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    • 专利摘要:
    The present invention proposes a system for regulating a depressed mood by music feedback based on an electroencephalogram (EEG) signal. The present invention analyzes a correspondence relationship between electrical signals and music signals and carries out a corresponding feedback music training for a depressed patient in order to achieve the goal of improving the mood of the depressed patient. The system includes an EEG signal acquisition module, an EEG signal data processing module, a feedback music generation module, a feedback training regulation module, and a data storage / analysis module. The EEG signal acquisition module is designed to receive an EEG resting signal from the depressed patient. The EEG signal data processing module is designed to process the captured EEG signal. The feedback music generation module is designed to segment and integrate the processed EEG signal, to analyze an association between the EEG signal and a music signal, and to compare it in a reference library created for feedback music types in order to obtain a feedback music type to the music -Get feedback training. The feedback training regulation module is designed to use feedback music matching the feedback music type in order to carry out feedback training for the depressed patient in order to achieve the regulation of the depressed mood. The data storage / analysis module is designed to store and analyze a course and results of the emotional regulation of the person.
    公开号:CH717003A2
    申请号:CH01418/20
    申请日:2020-11-04
    公开日:2021-06-30
    发明作者:Hu Bin；Cai Hanshu；Xiao Han
    申请人:Univ Lanzhou；
    IPC主号:
    专利说明:

    TECHNICAL AREA
    The present invention relates to a system for regulating a depressed mood by music feedback based on an electroencephalogram (EEG) signal and belongs to the technical field of medical auxiliary systems.
    BACKGROUND
    When the nerves of the brain are active, weak electrical field fluctuations are generated. When tens of thousands of nerves are active at the same time, the electric field fluctuations are rhythmic. These are brain waves that can be measured on the scalp. Brain wave waves are a spontaneous electrical potential activity that is generated by cranial nerve activity and is always present in the central nervous system. The electroencephalogram (EEG) signal is a weak signal with poor anti-interference behavior and poor robustness. It is random, unsteady, non-Gaussian, non-linear and reflects the state and changes in the nervous system.
    Since the EEG signal reflects human emotional changes in real time, it can be examined to understand the mechanism of brain activity and cognitive processes of humans and to diagnose brain diseases, etc. The EEG rest signal is mostly used for comparative analysis between mentally ill Patients and healthy people, and the event-related EEG signal is mostly used to study changes in a patient's cognitive function. Therefore, the physical and mental state of a person can be monitored through the acquisition and analysis of the EEG signal.
    Modern music therapy originated in the United States and is a broad applied discipline that integrates music, medicine, and psychology. According to the book Defining Music Therapy published in 1989 by Professor K. Bruscia, a famous music therapist at Temple University in the United States, music therapy is a systematic intervention process in which the therapist experiences various forms of music and the therapeutic relationship that develops during the course of therapy (as a therapeutic force) to help the client achieve the goal of health.
    In music therapy, receptive, improvisational and recreational methods are used. Recreational music therapy requires the client to listen to music and personally participate in various music-related activities. Playing instruments and singing does not require the client to have completed musical training or have musical skills. On the contrary, recreational music therapy is intended for people without musical skills.
    As a psychotherapy approach, music therapy follows the same therapeutic principles as general psychotherapy, such as confidentiality and friendship. Recreational music therapy still has to follow some special therapeutic principles. a. Gradual progress. The selection and playback of music should be gradual according to the psychological characteristics of the client. b. Learning and inspiration. During music therapy, a client who does not understand anything about music is instructed and informed about the background of the musical creation and the artistic conception that the musician expresses. c. Experience. Music is used to create an atmosphere for the client in which he can attentively experience his emotions or feelings.
    Music therapy is not a random and isolated intervention process, but a rigorous, scientific and systematic intervention process that includes the creation and implementation of long-term and short-term therapy plans and the evaluation of therapeutic effects. Music therapy is the use of all music-related activities, including listening, singing, playing musical instruments, making music, and other artistic activities, not just listening to music. The music therapy process must have three factors, namely the music, the client and a specially trained music therapist.
