A passive-active stress regulation system and method based on individualized music eeg neurofeedback

Abstract

The application discloses a kind of active and passive pressure regulation system and method based on individualization music eeg neural feedback, the system includes: through the eeg acquisition module of evoked eeg, the eeg signal of subject is collected to carry out music neural feedback experiment, through eeg signal preprocessing module, eeg signal is preprocessed, through pressure feature extraction module, the eeg signal after pre-processing is carried out feature extraction, obtain pressure eeg feature, through pressure state music feedback module, individual baseline threshold analysis is carried out to pressure eeg feature, obtain individualization music feedback to carry out real-time adjustment to the music played to subject, guide the pressure state regulation iterative training of subject.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of non-invasive brain function neural regulation, and particularly relates to a passive stress regulation system and method based on individualized music electroencephalogram neural feedback. BACKGROUND

[0002] Under the current social background, people generally suffer from various pressures. According to the data of the World Health Organization, the problem of excessive stress is increasingly prominent, and shows a trend of younger age. Studies have shown that stress activates the hypothalamic-pituitary-adrenal axis of the brain, causing the adrenal cortex to release cortisol, which in turn activates the sympathetic nervous system, causing physiological responses such as heart rate, blood pressure, and respiration. These responses are helpful in coping with stress in the short term, but if they last too long, they will cause some harm to the body and may trigger heart disease, high blood pressure, diabetes, depression, and anxiety, affecting the function of the immune system. Existing stress management methods mainly include proper rest, healthy diet, reasonable exercise, and psychological counseling, etc., but the effects of these methods are relatively slow and individual differences are significant, and there is a lack of subjective and objective evaluation system. Therefore, there is an urgent need for personalized and long-lasting effective stress management approaches.

[0003] The characteristics of electroencephalogram signals are closely related to individual emotional state and stress level. Neurofeedback and closed-loop control have been widely verified in stress regulation at home and abroad. Existing research shows that stress groups will have abnormal changes in electroencephalogram, and high stress levels will lead to abnormal alpha band and changes in electroencephalogram microstate time domain parameters. Therefore, exploring the stress mechanism through electroencephalogram level and conducting neural regulation provides a potential and effective new approach for stress management.

[0004] Music has the ability to change emotions, improve cognitive conditions by changing emotions, release negative emotions of subjects, stimulate more positive emotions, and relieve stress, thereby achieving the effect of improving mental health. Neurofeedback is a technology that realizes individual self-regulation by real-time feedback of neural activity information. According to the physiological characteristics and needs of individuals, it helps individuals to reduce anxiety and stress, better understand and regulate individual emotions, and improve attention through training. Among the many feedback types, music has become an important part of neurofeedback due to its ease of perception and influence on emotions. Music neurofeedback mainly adjusts music parameter levels with changes in neural signals to achieve neural regulation. Existing music neurofeedback research has achieved positive results in reducing anxiety, emotional regulation, improving cognitive function, sleep quality, and reducing depression. It has the advantages of non-invasiveness, individualization, and long duration, which can maximize the regulation efficiency and reduce the side effects caused by treatment, and effectively regulate individuals for stress groups. However, the existing technology has the disadvantages of slow regulation and significant individual differences in existing stress management. SUMMARY

[0005] To solve the above technical problems, the application provides a kind of active and passive pressure regulation system and method based on individualized music EEG neural feedback to solve the problems existing in the prior art.

[0006] To achieve the above object, the application provides a kind of active and passive pressure regulation system based on individualized music EEG neural feedback, comprising:

[0007] evoked EEG acquisition module, EEG signal preprocessing module, pressure feature extraction module, pressure state music feedback module, wherein evoked EEG acquisition module, EEG signal preprocessing module, pressure feature extraction module, pressure state music feedback module are sequentially connected;

[0008] Wherein, the evoked EEG acquisition module is used to collect the EEG signal of the subject during the music neural feedback experiment, the EEG signal preprocessing module is used to preprocess the EEG signal, the pressure feature extraction module is used to extract the features of the preprocessed EEG signal to obtain the pressure EEG feature, and the pressure state music feedback module is used to analyze the individual baseline threshold of the pressure EEG feature to obtain the individualized music feedback to adjust the music played to the subject in real time and guide the iterative training of the subject's pressure state regulation.

