Active and passive pressure adjusting system and method based on individualized music electroencephalogram neural feedback

Abstract

The invention discloses an active and passive pressure regulation system and method based on individualized music electroencephalogram neural feedback, and the system comprises the steps: collecting an electroencephalogram signal of a subject in a music neural feedback experiment through an evoked electroencephalogram collection module, and carrying out the preprocessing of the electroencephalogram signal through an electroencephalogram signal preprocessing module; the method comprises the following steps: preprocessing electroencephalogram signals, performing feature extraction on the preprocessed electroencephalogram signals through a pressure feature extraction module to obtain pressure electroencephalogram features, and performing individual baseline threshold analysis on the pressure electroencephalogram features through a pressure state music feedback module to obtain individual music feedback so as to perform real-time adjustment on music played to a testee. And the pressure state adjustment iteration training of the testee is guided.

Description

Technical Field

[0001] The present invention belongs to the technical field of non-invasive brain function neural regulation, and in particular relates to an active and passive pressure regulation system and method based on individualized music electroencephalogram neural feedback. Background Art

[0002] In the current social context, people are generally under various pressures. According to data from the World Health Organization, the problem of excessive stress is becoming increasingly prominent and is showing a trend of younger age. Studies have shown that stress activates the hypothalamus-pituitary-adrenal axis of the brain, causing the adrenal cortex to release cortisol, which in turn activates the sympathetic nervous system, causing physiological reactions such as heart rate, blood pressure and breathing. These reactions help to cope with stress in the short term, but if they last too long, they will cause certain harm to the body and may cause diseases such as heart disease, hypertension, diabetes, depression and anxiety, and affect the function of the immune system. Existing stress management methods mainly include proper rest, healthy diet, reasonable exercise and psychological counseling, but the effects of these methods are relatively slow and there are significant individual differences, and there is a lack of subjective and objective evaluation systems. Therefore, there is an urgent need for personalized, long-lasting and effective stress management approaches.

[0003] EEG signal characteristics are closely related to individual emotional states and stress levels. Neurofeedback and closed-loop regulation have been widely verified in stress regulation at home and abroad. Existing studies have pointed out that people under stress will experience abnormal changes in EEG. Excessive stress levels can lead to abnormalities in the α frequency band and changes in the time domain parameters of EEG microstates. Therefore, exploring stress mechanisms at the EEG level and conducting neuroregulation provides a potential new and effective way to manage stress.

[0004] Music has the ability to change emotions, change cognition through emotions, release negative emotions of subjects, stimulate more positive emotions, relieve stress, and thus achieve the effect of improving mental health. Neurofeedback is a technology that realizes individual self-regulation through real-time feedback of neural activity information. According to the individual's physiological characteristics and needs, it helps individuals reduce anxiety and stress, better understand and regulate individual emotions, and improve attention through training. Among the many types of feedback, music has become an important part of neurofeedback because of its easy perception and emotional influence. Music neurofeedback mainly achieves neural regulation by adjusting the level of music parameters as neural signals change. Existing music neurofeedback research has achieved positive results in reducing anxiety, emotional regulation, improving cognitive function, sleep quality, and reducing depression. Its advantages of non-invasiveness, personalization, and long duration can maximize the efficiency of regulation, reduce the side effects caused by treatment, and conduct individualized and effective regulation for stressed people. However, the existing technology has the disadvantages of slow regulation of existing stress management and significant individual differences. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes an active and passive pressure regulation system and method based on individualized music EEG neural feedback to solve the problems existing in the above-mentioned prior art.

[0006] To achieve the above object, the present invention provides an active and passive pressure regulation system based on individualized music EEG neural feedback, comprising:

[0007] An induced EEG acquisition module, an EEG signal preprocessing module, a stress feature extraction module, and a stress state music feedback module, wherein the induced EEG acquisition module, the EEG signal preprocessing module, the stress feature extraction module, and the stress state music feedback module are connected in sequence;

[0008] Among them, the EEG signals of the subjects conducting the music neural feedback experiment are collected through the induced EEG acquisition module, the EEG signals are preprocessed through the EEG signal preprocessing module, and the features of the preprocessed EEG signals are extracted through the stress feature extraction module to obtain the stress EEG features. The stress EEG features are analyzed for individual baseline thresholds through the stress state music feedback module to obtain individualized music feedback so as to make real-time adjustments to the music played to the subjects, thereby providing guidance for the iterative training of the subjects' stress state regulation.