    Depression is the main type of mood disorder. In severe cases, psychotic symptoms such as hallucinations and delusions can occur. There are many therapeutic methods for mental and medical illnesses, but they have deficits. 1) Existing therapies for depression include Chinese and Western drugs and psychological therapy, all of which have side effects. Common psychotherapy for depression, such as cognitive behavioral therapy, ignores the patient's life and emotional experience, emphasizes rationality and objectivity too much, and is dominated by the therapist during the therapeutic process. 2) Under normal circumstances, EEG-based music therapy consists mainly of interfering with the patient's emotions by instructing the patient to listen to music. This type of hearing-related music therapy is called receptive music therapy. Because people have different stress responses to music, this method is not universal. In addition, this method only performs a low-level intervention, which can easily render the patient "immune", which does not produce good results. 3) The devices for EEG signal acquisition are not universal. A medical EEG signal acquisition device is complicated and expensive and requires special personnel to operate. A portable EEG signal detection device differs in EEG signal detection electrodes (number and position), data transmission method, cost and scope, and has a high power consumption and a small number of analog / digital (A / D) - Conversion bits. 4) Data modeling and data analysis are not reliable. Due to insufficient modeling data, the data model is not balanced and also not effective. The EEG extraction algorithm is not able to obtain pure physiological EEG signals. A single data analysis method leads to the results of feature extraction and selection lacking representativeness and accuracy. Because of these shortcomings, it is impossible to provide rationalized adjuvant therapy based on the characteristics of the person.
    SUMMARY
    The present invention proposes a system for regulating a depressed mood by music feedback based on an electroencephalogram (EEG) signal. The present invention detects and processes depressive EEG signals, analyzes a correspondence relationship between the EEG signals and music signals, generates feedback music under various emotional audio stimuli, and performs corresponding feedback music training to achieve the purpose of improving the mood of a to improve depressed patients.
    The present invention has the following technical solutions: 1. A system for regulating a depressed mood by music feedback based on an EEG signal, with an EEG signal acquisition module, an EEG signal data processing module, a feedback music generation module, a feedback training regulation module and a data storage / analysis module, wherein the EEG signal acquisition module is configured to receive an EEG rest signal from a person; the EEG signal data processing module is designed to process the captured EEG signal; the feedback music generation module is designed to segment and integrate the processed EEG signal, to analyze an association between the EEG signal and a music signal and to compare it in a reference library created for feedback music types in order to obtain a feedback music type to the music - Receive feedback training; the feedback training regulation module is designed to use feedback music matching the feedback music type in order to carry out feedback training for the person so as to achieve the regulation of the depressed mood; the data storage / analysis module is designed to store and analyze a course and results of the emotional regulation of the person. 2. A three-pole system is used in the EEG signal acquisition module; Electrode positions are selected according to the worldwide 10-20 electrode placement system, which includes Fp1, Fp2 and Fpz, which are located on the forehead and not disturbed by hair; the electrodes are medical sticky wet electrodes that avoid the interference of electrode contact impedance. 3. The EEG signal data processing module segments the captured EEG signal as follows: dividing a captured continuous EEG signal into EEG signal segments of moderate length, and overlaying each segment of EEG data with a portion of EEG data on a previous one Segment. 4. The EEG signal data processing module performs a noise reduction and filtering on the segmented EEG signal as follows: application of an improved dynamic model parameter of an augmented reality (AR) and a wavelet analysis for noise suppression, and use of a bandpass filter with finite impulse response (FIR ) to remove a frequency band below 0.5 Hz and a frequency band above 50 Hz from the EEG signal; and dividing the EEG signal according to the frequency and extracting the waveform characteristics of four frequency bands (δ, θ, α and β) of the EEG signal. 