[0009] Optionally, in the EEG signal preprocessing module, the preprocessing process includes electrode positioning, re-reference, filtering, electromyography and electrooculography removal processing, and independent principal component analysis.

[0010] Optionally, in the pressure feature extraction module, the process of extracting features from the preprocessed EEG signal includes: splitting the preprocessed EEG signal to obtain short signals, windowing and Fourier transform for each short signal to obtain power spectrum estimation of each signal, and calculating the average value of the power spectrum estimation of each signal to obtain the average power spectrum estimation, i.e. pressure EEG feature.

[0011] Optionally, in the pressure state music feedback module, the process of individual baseline threshold analysis includes:

[0012] Obtain the individual baseline threshold, judge the pressure EEG feature according to the individual baseline threshold, when the pressure EEG feature is greater than the individual baseline threshold, the individualized music feedback is to increase the volume of music, and when the pressure EEG feature is less than the individual baseline threshold, the individualized music feedback is to reduce the volume of music.

[0013] Optionally, the process of obtaining the individual baseline threshold includes:

[0014] The resting state electroencephalogram signal is collected by an induced electroencephalogram acquisition module, the resting state electroencephalogram signal is preprocessed by an electroencephalogram signal preprocessing module, the electroencephalogram signal after preprocessing is subjected to feature extraction by a stress feature extraction module, and the resting state electroencephalogram feature is obtained, and the mean value of the resting state electroencephalogram feature, i.e., the individual baseline threshold, is obtained by mean value calculation of the resting state electroencephalogram feature by the stress state music feedback module.

[0015] Optionally, the system further comprises an individualized music stimulation module, wherein the individualized music stimulation module is connected with the stress state music neural feedback module, and an individual music preference type is obtained according to the individual electroencephalogram feature of the subject and an individualized preference score, wherein the individualized preference score is obtained by numerical input, and the individual electroencephalogram feature is obtained by sequentially preprocessing and feature extraction on the induced electroencephalogram signals corresponding to different music types, and the obtained individual music preference type is used in the stress state music neural feedback module.

[0016] In order to better achieve the above technical purpose, the present application provides a kind of active and passive pressure regulation method based on individualized music electroencephalogram neural feedback, comprising: collecting the electroencephalogram signal of subject music neural feedback experiment, electroencephalogram signal is preprocessed, the electroencephalogram signal after preprocessing is subjected to feature extraction, and the stress electroencephalogram feature is obtained, and the individual baseline threshold analysis is carried out to the stress electroencephalogram feature, and the individualized music feedback is obtained to adjust the music played to subject in real time, and the pressure state regulation iterative training of subject is guided.

[0017] Compared with the prior art, the present application has the following advantages and technical effects:

[0018] The present application is particularly aimed at the stress brain activity characteristics of the existing unhealthy psychological state population, and a passive pressure regulation system based on individualized music electroencephalogram neural feedback is designed using brain-computer interface technology.

[0019] The present application proposes an individualized music stimulation module, which solves the problem that the effect is not good due to the difference in electroencephalogram rhythm between individuals and the fact that the standardized scheme cannot meet the individualized demand. The feedback music type is selected in combination with electroencephalogram features and individual preferences, dynamic multi-dimensional subjective and objective indicators are provided to evaluate the changes in individual stress brain conditions, the stress brain mechanism of the subject is revealed, and an effective scheme is provided for individualized music neural feedback of the subject.