[0009] Optionally, in the EEG signal preprocessing module, the preprocessing process includes: electrode positioning, re-referencing, filtering, myoelectric and oculoelectric removal processing, and independent principal component analysis.

[0010] Optionally, in the stress feature extraction module, the process of extracting features from the preprocessed EEG signal includes: splitting the preprocessed EEG signal to obtain short-segment signals, windowing and Fourier transforming each short-segment signal to obtain a power spectrum estimate for each signal segment, and averaging the power spectrum estimate for each signal segment to obtain an average power spectrum estimate, i.e., the stress EEG feature.

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

[0012] An individual baseline threshold is obtained, and the stress EEG characteristic is judged according to the individual baseline threshold. When the stress EEG characteristic is greater than the individual baseline threshold, the individualized music feedback is to increase the volume of the music; when the stress EEG characteristic is less than the individual baseline threshold, the individualized music feedback is to reduce the volume of the music.

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

[0014] The resting EEG signals are collected by the induced EEG acquisition module, preprocessed by the EEG signal preprocessing module, and feature extraction is performed on the preprocessed EEG signals by the stress feature extraction module to obtain resting EEG features. The resting EEG features are averaged by the stress state music feedback module to obtain the average of the resting EEG features, i.e., the individual baseline threshold.

[0015] Optionally, the system also includes an individualized music stimulation module, wherein the individualized music stimulation module is connected to the stress state music neurofeedback module, and an individual music preference type is obtained according to the individual EEG characteristics and individualized preference scores of the subjects, wherein the individualized preference scores are obtained through numerical input, and the individual EEG characteristics are obtained by preprocessing and feature extracting evoked EEG signals corresponding to different music types in sequence, and the obtained individual music preference type is used in the stress state music neurofeedback module.

[0016] In order to better achieve the above technical objectives, the present invention provides an active and passive stress regulation method based on personalized music EEG neural feedback, including: collecting EEG signals of subjects performing music neural feedback experiments, preprocessing the EEG signals, extracting features of the preprocessed EEG signals to obtain stress EEG features, performing individual baseline threshold analysis on the stress EEG features, obtaining personalized music feedback to make real-time adjustments to the music played to the subjects, and guiding iterative training for the subjects' stress state regulation.

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

[0018] The present invention specifically targets the stress brain activity characteristics of people in existing adverse psychological states, and designs an active and passive stress regulation system based on individualized music EEG neural feedback using brain-computer interface technology.

[0019] The present invention proposes an individualized music stimulation module to solve the problem that music neurofeedback is not effective due to differences in EEG rhythms between individuals and standardized solutions cannot meet individual needs. The feedback music type is selected based on EEG characteristics and individual preferences, and dynamic multi-dimensional subjective and objective indicators are provided to evaluate changes in individual stress brain conditions, reveal the individual stress brain mechanism of the subjects, and provide an effective solution for individualized music neurofeedback.

[0020] The present invention introduces a music feedback stimulation mode to achieve efficient regulation of the user's active and passive stress levels. The passive music stimulation type is selected based on EEG characteristics and individual preferences. The feedback music parameters are modified in real time based on the stress EEG characteristics. Through active closed-loop feedback, stress-related brain characteristics are targeted and regulated. The degree of individual relaxation is adjusted through adaptive feedback, which can quickly and non-invasively regulate the subject's stress level. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0022] Figure 1 It is a structural schematic diagram of an active and passive pressure regulation intervention system based on individualized music EEG neural feedback according to an embodiment of the present invention;

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

[0024] Figure 3 This is a flow chart of active and passive closed-loop real-time data processing and music neural feedback according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. 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 flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] Example 1

[0028] The purpose of the present invention is to address the shortcomings of existing stress management, such as slow efficacy and significant individual differences, and propose an active and passive stress regulation system based on personalized music EEG neural feedback. It uses brain-computer interface combined with music therapy technology to actively and passively regulate stress brain activity characteristics according to personal preferences and electrophysiological characteristics, and assist clinicians or families in regulating adverse psychological and emotional activities such as stress.