5. The feedback music generation module segments the EEG data as follows: First, the frequency bands are represented by corresponding characters d, t, a and b, where d is the δ-band, t is the θ-band, a is the α-band and b is the represents β band; Calculating an average µ and a standard deviation σ of waveform data of each frequency band; then separately calculating a segmentation threshold for each frequency band and selecting an optimal threshold from a plurality of segmentation thresholds, the segmentation threshold being calculated by: T = µ ± n · σ, where µ represents an average of the waveform data of each frequency band after frequency division; σ represents a standard deviation of the waveform data of each frequency band after frequency division; n represents a mean to standard deviation ratio used to calculate the segmentation threshold among various mean to standard deviation ratios and to select the optimal threshold; and finally, plotting a waveform of the EEG signal through the corresponding four frequency bands, analyzing a point in time at which the waveform of each frequency band is active according to the optimal threshold value of each frequency band selected after the calculation, and obtaining a chronological relationship of the EEG- Signal corresponding to each frequency band. 6. The feedback music generation module integrates EEG segments as follows: determining frequency bands at the same time according to the chronological relationship of the EEG signal corresponding to each frequency band obtained by using a segmentation algorithm; Adding characters representing the frequency bands to a time sequence; and superimposing and arranging the characters corresponding to the four frequency bands in the sequence to form a complete EEG character sequence. 7. The feedback music generation module creates a reference library for feedback music types as follows: First, acquisition of existing depressive EEG signals of a depressed patient under positive, neutral and negative specific emotional audio stimuli, and performing a frequency division, EEG data segmentation and EEG- Segment integration on the captured EEG signals to obtain integrated EEG character sequences; Marking the integrated EEG character sequences with positive, neutral and negative emotional markings in each case according to the corresponding positive, neutral and negative emotional audio stimuli, the EEG character sequences marked with different emotional markings having specific cyclic segments throughout the EEG signal; Selecting a cyclical segment of the EEG character sequence that occurs most frequently in a certain period of time as the feedback music type according to the emotional marker, and receiving three feedback music types: positive, neutral and negative. 8. The feedback music generation module compares in the created reference library for feedback music types as follows: comparing a number of times for the EEG character segments of the positive, neutral and negative feedback music types of the created reference library for feedback music types in the EEG signal character sequence appear to the person; Determining an emotional tag of the person's EEG signal; and generating a feedback music style appropriate for the person. 9. The feedback training regulation module performs the music feedback training as follows: Selecting feedback music suitable for the feedback music type that is suitable for the person, and regulating the person's emotion into a positive and relaxed state, using an EEG- Signal character sequence with a positive marking indicates that the depressed patient has a positive physiological and psychological state, and the corresponding feedback music to be selected should be a neutral to positive musical stimulus; wherein an EEG signal sequence with a negative mark indicates that the depressed patient has a negative physiological and psychological state, and the corresponding feedback music to be selected should be a positive musical stimulus in order to improve the mood of the depressed patient towards a positive state; wherein an EEG signal character sequence with a neutral marking indicates that the depressed patient has a normal physiological and psychological state, and the corresponding feedback music to be selected should be a positive to neutral musical stimulus. 10. The feedback training regulation module performs breathing training as follows: directing the person to concentrate on breathing, and counting down with a difference according to a breathing rhythm until the counting down ends.
    The present invention has the following technical effects: The present invention proposes a system for regulating a depressed mood by music feedback based on an EEG signal. Based on the principles of music therapy, the present invention acquires, processes and analyzes EEG signals in real time, maps processed EEG data segments onto music signals (in order to convert the EEG signals into music signals) and conducts a corresponding feedback music training according to the various emotional levels Audio stimuli received through feedback types of music. In this way the present invention achieves the purpose of improving the mood of a depressed patient.
    BRIEF DESCRIPTION OF THE DRAWINGS
    FIG. 1 is a structural diagram of a system for regulating a depressed mood by music feedback based on an electroencephalogram (EEG) signal according to the present invention. FIG. Figure 2 shows the 10-20 international system and an electrode position map used by the present invention. FIG. 3 is a schematic illustration of a segmental acquisition and processing of EEG data. FIG. Figure 4 is a flow chart of creating a reference library for feedback music types. FIG. Figure 5 is a schematic illustration of EEG signal segmentation. FIG. 6 is a schematic illustration of EEG signal integration. FIG. 7 is a flowchart of the system for regulating a depressed mood by music feedback based on an EEG signal according to the present invention. FIG. Fig. 8 is a diagram showing a mechanism of action of a hypothalamic-pituitary-adrenal (HPA) axis. FIG. 9 shows a mechanism of action of music feedback for regulating a mood of a depressed patient.
