroencephalogram acquisition module acquires the current electroencephalogram of the user and continues the next round of neural feedback intervention, and the electroencephalogram is continuously adjusted in a multi-round feedback closed loop mode.

In another aspect, the present application also provides a method for neural feedback intervention based on the memorial meditation, comprising:

collecting electroencephalogram signals of a user, and storing the collected electroencephalogram signals; the electroencephalogram signals are stored in an electroencephalogram signal buffer area;

reading an electroencephalogram signal of the electroencephalogram signal buffer area, and performing denoising processing to obtain a first electroencephalogram signal after denoising processing;

performing feature extraction on the first electroencephalogram signal to obtain linear and nonlinear features of the first electroencephalogram signal about a plurality of wave bands;

calculating a first emotional state value according to the linear and nonlinear characteristics;

substituting the first emotional state value into a normalization model, and scaling the first emotional state value according to a proportion through normalization operation to obtain a second emotional state value;

performing sound mapping on the second emotion state value through a natural sound volume mapping model and a natural sound type mapping model to obtain a mapped first audio, and sending the first audio to a user;

collecting a second electroencephalogram signal, and judging whether an intervention ending condition is met; the intervention ending condition comprises a first intervention condition and a second intervention condition; the second electroencephalogram signal is an electroencephalogram signal obtained after a user listens to a first audio frequency;

and if the condition for ending the intervention is not met, acquiring the current electroencephalogram signal of the user and continuing the next round of neural feedback intervention.

Optionally, the step of reading the electroencephalogram signal in the electroencephalogram signal buffer area and performing denoising processing to obtain a first electroencephalogram signal after denoising processing includes:

and performing electromyographic noise, ocular electrical noise, power frequency interference and baseline drift noise removal operation on the original electroencephalogram signal to obtain a preprocessed first electroencephalogram signal.

Optionally, the step of extracting the features of the first electroencephalogram signal to obtain linear and nonlinear features of the first electroencephalogram signal about a plurality of wave bands includes:

and calculating linear characteristics of Alpha, Beta, Delta, Theta and Gamma wave bands of the first electroencephalogram signal by a frequency domain analysis method, and calculating nonlinear characteristics of the Alpha, Beta, Delta, Theta and Gamma wave bands by a nonlinear dynamics method.

Optionally, the step of acquiring a second electroencephalogram signal and determining whether the condition for ending intervention is satisfied further includes:

acquiring a second emotion state value of the user, judging whether the second emotion state value is within a preset threshold interval, and if so, meeting a first intervention condition;

judging whether the training time of the user reaches the preset training time or not, and if so, meeting a second intervention condition;

and when the first intervention condition and the second intervention condition are simultaneously met, judging that the intervention ending condition is met, and stopping the neural feedback intervention.

The following specific cases are given to illustrate the specific working method of the neural feedback intervention system based on the memorial meditation provided by the application:

(1) preparation of the experiment: the subject is asked to fill in basic information, such as name, age, gender, etc. Informing the relation between the emotional state and the auditory nerve feedback of the tested person before the experiment begins, requiring the tested person to follow the guide words of the training of the positive meditation during the training process of the positive meditation, sensing the received auditory nerve feedback at any time, and self-regulating according to the received auditory nerve feedback. Specifically, when the auditive environment sensed by the examinee is noisy, the examinee is required to improve the attention degree to the formal meditation training and train the auditive training following the formal meditation training guidance; the subject is required to keep the current state as the subject perceives a quieter auditory environment.

(2) Pasting the medical semi-wet electrode: the medical semi-wet electrode is connected to the Fp1, Fpz and Fp2 leads of the portable three-lead brain signal sensor, and the medical semi-wet electrode is pasted to the forehead brain area to be tested according to the electrode position shown in figure 2.

(3) Equipment connection: and starting a power supply of the portable three-lead electroencephalogram signal sensor, starting the neural feedback intervention system for the memorial meditation, and clicking a start-to-collect button in the system to collect the electroencephalogram signals after the connection is successful. Due to the fact that the Bluetooth protocol is adopted to connect the equipment, when connection fails, the equipment needs to be restarted and collection operation needs to be carried out again.

(4) Acquiring an electroencephalogram signal: when the connection is successful and the electroencephalogram signal acquisition is started, the portable three-lead electroencephalogram signal sensor transmits the electroencephalogram signals to the system through a Bluetooth 4.0 protocol at a sampling rate of 250Hz, and the electroencephalogram signals are cached in the memory and transmitted into the electroencephalogram signal preprocessing module.

(5) Training of positive meditation: after the electroencephalogram signal acquisition is started, the earphone is worn for the person to be tested, the memorial meditation training guide is played, and the memorial meditation training guide is not processed in any form in the whole training process.

(6) Preprocessing an electroencephalogram signal: the electroencephalogram signal preprocessing module carries out noise removal operations such as electromyographic noise, electro-ocular noise, power frequency interference, baseline drift and the like on the received original electroencephalogram signals, and transmits the obtained relatively pure electroencephalogram signals to the electroencephalogram signal feature extraction module.

(7) Extraction of electroencephalogram signal features: the EEG signal feature extraction module calculates linear and nonlinear features of Alpha, Beta, Delta, Theta and Gamma wave bands of the received relatively pure EEG signals by adopting a frequency domain analysis method and a nonlinear dynamics method. And calculating a first emotional state value according to a predetermined nerve feedback index formula, and transmitting the first emotional state value into a normalized nerve feedback index calculation module.

(8) Calculating a normalized nerve feedback index: the normalization nerve feedback index calculation module inputs the received first emotion state value into the nerve feedback index normalization model, so that a second emotion state value is calculated and is transmitted into the normalization nerve feedback index and auditory nerve feedback mapping module.

(9) Mapping of normalized neurofeedback index to auditory neurofeedback: the mapping of the normalized neuro-feedback index and the auditory neuro-feedback is accomplished by a natural sound volume mapping model as shown in fig. 5, and by a natural sound type mapping model as shown in fig. 6. Through changing the natural sound type of broadcast simultaneously, the change of cooperation natural sound volume builds quiet or noisy auditory environment to the current emotional state of user is reminded to the form of sound feedback, thereby reaches the purpose of giving the experimental feedback.

(10) Self-regulation: after the tested receiving the auditory nerve feedback, the self-regulation is carried out according to the relationship between the informed emotional state and the auditory nerve feedback before the test. The tested self-regulation changes the electroencephalogram signal, so that the changed electroencephalogram is received by the portable three-lead electroencephalogram signal sensor after being awakened, and the next round of feedback is started.

The embodiments provided in the present application are only a few examples of the general concept of the present application, and do not limit the scope of the present application. Any other embodiments extended according to the scheme of the present application without inventive efforts will be within the scope of protection of the present application for a person skilled in the art.