[0029] To achieve the above purpose, Figure 1 As shown, the present invention provides an active and passive pressure regulation system based on individualized music EEG neural feedback, comprising:

[0030] The induced EEG acquisition module, the EEG signal preprocessing module, the stress feature extraction module, and the stress state music feedback module are connected in sequence, and the stress state music feedback module provides feedback guidance to the subjects, and the subjects adjust their stress state autonomously according to the feedback guidance, thereby inducing the EEG signals collected by the induced EEG acquisition module to adjust; specifically:

[0031] The induced EEG acquisition module is used to collect EEG signals of subjects in the music neurofeedback experiment;

[0032] An EEG signal preprocessing module, used for preprocessing EEG signals;

[0033] A pressure feature extraction module, used to extract pressure EEG features of the preprocessed EEG signal;

[0034] The stress state music feedback module is used to analyze the stress EEG characteristics to characterize the individual stress state of the subject, and generate individualized music feedback based on the analysis results. The individualized music feedback is used as a guide to adjust the subject's stress condition through iterative training intervention.

[0035] As some embodiments, the system provided by the present invention also includes an individualized music stimulation module, which is used to analyze and obtain the individual music preference type based on the individual EEG characteristics and individualized preference scores of the subjects, and is connected to the stress state music neural feedback module to support the stress state music neural feedback module.

[0036] Specifically, the individualized music stimulation module is used to select the individualized music preference type of the subjects. This module includes three parts: music preference experimental paradigm: by listening to different types of music, the subjects input the individualized preference score; music induced EEG feature extraction, by playing different types of music to the subjects, collecting the induced EEG signals of the subjects, and obtaining individual EEG features, namely stress-related spectrum features, through EEG preprocessing and feature extraction of the induced EEG signals; individualized music type screening, using the method of repeated measures variance analysis through individual preference scores and stress-related spectrum features to screen out individualized music preference types.

[0037] Specifically: The implementation process of the repeated measures variance analysis method includes: real-time collection of EEG signals when listening to music, obtaining EEG characteristics under different music states, performing repeated measures variance analysis on the EEG characteristics collected in the resting state, obtaining the statistical indicator P value used to evaluate the importance of differences between groups, and combining the individual preference score (five-point Likert scale). If there is a significant P value in the repeated measures variance analysis (P<0.05), the music type with a larger individual preference score among the significant music types is selected (if the individual preference scores are the same, the music type with a smaller P value is selected); if there is no significant P value in the repeated measures variance analysis (P≥0.05), the music type with a smaller P value among the music types with individual preference scores greater than or equal to 3 is selected (if the individual preference scores are all less than 3, the music type with the smallest P value is selected) 。

[0038] As some embodiments, the induced EEG acquisition module collects the EEG signals of the subjects in the resting state before and after the music neurofeedback experiment and during the experiment, that is, the collected EEG signals include the resting state EEG signals before the experiment, the resting state EEG signals after the experiment and the real-time EEG signals, and the individual threshold baseline uses only the individual threshold calculated from the resting state EEG before the experiment.

[0039] As some embodiments, in the EEG signal preprocessing module, the process of preprocessing the EEG signal includes: electrode positioning, re-referencing, filtering, myoelectric and oculoelectric removal processing, and independent principal component analysis to obtain the pure EEG signal of the subject.

[0040] As some embodiments, stress EEG features include: time domain and frequency domain EEG signal features related to stress state.

[0041] As some embodiments, a stress state music neurofeedback module is used to correspond the analysis results of stress EEG characteristics to music parameters; wherein, the material of the music neurofeedback is selected by the subject according to the individualized music stimulation module, and after obtaining the subject's baseline brain characteristics, i.e., the individual baseline threshold, based on the resting-state EEG signal, the stress EEG characteristics corresponding to the real-time EEG signal are analyzed according to the individual baseline threshold, and individualized music feedback is generated to provide reward and punishment feedback signals to guide the subject's individualized stress level adjustment.