    DETAILED DESCRIPTION
    The present invention is described in more detail below with reference to the accompanying drawings.
    FIG. 1 is a structural diagram of a system for regulating a depressed mood by music feedback based on an electroencephalogram (EEG) signal according to the present invention.
    The system for regulating a depressed mood by music feedback based on an EEG signal comprises an EEG signal acquisition module, an EEG signal data processing module, a feedback music generation module, a feedback training regulation module and a data storage / analysis module. The EEG signal acquisition module is designed to receive an EEG rest signal from a person. The EEG signal data processing module is designed to process the captured EEG signal. The feedback music generation module is designed to segment and integrate the processed EEG signal, to analyze an association between the EEG signal and a music signal, and to compare it in a reference library created for feedback music types in order to obtain a feedback music type to the music - Receive feedback training. The feedback training regulation module is designed to use feedback music matching the feedback music type in order to carry out feedback training for the person in order to achieve the regulation of the depressed mood. The data storage / analysis module is designed to store and analyze a course and results of the emotional regulation of the person.
    The EEG signal acquisition module uses a three-pole universal EEG acquisition system (associated patent no .: CN201520628152.6) to acquire EEG signals. Compared to a conventional EEG acquisition system, the universal EEG acquisition system acquires the EEG anytime and anywhere, with fewer wires, a simpler acquisition process, faster EEG signal processing, with fewer resources and no complicated tasks to induce an EEG.
    In this example, the placement positions of the electrodes for EEG detection are shown in FIG. 2, which refer to the worldwide 10-20 system. Since the prefrontal brain area has a strong correlation with emotional changes and mental illness, the electrode positions Fp1, Fp2 and Fpz are selected, which are located on the forehead and are not disturbed by hair. Medical sticky wet electrodes are used that avoid the interference of electrode contact impedance. The EEG signals are recorded by the electrodes on Fp1, Fp2 and Fpz and transmitted to the data processing module via a Bluetooth transmitter.
    The EEG signal data processing module is designed to process the captured EEG signals. First, the captured EEG signal is segmented. The feedback music must have continuity, rhythm and a timeline to reflect the physiological state of the depressed patient in real time. Therefore, the length of the original EEG signal, which is processed in real time each time, should be moderate. If the intercepted EEG signal is too long, the music segment that is converted from the EEG data after processing will have a deficient timeline and can no longer accurately reproduce the real-time physiological state of the depressed patient. If the intercepted EEG signal is too short, it cannot effectively reflect the physiological state of the patient, causing the converted music segment to lose continuity, rhythm and effectiveness, thereby reducing the emotional regulation effect of the feedback training. In the present invention, a 6s long EEG signal is processed each time. The first time, raw EEG data for 6 seconds are processed. From then on, the last 3s long data recorded will be superimposed on each processing. This means that the 6s long data that are processed each time after the first time contain 3s long EEG raw data that will be collected this time and in the second half 3s long EEG raw data that was collected the last time as shown in FIG. 3 shown. In this way, the 6s long data is used to determine the physiological state of the depressed patient, and the state is converted into 3s feedback music. The music feedback of the depressed patient is performed every 3s, which ensures the effectiveness of the music and the timeline of the feedback information.
    In the course of the EEG signal acquisition, it is easily possible due to the interference of environmental factors that certain noise signals are generated that have nothing to do with the physiological state. In addition, EEG data acquisition is more or less influenced by eye movements. Therefore, the EEG signal data processing module uses an improved dynamic augmented reality (AR) model parameter and wavelet analysis for noise suppression on the captured EEG signal, and it uses a finite impulse response (FIR) bandpass filter to cut a frequency band below 0.5 Hz and a frequency band above 50 Hz in the EEG signal. The EEG signal data processing module further divides the EEG signal by frequency and extracts the waveform characteristics of four frequency bands (δ, θ, α and β) of the EEG signal. The EEG signals of each line, which are marked with an emotional marker, are processed in four frequency bands: δ-band, θ-band, α-band and β-band.