[0020] The present application introduces a music feedback stimulation mode, realizes efficient regulation of the active and passive stress levels of the user, selects the passive music stimulation type based on electroencephalogram features and individual preferences, modifies the feedback music parameters in real time based on the stress electroencephalogram features, targets the regulation of stress-related brain features through active closed-loop feedback, and adaptively feedbacks to regulate the individual relaxation degree, which can quickly and non-invasively regulate the stress level of the subject. BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings, which form a part of the present application, are intended to provide further understanding of the present application and are incorporated herein for a purpose of explanations and are not intended as improper limitations on the present application. In the drawings:

[0022] Figure 1 A structural schematic diagram of a passive-active pressure regulation intervention system based on individualized music electroencephalogram neural feedback according to an embodiment of the present application;

[0023] Figure 2 A music neural feedback module according to an embodiment of the present application;

[0024] Figure 3 A passive-active closed-loop real-time data processing and music neural feedback flowchart according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0026] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a group of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0027] Embodiment 1

[0028] The present application aims to address the drawbacks of slow efficacy and significant individual differences of existing stress management, and proposes a passive-active pressure regulation system based on individualized music electroencephalogram neural feedback, which uses brain-computer interface combined with music therapy technology, adjusts the characteristics of stress brain activity according to personal preferences and electrophysiological characteristics, and assists in the adjustment of stress and other adverse psychological and emotional activities in clinical or family settings.

[0029] To achieve the above-mentioned purpose, as shown in Figure 1 The present application provides a passive-active pressure regulation system based on individualized music electroencephalogram neural feedback, which comprises:

[0030] An evoked electroencephalogram acquisition module, an electroencephalogram signal preprocessing module, a stress feature extraction module, and a stress state music feedback module, wherein the evoked electroencephalogram acquisition module, the electroencephalogram signal preprocessing module, the stress feature extraction module, and the stress state music feedback module are connected in sequence, the stress state music feedback module provides feedback guidance to the subjects, the subjects adjust the stress state according to the feedback guidance, and then the brain electrical signals collected by the evoked electroencephalogram acquisition module are adjusted; specifically:

[0031] an induced electroencephalogram acquisition module, configured to acquire electroencephalogram signals of a subject during a music neurofeedback experiment;

[0032] an electroencephalogram signal preprocessing module, configured to preprocess the electroencephalogram signals;

[0033] a stress feature extraction module, configured to extract stress electroencephalogram features of the preprocessed electroencephalogram signals;

[0034] a stress state music feedback module, configured to analyze the stress electroencephalogram features to represent individualized stress states of the subject, and to generate individualized music feedback according to the analysis results, so as to guide the subject to adjust the stress state through iterative training intervention.

[0035] As some embodiments, the system provided by the present application further comprises an individualized music stimulation module, configured to analyze individual music preference types according to individual electroencephalogram features and individualized preference scores of the subject, and to connect the stress state music neurofeedback module to support the stress state music neurofeedback module.

[0036] Specifically, the individualized music stimulation module is configured to select individualized music preference types of the subject. The module comprises three parts: a music preference experiment paradigm, in which the subject inputs individualized preference scores by listening to different music types; music-induced electroencephalogram feature extraction, in which the subject's induced electroencephalogram signals are acquired by playing different music types to the subject, and individual electroencephalogram features, i.e., stress-related spectral features, are obtained through electroencephalogram preprocessing and feature extraction; and individualized music type screening, in which the individualized music preference types are screened by using repeated measures ANOVA method based on the individualized preference scores and the stress-related spectral features.