[0042] The individualized stress regulation intervention system based on individualized music neurofeedback in the embodiment of the present invention can be used for regulation intervention when stress is too high, and has potential clinical value and social value.

[0043] The above technical solution is described in detail with reference to the relevant drawings.

[0044] like Figure 1 As shown, an embodiment of the present invention provides a stress regulation system based on an individualized music neural feedback system, comprising:

[0045] The induced EEG acquisition module is used to collect multi-channel EEG signals of subjects in the music neurofeedback experiment, so as to obtain the EEG signals induced by the subjects when they listen to the music stimulation of the individualized preference type, which is used for the subsequent EEG signal preprocessing module;

[0046] The EEG signal preprocessing module is used to preprocess the EEG signals, including electrode positioning, re-referencing, filtering, myoelectric and oculomotor removal, independent principal component analysis and other noise removal methods, to obtain the subjects' pure EEG signals for use in the subsequent stress feature brain feature extraction module;

[0047] The stress feature extraction module is used to extract the real-time features of the real-time EEG signals related to individual stress in real time. The stress EEG features include stress-related time domain and frequency domain EEG signal features. The stress EEG features will be used as the input of the stress state music feedback module for the subsequent use of the stress state music feedback module;

[0048] The stress state music feedback module is used to analyze stress EEG characteristics to characterize the individual stress state of the subject, and generate individualized music feedback based on the analysis results, and use individualized music feedback to adjust the stress state of the subject in real time through iterative training intervention. The individualized music feedback type selects the corresponding music type according to the individual EEG characteristics and individual preferences, provides reward and punishment feedback signals to guide the individual stress regulation of the subject, and realizes non-invasive intervention of individual stress.

[0049] As some embodiments, the system provided by the present invention also includes an individualized music stimulation module, which is used to obtain individual music preference types based on individual EEG characteristics and individualized preferences. This module includes three parts: music preference experimental paradigm: by listening to different types of music to the subjects, the subjects input and give individualized preference scores; music induced EEG feature extraction, by playing different types of music to the subjects, collecting the induced EEG signals of the subjects, and obtaining individual EEG characteristics, i.e., stress-related spectral features, through EEG preprocessing and feature extraction of the induced EEG signals; individualized music type screening, using the individual preference scores and stress-related spectral features using the repeated measures variance analysis method to screen out individualized music preference types.

[0050] In the above content, the music-induced EEG feature extraction can be performed by preprocessing and feature extraction of EEG signals induced by different music types through the induced EEG acquisition module, EEG signal preprocessing module and pressure feature extraction module of the present invention to obtain individual EEG features.

[0051] Specifically, the active and passive pressure regulation system provided by the present invention includes four online modules (induced EEG acquisition module, EEG signal preprocessing module, pressure feature extraction module, pressure state music feedback module) and one offline module (individualized music stimulation module), wherein:

[0052] As some embodiments, the evoked EEG acquisition module collects 32-channel signals of the subjects 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. Real-time data streaming (Lab Streaming Layer, LSL) technology is used to synchronize the EEG signals in the EEG acquisition device to the individualized music neurofeedback system in real time, and store the real-time collected EEG signals in the memory for subsequent EEG signal processing.

[0053] As some embodiments, the EEG signal preprocessing module performs real-time preprocessing on the collected multi-channel EEG signals, selects TP9 and TP10 channels in the multi-channel as reference electrodes, removes power frequency interference through a 0.5-45Hz finite impulse response filter (Finite Impulse Response, FIR) bandpass filter, retains the required EEG signal frequency band, and then uses the extended Kalman filter to provide real-time state estimation, suppresses noise and interference in the EEG signal, has adaptive effects, and dynamically adjusts the filter parameters according to the characteristics of the EEG signal to achieve the optimal filtering effect. The artifact subspace reconstruction method (Artifact Subspace Reconstruction, ASR) mainly relies on independent principal component analysis, aiming to remove the tails of high-amplitude motion components such as eye movement, electrocardiogram, and lip movement. By calculating the mean square value in the sliding window, calibration data is obtained to eliminate signals that do not meet the standards. The pseudo shadow space reconstruction method can effectively remove abnormal signals, and at the same time, the operation speed is fast and the computational complexity is low. Through the sequential preprocessing method, a pure EEG signal can be obtained.