    Δ wave: 1-3 Hz, with an amplitude of 20-200 µV, which mainly reflects the state of a person in deep sleep or a severe organic brain disease.
    [0021] θ wave: 4-7 Hz, with an amplitude of 10-40 µV, which mainly reflects the state of a person in the early stage of sleep or in the state of meditation, drowsiness and emotional depression.
    Α-wave: 8-13 Hz, with an amplitude of 30-50 µV, which mainly reflects the state in which a person is awake and calm with his eyes closed.
    Β wave: 14-30 Hz, with an amplitude of 5-20 µV, which mainly reflects the state of a person in mental tension, emotional excitement or excitement, active thinking and concentration.
    The feedback music generation module creates a reference library for feedback music types. FIG. Fig. 4 is a flow chart of creating the reference library for feedback music types.
    The reference library for feedback musical types is created based on the latest data model of an experiment of the Chinese National Basic Research Program (Program 973) (grant number: 2014CB744600). The corresponding EEG signal segments obtained under various emotional stimuli in the experiment are used as the basic index parameters of the system. In order to create a generative model of the feedback music, an index parameter library is created as follows: First, an internationally recognized audio sequence with positive, neutral and negative emotional states is used to stimulate the depressed patient, and the EEG signals of the depressed patient are used recorded under various emotional stimuli. The active state of each frequency band varies in different states of the body, and the activity is the amplitude of the waveform in each frequency band. Therefore, these EEG signals marked with an emotional marker are divided into four frequency bands with different waveforms, and then EEG data segmentation and EEG segment integration are performed. By setting a waveform threshold for each frequency band, a point in time at which the waveform in each frequency band is active is selected and the active waveforms in the four processed frequency bands are compared in a time sequence. A chronological relationship of frequency band activity under various emotional stimuli is recorded, which is an EEG character sequence. A chronological relationship segment, which occurs more frequently in the entire chronological relationship, represents the emotional state of the depressed patient under stimulation. This chronological relationship segment and the corresponding emotional audio stimulus form an index parameter. Finally, feedback music is designed and each chronological relationship segmenter is converted into a feedback music segment. Each feedback music segment forms an assignment relationship with the corresponding emotional stimulus, and the feedback music types corresponding to the various emotional stimuli are obtained.
    The EEG data segmentation of the present invention adopts a threshold selection method relating to statistical parameters such as mean and standard deviation, and aims to obtain a waveform of the frequency band having physiological meaning and the most important physiological information before the EEG -Can describe signal segmentation. In this method, the number of segments of each frequency band of the EEG signal becomes the maximum, and the segmentation threshold of each frequency band becomes the optimum. This method preserves the properties of the EEG data before segmentation and ensures the simplicity of the EEG data after segmentation. The EEG data are represented by superimposing and arranging the symbols d, t, a and b, each symbol corresponding to a frequency band. The threshold selection process is divided into two steps, namely finding and selecting an optimal threshold for each frequency band and using the selected threshold to segment the waveform in the corresponding frequency band.
    This method calculates the optimal threshold value based on the statistical characteristics of the EEG data. First, the mean value and the standard deviation (µ, σ) of the waveform data of each frequency band are calculated. They represent two input variables of the threshold value calculation method. Then the segmentation threshold value of each frequency band is calculated separately and an optimal threshold value is selected from a large number of segmentation thresholds. The segmentation threshold is calculated by: T = µ ± n · σ, where µ represents an average of the waveform data of each frequency band after frequency division; σ represents a standard deviation of the waveform data of each frequency band after frequency division; n represents a mean to standard deviation ratio which is used to calculate the segmentation threshold among various mean to standard deviation ratios and to select an optimal threshold. The optimal threshold value is selected by initializing the value of no, gradually increasing it and calculating the threshold value Ti in the transformation process of ni, where i is a number of calculated threshold values. When one or more data items of the waveform in the frequency band exceeds or exceed the corresponding threshold value Ti, the exceeding part is marked as a segment corresponding to the temporal length of the waveform. The segmentation process is repeated for the entire frequency band to obtain the length of each segment and the total number of segments in the frequency band below the threshold Tizu. By comparing and analyzing a segment density map of each threshold Ti, the optimal threshold can be selected. A threshold is selected with the greatest number of segments in the corresponding frequency band, and each frequency band on each line has a corresponding optimal threshold. Finally, the waveform of the EEG signal is represented by the corresponding four frequency bands as shown in FIG. 5 shown. In the figure, a whole segment of brain waves is divided into 4 frequency bands. The waveform segment (short strip) of each frequency band represents the currently active waveform of the frequency band and is also a character segment divided according to the threshold value.