[0037] Specifically, the implementation process of the repeated measures ANOVA method comprises: acquiring electroencephalogram signals in real time while listening to music, obtaining electroencephalogram features in different music states, performing repeated measures ANOVA on the electroencephalogram features acquired in the resting state, obtaining a statistical index P value for evaluating the significance of the difference between groups, and combining the individualized preference scores (five-point Likert scale). If the P value of the repeated measures ANOVA is significant (P<0.05), the music type with a larger individualized preference score is selected from the significant music types (if the individualized preference scores are the same, the music type with a smaller P value is selected); if the P value of the repeated measures ANOVA is not significant (P≥0.05), the music type with a smaller P value is selected from the music types with an individualized preference score greater than or equal to 3 (if all the individualized preference scores are less than 3, the music type with the smallest P value is selected) 。

[0038] As some embodiments, the induced brain electrical acquisition module acquires the brain electrical signals of the subject before and after the music neural feedback experiment, and during the experiment, that is, the acquired brain electrical signals include the resting state brain electrical signals before the experiment, the resting state brain electrical signals after the experiment, and the real-time brain electrical signals, and the individual threshold baseline uses the individual threshold calculated only from the resting state brain electrical signals before the experiment.

[0039] As some embodiments, the process of pre-processing the brain electrical signals in the brain electrical signal pre-processing module includes electrode positioning, re-reference, filtering, electromyography and electrooculography removal processing, independent principal component analysis, and obtaining pure brain electrical signals of the subject.

[0040] As some embodiments, the stress brain electrical features include time domain and frequency domain brain electrical signal features related to the stress state.

[0041] As some embodiments, the stress state music neural feedback module is used for analyzing the stress brain electrical features corresponding to the music parameters; wherein the music neural feedback material is selected by the subject according to the individualized music stimulation module, the baseline brain features of the subject, that is, the individual baseline threshold, are obtained according to the resting state brain electrical signals, the stress brain electrical features corresponding to the real-time brain electrical signals are analyzed according to the individual baseline threshold, the individualized music feedback is generated, and the reward and punishment feedback signals are provided to guide the individualized stress level adjustment of the subject.

[0042] The individualized stress adjustment intervention system based on the individualized music neural feedback of the embodiments of the present application can be used for adjustment intervention when the stress is too large, and has potential clinical value and social value.

[0043] The above technical solutions are described in detail in combination with the related drawings.

[0044] As shown in Figure 1 The embodiments of the present application provide a stress adjustment system based on an individualized music neural feedback system, which comprises:

[0045] An induced brain electrical acquisition module is used for acquiring the multi-channel brain electrical signals of the subject during the music neural feedback experiment, so as to obtain the brain electrical signals induced by the subject when listening to the individualized preferred type of music stimulation, and for the use of the subsequent brain electrical signal pre-processing module;

[0046] A brain electrical signal pre-processing module is used for pre-processing the brain electrical signals, including electrode positioning, re-reference, filtering, electromyography and electrooculography removal processing, independent principal component analysis, and other noise removal methods, so as to obtain pure brain electrical signals of the subject for the use of the subsequent stress feature brain feature extraction module;

[0047] a pressure feature extraction module for extracting real-time features of real-time EEG signals related to individual stress in real time, the stress EEG features including stress-related time-domain and frequency-domain EEG signal features, etc., which will be used as input of the stress state music feedback module for subsequent use of the stress state music feedback module;

[0048] a stress state music feedback module for analyzing the stress EEG features to represent the individualized stress state of the subject, and generating individualized music feedback according to the analysis results to intervene in the adjustment of the stress state of the subject through iterative training in real time.

[0049] As some embodiments, the system provided by the present application further comprises an individualized music stimulation module for obtaining the individualized music preference type according to the individual EEG features and individualized preferences. This module includes three parts: a music preference experiment paradigm, in which the subject is played different music types, and the subject inputs the individualized preference score; a music-induced EEG feature extraction, in which the subject is played different music types, and the induced EEG signals of the subject are collected, and the individual EEG features, i.e., stress-related spectral features, are obtained through EEG preprocessing and feature extraction; and an individualized music type screening, in which the individualized music preference type is screened out by using repeated measurement variance analysis method based on the individualized preference score and the stress-related spectral features.

[0050] In the above content, the music-induced EEG feature extraction can be performed by the induced EEG collection module, the EEG signal preprocessing module and the pressure feature extraction module of the present application to preprocess and extract the features of the EEG signals induced under different music types, so as to obtain the individual EEG features.