[0054] As some embodiments, the stress feature extraction module extracts stress EEG features from pure stress-related EEG signal fragments obtained after passing through 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 stress EEG feature to better understand the working mechanism of the brain.

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

[0056] Among them, the power spectrum estimation formula of each signal is as follows:

[0057]

[0058] in, is the power spectrum estimate of the i-th signal, M is the length of each signal 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 sequence, d(n) has the characteristics of a smooth window function, and a suitable non-rectangular window is used for signal processing to reduce the impact of non-target frequency components and spectrum 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 segments, each with a length of M. The power spectrum of all signal sequences is estimated. The specific calculation process is shown in formula (3).

[0061]

[0062] As some embodiments, the stress state music feedback module is mainly implemented 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 calculation of individual baseline threshold, this module preprocesses and extracts features of the resting EEG signals based on the EEG signals collected in the resting state to generate resting EEG features. Starting from the resting EEG features, the module calculates the mean of the resting EEG features as the threshold for experimental feedback, namely the individual baseline threshold, which reflects the subject's own EEG activity level. This process takes into account the differences in EEG activity levels that may occur in each person.

[0064] Specifically, in real-time music neurofeedback, after calculating the mean of the resting EEG features, the mean is included in the individualized music feedback as the individual baseline threshold, and the real-time EEG signal is preprocessed and feature extracted to obtain the 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 EEG features, the volume of the music will increase, that is, the subject's brain activity level is higher than its own average state. As positive feedback, the subject is guided to maintain or further improve this state; when the real-time feature value is less than the mean of the resting EEG features, the volume of the music will decrease, indicating that the subject's brain activity level 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 within the range of 0 to 100. During the experiment, the subject feels the volume of the music completely by himself, and the subject adjusts the brain state and body posture to the most relaxed state. The whole training is carried out in a cycle, with the volume adjustment of the music neurofeedback training.

[0065] This embodiment provides an active and passive stress regulation intervention system based on personalized music EEG neural feedback, which is developed using MATLAB language and runs on a device installed on a Windows system. It includes an induced EEG acquisition module, an EEG signal preprocessing module, a stress feature extraction module, and a stress state music feedback module. Among them, the personalized music stimulation module is implemented through three functions: offline music preference experimental paradigm, music induced EEG, and personalized music type screening, which is used to obtain individual music preference types based on individual EEG characteristics and individual preferences.

[0066] See also Figure 2 As shown in the figure, the left picture is the music neurofeedback control terminal interface, which is used by the experimenter to monitor the EEG signal state of the subject and control the experimental process, and the right picture is the music player interface, which is used to play individualized music types; the active and passive stress condition intervention system based on individualized music EEG neurofeedback is mainly divided into two parts, the control terminal and the player. As a component of the stress state music feedback module, the control terminal is mainly responsible for the real-time analysis and calculation of EEG signals and the real-time display of EEG characteristics, and adjusts the music parameters of the player according to the EEG signal characteristics. The player is built into the stress state music feedback module, executes the operation instructions of the control terminal to adjust the music parameters, and changes the music type according to personal preferences to conduct individualized music stimulation feedback training experiments.

[0067] The induced EEG acquisition module is used to collect EEG signals of the subjects during the music neurofeedback experiment, and a 32-lead EEG device (BrainProducts, 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 references. Therefore, the EEG signals of 30 channels are finally collected and included in the feedback platform for real-time analysis. After the EEG cap connected to the EEG device collects the EEG signal, the EEG device will transmit the relevant EEG information to the amplifier, making the information 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 method 10-20 system. The EEG signals are stored in the memory in real time. The subjects are guided to sit comfortably on the chair, which is adjusted to be comfortable. The subjects are assisted to wear the EEG cap, apply scrub to reduce scalp cuticle, and apply conductive paste to adjust the impedance to below 15 kilo-ohms. The subjects are assisted to wear headphones and adjust the computer volume to a reasonable range. The subjects are required to sit quietly and reduce physical activities such as limb movement, clenching teeth, shaking head, and eye movement.