    The EEG signal integration is then carried out by combining these four segmented characters into an EEG signal character sequence. These four segmented characters have physiological meaning and can describe the most important physiological information before the EEG signal segmentation. The segmentation algorithm also provides an EEG signal waveform segment composed of characters segmented according to the waveform in the frequency band. The EEG frequency bands contained at the same time are compared, and the characters represented by these EEG frequency bands are added to a time sequence. As in FIG. 6, the intercepted EEG contains the wave segments α and β in the first point in time, so that ab is added to the time sequence as a combination. θ-, α- and β-wave segments are included in the second point in time, so tab is added as a combination to the time sequence. In this way, the characters corresponding to the four frequency bands in the sequence are overlaid and arranged to form a complete EEG character sequence. These generated segment-like character sequences have a certain cycle period for the entire EEG signal, and the character sequence segment which appears most frequently in the character sequence generated under this audio stimulus is selected as the EEG feature under the audio stimulus. After stimulating the positive, neutral and negative audio sequence, the three types of EEG data of the depressed patient are segmented and integrated to obtain corresponding three types of EEG character sequences. The EEG signaling segment that occurs most frequently in each type of EEG signaling sequence in a given time is selected and the emotion generated by that audio stimulus is used as the marker of the EEG signaling segment, as shown in the following table: Positive (ab) (abd) (abt) (abdt) (abd) negative (ab) (abt) (abdt) (bd) neutral (bd) (bdt) (dt)
    The EEG signal segments corresponding to the positive label are (ab), (abd), (abt), (abdt) and (abd). The EEG signal segments corresponding to the negative mark are (ab), (abt), (abdt) and (bd). The EEG signal segments corresponding to the neutral marker are (bd), (bdt) and (bt). These three types of character segments are obtained on the basis of a large number of known EEG data from depressed patients under various emotional audio stimuli and are used as basic index parameters of the reference library for feedback music types. The symbols a, b, d and t in the brackets represent frequency bands that are selected by a segmentation algorithm. The representative frequency bands are out of order at this time; e.g. (ab) and (ba) are the same.
    The feedback training regulation module performs the music feedback training by selecting feedback music suitable for the type of feedback music suitable for the person and adjusting the person's emotion to a positive and relaxed state. An EEG signal sequence with a positive marker indicates that the depressed patient has a positive physiological and psychological state, and the corresponding feedback music to be selected should be a neutral to positive musical stimulus. An EEG signaling sequence with a negative mark indicates that the depressed patient has a negative physiological and psychological state, and the corresponding feedback music to be selected should be a positive musical stimulus to improve the mood of the depressed patient to a positive state. An EEG signal sequence with a neutral marker indicates that the depressed patient has a normal physiological and psychological state, and the corresponding feedback music to be selected should be a positive to neutral musical stimulus.
    The feedback training regulation module performs breathing training by instructing the person to concentrate on breathing and counting down with a difference according to a breathing rhythm until the counting down ends.
    The data storage module is designed to create, query, modify, delete and store information about the person and the course and results of the emotional regulation of the person by the system. All data is stored by a database security service (DBSS) system. The stored data is mainly divided into two categories. The first category represents the basic information of the person, including: the number of the person, the name, the gender, the age, the training program, the training times and the contact address, etc. The second category represents the EEG data of the person during the last Training. Apart from changes to the training program and the training times, the first data category remains essentially unchanged. The second category of data changes frequently and only the EEG signal data from the last exercise is saved. In addition, the system can also save the data as files in XML format for later online transmission and remote access for evaluating results and for data mining.