[0051] Specifically, the active-passive stress regulation system provided by the present application comprises 4 online modules (induced EEG collection module, EEG signal preprocessing module, pressure feature extraction module, and stress state music feedback module) and 1 offline module (individualized music stimulation module), wherein:

[0052] The induced brain electrical acquisition module of some embodiments acquires 32-channel signals of the subject during the music neurofeedback experiment at a sampling rate of 250 Hz, including: EEG signals of FP1, Fz, F3, F7, FT9, FC5, FC1, C3, T7, CP5, CP1, Pz, P3, P7, O1, Oz, O2, P4, P8, TP10, CP6, CP2, Cz, C4, T8, FT10, FC6, FC2, F4, F8, Fp2, and TP9 electrode channels. The real-time data stream (Lab Streaming Layer, LSL) technology is used to synchronize the real-time EEG signals from the EEG acquisition device to the individualized music neurofeedback system, and the real-time acquired EEG signals are stored in the memory for subsequent EEG signal processing.

[0053] The EEG signal preprocessing module of some embodiments performs real-time preprocessing on the acquired multi-channel EEG signals, selects TP9 and TP10 channels in the multi-channel as reference electrodes, removes power frequency interference and retains the required EEG signal frequency band through band-pass filtering of a finite impulse response filter (FIR) with a length of 0.5-45 Hz, and then uses extended Kalman filtering to provide real-time state estimation, suppress noise and interference in the EEG signal, and has an adaptive effect feature. The filter parameters are dynamically adjusted according to the characteristics of the EEG signal to achieve optimal filtering effect. The artifact subspace reconstruction (ASR) method mainly relies on independent principal component analysis, aiming to eliminate the tail traces of high-amplitude motion components such as eye movement, ECG, and lip movement. By calculating the mean square value in the sliding window, calibration data are obtained to eliminate signals that do not meet the standard. The artifact subspace reconstruction method can effectively remove abnormal signals, has fast operation speed and low computational complexity, and through the above sequential preprocessing methods, pure EEG signals are obtained.

[0054] The pressure feature extraction module of some embodiments extracts pressure EEG features from the pure pressure-related EEG signal segments obtained after the EEG signal preprocessing module. In EEG signal analysis, the frequency characteristics of brain activity can be understood through power spectral density estimation, and the power spectral density estimation is used as a pressure EEG feature, so as to better understand the working mechanism of the brain.

[0055] Specifically, the Welch method used by the pressure feature extraction module is an improved periodogram method. First, the preprocessed electroencephalogram signal, i.e., the signal to be estimated x(n), is divided into multiple overlapping short segments, each 2 seconds long, with 50% overlap between each short segment and the previous one, thereby improving the frequency resolution. Then, each signal segment is windowed and then Fourier transformed to obtain the power spectrum estimate of each signal segment, which can effectively reduce the influence of non-target components. Then, all segments are added together, and the average power spectrum estimate is obtained by removing the sample number.

[0056] wherein the power spectrum estimate of each signal segment is as follows:

[0057]

[0058] wherein, is the power spectrum estimate of the i-th signal segment, M is the length of each signal segment sequence, n is the index of all sample points in the signal segment, ω is the angular frequency, which is the frequency parameter in Fourier transform, and U is the normalization factor to ensure the realization of asymptotic unbiased estimation:

[0059]

[0060] M is the length of each signal segment sequence, and d(n) is a smoothing window function with the characteristics of a non-rectangular window for signal processing, which reduces the influence of non-target frequency components and reduces spectral leakage. The main lobe width can be appropriately increased to make different frequency components easier to distinguish. Then, the average period method is used to divide the entire signal sequence into L overlapping paragraphs, each with a length of M. The power spectrum estimate of all signal sequences is calculated, and the specific calculation process is shown in equation (3).

[0061]

[0062] As some embodiments, the stress state music feedback module is mainly realized through a self-developed high-compatibility data real-time processing platform, including individual baseline threshold calculation and real-time music neural feedback.