[0068] The EEG signal preprocessing module reads the EEG signal saved by the induced EEG acquisition module once every two seconds, uses FIR bandpass filtering to remove power frequency interference from the EEG signal fragments and retains the required EEG signal frequency band, and then uses extended Kalman filtering to provide real-time state estimation, suppress noise and interference in the EEG signal, and removes the tails of high-amplitude motion components such as eye movement, electrocardiogram, and lip movement based on ASR to improve the signal-to-noise ratio of the EEG signal. After preprocessing, the pressure feature extraction module is called.

[0069] The pressure feature extraction module extracts the pressure brain point signal of the selected training electrode, i.e., the pressure-related features in the EEG signal, from the fragment of the pure EEG signal obtained after the EEG preprocessing module. Specifically, two selected electrode channels (the F3 electrode of the left prefrontal cortex and the T8 electrode of the right temporal lobe) are used to calculate the power spectral density in two frequency bands of interest (Frequency of Insterest, FOI): α (8-13Hz) and β (14-30Hz). Short-time Fourier transform is used to perform Fourier transform on the sliding window of the signal to achieve time-frequency analysis of the signal. 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, starting from the features collected in the resting state, by calculating the mean of the features in the resting state as the threshold of the experimental feedback, it reflects the subject's own EEG activity level and takes into account the differences in EEG levels that may occur in each person.

[0072] After calculating the mean, the mean is included in the feedback. The real-time eigenvalue is calculated every two seconds. When the real-time eigenvalue is greater than the mean eigenvalue, the volume of the music will increase, that is, the subject's brain activity level is higher than its own average state. As positive feedback, the subject is encouraged to maintain or further improve this state; when the real-time eigenvalue is less than the mean eigenvalue, the volume of the music will decrease, indicating that the subject's brain activity level 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 within the range of 0 to 100. During the experiment, the subject feels the volume of the music completely. The subject adjusts the brain state and body posture to the most relaxed state. The entire training is carried out in a cycle, with the volume adjustment of the music neurofeedback training.

[0073] In summary, compared with other stress intervention methods, the system of the present invention designs stress regulation intervention neurofeedback based on EEG signals to regulate brain function of stressed people, which provides a potential objective and effective means for the mechanism exploration of stressed people, and provides a new type of brain-computer interaction adjustment method that is non-invasive, highly individualized, and has lasting effects. The individual personality and music preference characteristics are fully considered to combine repeated measures variance analysis to determine the individualized music type screening for feedback training, and the individual feedback adjustment threshold is determined based on individual EEG differences. According to individual neurophysiological characteristics, the intensity of music stimulation is optimized, and the individualization of music neurofeedback indicators is realized, which helps to improve the targeting and effectiveness of individual neurofeedback training. In short, the present invention is especially aimed at the health problems caused by excessive stress in stressed people, and designs an active and passive stress regulation intervention system based on individual music EEG neurofeedback, which has potential clinical value and social value.

[0074] Example 2

[0075] The purpose of the present invention is to address the shortcomings of existing stress management, such as slow efficacy and significant individual differences, and propose an active and passive stress regulation method based on individualized music EEG neural feedback. By using brain-computer interface combined with music therapy technology, the characteristics of stress brain activity are actively and passively regulated according to personal preferences and electrophysiological characteristics, to assist clinicians or families in regulating adverse psychological and emotional activities such as stress.

[0076] In order to better achieve the above technical objectives, the embodiment of the present invention also provides an active and passive pressure regulation intervention method based on individualized music EEG neural feedback, including:

[0077] Collect the EEG signals of the subjects in the music neurofeedback experiment; pre-process the EEG signals;

[0078] Extracting stress EEG features of the preprocessed EEG signal;

[0079] The stress EEG characteristics are analyzed to characterize the individual stress state of the subjects. Individualized music feedback is generated based on the analysis results. The individualized music feedback is used as a guide to regulate the stress state of the subjects through iterative training intervention.