    FIG. 7 is a flow chart of the system for regulating a depressed mood by music feedback based on an EEG signal according to the present invention, including the following steps: 1) EEG signal acquisition: acquisition of EEG signals at three electrode positions, namely FP1, FP2 and FPz in the normal state of the person. 2) EEG data processing: processing of the recorded EEG signal data. 3) Generation of feedback music: segmentation and integration of the EEG signals, extraction of a feedback musical character sequence that corresponds to the person's EEG signals, and determination of the corresponding emotional markings. 4) Music feedback training: Selecting the feedback music suitable for emotional marking in order to conduct music feedback training for the person. 5) Emotional analysis of the person: Adjusting the person's emotion to a positive and relaxed state.
    The present invention provides a method for regulating a depressed mood of a depressed patient by biological information feedback with the aid of music. First, an expert uses a medical instrument to measure the physiological or pathological information that the depressed patient senses weakly or cognitively incorrectly, and combines the internal biological information with external physical behavior to intuitively provide feedback to the patient through visual and auditory perception processes give. The internal biological information is an EEG signal that is measured by the medical instrument. The intuitive feedback process is a music signal that has been converted from the EEG signal through segmentation and integration. Then the expert determines the physical and psychological state of the depressed patient through the musical information that the depressed patient receives and designs a specific feedback training for the depressed patient. Finally, after a certain time of the feedback training, the depressed patient is clear about his physiological or pathological state through the music feedback and can freely control some physiological functions and achieve a quick regulation of some simple emotions. The specific course of the feedback training is as follows: The training course is divided into two parts: breathing training and music training.
    In the first part of the breathing training, a breathing regulation method is mainly used: (1) During the entire training process, the test environment is kept calm in order to eliminate the influence of the test environment on the feedback training. The depressed patient sits in a chair with feet flat on the floor, hands naturally on thighs, upper body upright. (2) The depressed patient closes his eyes, feels and relaxes his body. In order to keep the depressed patient from falling asleep, it is necessary that the depressed patient keep their back straight and be awake throughout the training process. (3) The depressed patient is instructed to concentrate on breathing and to count down with a difference according to his breathing rhythm. For example, if the depressed patient silently counts the number 10 while inhaling, he will count the number 8 on the next exhale until the countdown ends. If an error occurs in the course of the countdown, the depressed patient will need to make adjustments in terms of time and focus. (4) In order to prevent the depressed patient from feeling numbness and boredom in his hands and feet due to a long training period, the test time is adjusted according to the specific conditions of the depressed patient in order to ensure a smooth flow of the breathing training process. When the breathing training is completed, the depressed patient reaches a state of clear mind and relaxed body.
    In the second part of the music training, the music feedback is the main regulation method and the imaginary relaxation is the auxiliary regulation method. (1) During the course of the music training, the depressed patient leans back on a chair and maintains a relatively comfortable sitting posture. The depressed patient wears an EEG recording device and is informed of the functions and operations of the system so that the depressed patient understands the physiological and psychological state represented by each feedback music. (2) The depressed patient receives music signals from the data processing module with information about his physical or psychological state. These music signals are feedback music segments with certain emotional stimulation selected by the person, and they are generated in real time according to changes in the person's EEG signals. (3) Through the gentle stimulation of the feedback music and the imagination guided by the expert, the emotions of the depressed patient are kept in a positive and relaxed state.