[0063] Specifically, in the individual baseline threshold calculation, the module is based on the resting-state electroencephalogram signal, pre-processes and extracts features from the resting-state electroencephalogram signal to generate resting-state electroencephalogram features. Starting from the resting-state electroencephalogram features, the mean value of the resting-state electroencephalogram features is calculated as the threshold of the experiment feedback, i.e., the individual baseline threshold, which reflects the subject's own electroencephalogram activity level. This process takes into account the differences in electroencephalogram activity levels that may occur in each person.

[0064] Specifically, in real-time music neural feedback, after calculating the mean of resting-state EEG features, the mean is taken as the individual baseline threshold in individualized music feedback. The real-time EEG signal is preprocessed and features are extracted to obtain real-time EEG features. The real-time EEG feature value is calculated every two seconds. When the real-time feature EEG value is greater than the mean of the resting-state EEG feature, the volume of the music will increase, that is, the brain activity level of the subject is higher than its own average state, as positive feedback, guiding and encouraging the subject to maintain or further improve this state. When the real-time feature value is less than the mean of the resting-state EEG feature, the volume of the music will decrease, indicating that the brain activity level of the subject is lower than its own average state, and the subject needs to adjust the brain state or body posture to achieve a more relaxed state. At the same time, ensure that the system volume is in the range of 0 to 100, and the subject himself feels the music size during the experiment. The subject adjusts to the most relaxed state by adjusting the brain state and body posture. The whole training is carried out in a cycle, and the volume of the music neural feedback training is adjusted.

[0065] The embodiment provides a kind of active and passive pressure regulation intervention system based on individualized music EEG neural feedback, it is installed in the equipment of Windows system and run using MATLAB language development, it includes evoked EEG acquisition module, EEG signal preprocessing module, pressure feature extraction module, pressure state music feedback module.Individualized music stimulation module is realized by three functions of offline music preference experiment paradigm, music evoked EEG, individualized music type screening, is used to obtain individual music preference type according to individual EEG feature and individual preference.

[0066] Referring to Figure 2 As shown in the left drawing of the music neural feedback control end interface, for the subject to monitor the EEG signal state of the subject and control the experiment process, the right drawing is music playing interface, for playing individualized music type;The active and passive pressure condition intervention system based on individualized music EEG neural feedback is mainly divided into two parts, control end and player two parts.Control end is as the component part of pressure state music feedback module, mainly responsible for the real-time analysis calculation of EEG signal and the real-time display of EEG feature, and simultaneously adjusts the music parameters of player according to EEG signal feature. Player is built in pressure state music feedback module, executes the operation instruction of control end to adjust music parameters, and replaces music type according to personal preference to carry out individualized music stimulation feedback training experiment.

[0067] The induced EEG acquisition module is used to collect the EEG signals of the subjects during the music neural feedback experiment. A 32-channel EEG device (Brain Products, Germany) is used for EEG data acquisition. Due to the characteristics of the device itself, TP9 and TP10 of the 32 electrodes are used as the reference, so that 30 channels of EEG signals are finally collected for real-time analysis on the feedback platform. After the EEG cap connected to the EEG device collects the EEG signals, the EEG device transmits the related EEG information to the amplifier, so that the information is easy to observe and process, providing sufficient data availability for the experiment. At the same time, the electrodes of the EEG cap adopt the international standard EEG electrode placement system 10-20 system. The EEG signals are stored in the storage in real time, the subjects are guided to sit comfortably on the chair, the seat is adjusted to be comfortable, the subjects are helped to wear the EEG cap, and the subjects are helped to wear the earphones and adjust the impedance to below 15 kΩ by applying the scrubbing cream to reduce the cutin of the scalp and applying the conductive cream. The computer volume is adjusted to a reasonable range. The subjects need to sit still, and physical activities such as limb movement, tooth clenching, head shaking, and eyeball rotation are reduced.