[0080] As some embodiments, an individual music preference type is obtained based on individual EEG characteristics and individual preferences to support individualized music feedback.

[0081] Specifically, Figure 3 As shown, the embodiment of the present invention provides an active and passive pressure regulation intervention method based on individualized music EEG neural feedback, and a closed-loop real-time data processing flow, including:

[0082] Collect the EEG signals of the subjects induced by different music stimuli;

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

[0084] Calculate the preprocessed EEG features, first obtain the individual baseline threshold, and then calculate the real-time stress features;

[0085] Adjust the music stimulation parameters according to the real-time pressure characteristics.

[0086] This method corresponds to the data processing flow of the above-mentioned system and will not be described in detail here.

[0087] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An active and passive pressure regulation system based on individualized music EEG neural feedback, characterized in that: include: An induced EEG acquisition module, an EEG signal preprocessing module, a stress feature extraction module, and a stress state music feedback module, wherein the induced EEG acquisition module, the EEG signal preprocessing module, the stress feature extraction module, and the stress state music feedback module are connected in sequence; Among them, the EEG signals of the subjects conducting the music neural feedback experiment are collected through the induced EEG acquisition module, the EEG signals are preprocessed through the EEG signal preprocessing module, and the features of the preprocessed EEG signals are extracted through the stress feature extraction module to obtain the stress EEG features. The stress EEG features are analyzed for individual baseline thresholds through the stress state music feedback module to obtain individualized music feedback so as to make real-time adjustments to the music played to the subjects, thereby providing guidance for the iterative training of the subjects' stress state regulation.

2. The system according to claim 1, characterized in that In the EEG signal preprocessing module, the preprocessing process includes: electrode positioning, re-referencing, filtering, myoelectric and oculoelectric removal processing, and independent principal component analysis.

3. The system according to claim 1, characterized in that In the stress feature extraction module, the process of extracting features from the preprocessed EEG signal includes: splitting the preprocessed EEG signal to obtain short-segment signals, windowing and Fourier transforming each short-segment signal to obtain a power spectrum estimate for each signal segment, and averaging the power spectrum estimate for each signal segment to obtain an average power spectrum estimate, i.e., the stress EEG feature.

4. The system according to claim 1, characterized in that 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 characteristic is judged according to the individual baseline threshold. When the stress EEG characteristic is greater than the individual baseline threshold, the individualized music feedback is to increase the volume of the music; when the stress EEG characteristic is less than the individual baseline threshold, the individualized music feedback is to reduce the volume of the music.

5. The system according to claim 1, characterized in that The process of obtaining the individual baseline threshold comprises: The resting EEG signals are collected by the induced EEG acquisition module, preprocessed by the EEG signal preprocessing module, and feature extraction is performed on the preprocessed EEG signals by the stress feature extraction module to obtain resting EEG features. The resting EEG features are averaged by the stress state music feedback module to obtain the average of the resting EEG features, i.e., the individual baseline threshold.

6. The system according to claim 1, characterized in that It also includes an individualized music stimulation module, wherein the individualized music stimulation module is connected to the stress state music neural feedback module, and an individual music preference type is obtained according to the individual EEG characteristics and individualized preference scores of the subjects, wherein the individualized preference scores are obtained through numerical input, and the individual EEG characteristics are obtained by sequentially preprocessing and feature extracting induced EEG signals corresponding to different music types, and the obtained individual music preference type is used in the stress state music neural feedback module.

7. An active and passive pressure regulation method based on individualized music EEG neural feedback, characterized in that: include: The EEG signals of the subjects in the music neurofeedback experiment are collected, the EEG signals are preprocessed, and features are extracted from the preprocessed EEG signals to obtain stress EEG features. Individual baseline threshold analysis is performed on the stress EEG features to obtain individualized music feedback so as to make real-time adjustments to the music played to the subjects, thereby providing guidance for iterative training on the subjects' stress state regulation.

Images