[0068] The EEG signal preprocessing module reads the EEG signals saved by the induced EEG acquisition module every two seconds, removes the power frequency interference of the EEG signal segments by using the FIR band-pass filter, retains the required EEG signal frequency band, then provides real-time state estimation by using the extended Kalman filter, suppresses the noise and interference in the EEG signal, removes the tail of the motion component with a high amplitude such as eyeball movement, electrocardiogram, and lip movement based on ASR, so as to improve the signal-to-noise ratio of the EEG signal, and call the stress feature extraction module after preprocessing.

[0069] The stress feature extraction module extracts the stress-related features of the pure EEG signal segments obtained after the EEG preprocessing module from the selected training electrodes, specifically, calculates the power spectral density in two frequency bands of interest (Frequency of Interest, FOI): α (8-13 Hz) and β (14-30 Hz) using two selected electrode channels (F3 electrode of the left frontal lobe cortex and T8 electrode of the right temporal lobe region). The time-frequency analysis of the signal is realized by Fourier transform on the sliding window of the signal using short-time Fourier transform. This method can capture the frequency components of the signal at different time points.

[0070] The stress state music feedback module includes individual baseline threshold calculation and real-time music neural feedback.

[0071] Among them, the characteristics collected in the resting state are used as the threshold of the experimental feedback by calculating the mean value of the characteristics in the resting state, which reflects the EEG activity level of the subjects themselves and considers the difference in the EEG level that each person may have.

[0072] After calculating the mean, the mean is included in the feedback, the real-time feature value is calculated every two seconds, when the real-time feature value is greater than the mean feature value, the volume of the music will increase, that is, the brain activity level of the subject is higher than its own average state, as positive feedback, encouraging the subject to maintain or further improve this state, when the real-time feature value is less than the mean feature value, the volume of the music will decrease, indicating that the brain activity level of the subject is lower than its own average state, and the subject needs to adjust the brain state or body posture to achieve a more relaxed state. At the same time, ensure that the system volume is in the range of 0 to 100, and the subject himself feels the music size during the experiment, and the subject adjusts to the most relaxed state by adjusting the brain state and body posture. The whole training is carried out in a cycle, and the volume of the music neural feedback training is adjusted.

[0073] In summary, compared with other stress intervention methods, the system of the present application designs stress adjustment intervention neural feedback based on EEG signals to regulate the brain function of stress groups, provides a potential objective and effective means for mechanism exploration of stress groups, and provides a new type of non-invasive, highly individualized and long-lasting brain-computer interaction adjustment method. Individual personality and music preference characteristics are fully considered to determine the individualized music type screening of feedback training by repeated measurement variance analysis, and the individual feedback adjustment threshold is determined based on individual EEG differences. For individual neurophysiological characteristics, the music stimulation intensity is optimized to realize individualization of music neural feedback indicators, which helps to improve the targeting and effectiveness of individual neural feedback training. In summary, the present application is particularly designed for the health problems caused by excessive stress in stress groups, and a passive stress adjustment intervention system based on individualized music EEG neural feedback is designed, which has potential clinical value and social value.

[0074] Embodiment 2

[0075] The present application aims at the drawbacks of slow efficacy and significant individual differences of existing stress management, and proposes a passive stress adjustment method based on individualized music EEG neural feedback, which uses brain-computer interface combined with music therapy technology to adjust stress brain activity characteristics passively according to personal preferences and electrophysiological characteristics, and assists clinical or family in adjusting stress and other adverse psychological and emotional activities.

[0076] In order to better achieve the above technical purposes, the embodiment of the present application also provides a passive stress adjustment intervention method based on individualized music EEG neural feedback, comprising:

[0077] Collecting the EEG signals of the subjects in the music neural feedback experiment; and pre-processing the EEG signals;

[0078] Extracting the stress EEG features of the pre-processed EEG signals;

[0079] The stress EEG features are analyzed to represent the individualized stress state of the subject, and individualized music feedback is generated according to the analysis result, so as to guide the subject to adjust the stress state through iterative training intervention.

[0080] As some embodiments, the individual music preference type is obtained according to the individual EEG features and individualized preferences, so as to support the individualized music feedback.

[0081] Specifically, as shown in Figure 3 The embodiment of the present application provides a passive stress regulation intervention method based on individualized music EEG neural feedback, a closed-loop real-time data processing flow, which comprises the following steps:

[0082] Collecting the EEG signals of the subject induced by different music stimuli;

[0083] Real-time preprocessing, processing according to FIR band-pass filtering, extended Kalman filtering and ASR correction;

[0084] Calculating the EEG features after preprocessing, first obtaining the individual baseline threshold, and then calculating the real-time stress features;

[0085] Adjusting the music stimulation parameters according to the real-time stress features.

[0086] The method corresponds to the data processing flow of the system described above, and will not be repeated here.

[0087] The above is only the preferred specific embodiment of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A passive-active stress regulation system based on individualized music electroencephalogram neurofeedback, characterized in that, include: The system includes an evoked EEG acquisition module, an EEG signal preprocessing module, a stress feature extraction module, and a stress state music feedback module, which are connected sequentially. Specifically, the experiment involves acquiring EEG signals from subjects during music neurofeedback experiments using an induced EEG acquisition module, preprocessing the EEG signals using an EEG signal preprocessing module, extracting features from the preprocessed EEG signals using a stress feature extraction module to obtain stress EEG features, and performing individual baseline threshold analysis on the stress EEG features using a stress state music feedback module to obtain individualized music feedback for real-time adjustment of the music played to the subjects, thus guiding iterative training for the subjects' stress state regulation. In the EEG signal preprocessing module, the preprocessing process includes: electrode localization, rereference, filtering, electromyography and electrooculography removal processing, and independent principal component analysis; In the stress feature extraction module, the process of feature extraction of the preprocessed EEG signal includes: splitting the preprocessed EEG signal into short signal segments, performing windowing and Fourier transform on each short signal segment to obtain the power spectrum estimate of each signal segment, and calculating the average value of the power spectrum estimate of each signal segment to obtain the average power spectrum estimate, i.e., stress EEG feature. In the stress state music feedback module, the process of performing individual baseline threshold analysis includes: An individual baseline threshold is obtained, and the stress EEG feature is judged based on the individual baseline threshold. When the stress EEG feature is greater than the individual baseline threshold, the individualized music feedback is to increase the music volume; when the stress EEG feature is less than the individual baseline threshold, the individualized music feedback is to decrease the music volume. The process of obtaining the individual baseline threshold includes: Resting-state EEG signals are acquired through an evoked EEG acquisition module, preprocessed through an EEG signal preprocessing module, and feature extracted from the preprocessed EEG signals through a pressure feature extraction module to obtain resting-state EEG features. The mean of the resting-state EEG features is calculated through the pressure state music feedback module to obtain the mean of the resting-state EEG features, which is the individual baseline threshold. It also includes a personalized music stimulation module, which is connected to the stress state music feedback module. Based on the individual EEG characteristics and individual preference scores of the subjects, the individual music preference type is obtained. The individual preference score is obtained through numerical input, and the individual EEG characteristics are obtained by preprocessing and feature extraction of the evoked EEG signals corresponding to different music types. The obtained individual music preference type is used in the stress state music neurofeedback module.

2. A method of active and passive stress regulation based on individualized music EEG neurofeedback, said method being implemented based on the system as claimed in claim 1, characterized in that, include: The brain electrical signals of the subjects are collected for a music neurofeedback experiment, the brain electrical signals are preprocessed, the brain electrical signals after the preprocessing are subjected to feature extraction, stress brain electrical features are obtained, individual baseline threshold analysis is performed on the stress brain electrical features, and individualized music feedback is obtained to adjust the music played to the subjects in real time, and to guide the iterative training of the stress state adjustment of the subjects.

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