    The feedback music is determined based on the EEG signal character sequences with positive, negative and neutral emotional markings. The EEG character sequences are obtained from each segment of the EEG signal using segmentation and integration algorithms. A number of times that EEG character segments of the positive, neutral and negative feedback music types generated by the feedback music generation module appear in the person's EEG signal character sequence is compared to determine the emotional mark of the person's EEG signal and generate a feedback music type suitable for the person. An EEG signal sequence with a positive marker indicates that the depressed patient has a positive physiological and psychological state, and the corresponding musical stimulus should be a neutral to positive musical stimulus. This type of stimulation need not be too strong as long as it can maintain the depressed patient's emotional state. An EEG signaling sequence with a negative mark indicates that the depressed patient has a negative physiological and psychological state, and the corresponding music stimulus should be a positive music stimulus. In this state, the depressed patient needs strong positive stimulation in order to increase his emotion to a positive state. An EEG signal sequence with a neutral marker indicates that the depressed patient has a normal physiological and psychological state, i.e. neither joy nor sadness, and the corresponding feedback stimulus should be a positive to neutral musical stimulus. Different classical music in the world belongs to different emotional types, and the classical music corresponding to the representative emotional states is selected to prepare a feedback music library related to the emotional type. The musical stimulus used for the intervention in music training is selected from the created music library. In this way, a type of feedback music suitable for the patient is created and the second stage of the music training is completed.
    The present invention employs a method of music intervention to regulate a depressed mood, and the mechanism of music intervention is as follows: Physiological / physical effects. Music can cause various physiological reactions, such as decreased blood pressure, slowed breathing, increased skin temperature, decreased muscle potential, increased blood vessel volume and decreased norepinephrine and adrenaline levels in the blood. This significantly promotes the homeostasis of the human body, relieves tension and fears and supports relaxation. Studies have shown that auditory information affects the activity of the amygdala and hippocampus, etc. There is a network between these limbic system structures, and this network plays a crucial role in emotional processing.
    Interpersonal / Social Impact. Music is an art of social non-verbal communication, and musical activities (including singing, playing instruments, creating, etc.) in turn are social interaction activities. Music activities provide people with a safe and comfortable environment for interpersonal communication by stimulating people to learn and improve their language skills, learn correct social behavior and the ability to work with others, and improve their self-confidence and self-assessment.
    Psychological / emotional effects. Music has a great influence on people's emotions. Music therapists use music to change people's emotions and ultimately to change people's cognition and behavior. Music plays a unique catalytic role. The purpose of regulating emotions through music is not to improve the person's musical skills, but rather to change the person's mood, behavior, and ideology through the psychological experience of music. Through these changes, the patient's psychology can be improved and he can better adapt to the environment.
    Depression, also known as a depressive disorder, is characterized by a pronounced and persistent depressed mood. The symptoms of depressed patients are varied and can be divided into emotional symptoms and physical symptoms. Emotional symptoms are usually the most significant and common symptoms, including: depressed mood, anhedonia, feelings of guilt, low self-esteem, etc. The physical symptoms of depressed patients are also diverse and affect different organs of the body. The manifestation of symptoms is influenced by various physical and psychological factors. Congenital depression is genetically predisposed. Acquired depression is associated with changes in stress response and cranial nerve structure. Long-term stress and sadness cause neuronal damage and atrophy in the brain structure, most obviously in the hippocampus.
    A stress response can cause atrophy of the limbic system and lead to depression. The hypothalamic-pituitary-adrenal axis (HPA) is a complex collection of limbic structures in the brain and its main function is to control the stress response and regulate physical activity. FIG. 8 shows the course of regulation of the HPA axis under normal circumstances. As the regulatory center of the endocrine system, the hypothalamus secretes a corticotropin-releasing hormone in order to induce the pituitary gland to release corticotropin. These hormones follow the flow of blood to reach various endocrine glands and promote the secretion of hormones by each endocrine gland. When there are too many hormones in the blood to disrupt homeostasis, those hormones in turn inhibit the functioning of the hypothalamus and pituitary gland. When the body is stimulated by stress, glucocorticoid levels rise cyclically. However, some areas of the brain of people with a tendency to depression, such as the hypothalamus and hippocampus, can be stimulated over long periods of time by high concentrations of glucocorticoids. This leads to a decrease in the number of synapses and, in severe cases, causes neuronal apoptosis in the hippocampus. The atrophy of the hippocampus is related to the abnormal activity of the HPA axis, the increase in glucocorticoid levels and the destruction of the negative feedback regulation mechanism. The atrophy of these brain structures causes part of the endocrine system to cease to function normally, causing mood disorders and weakening the body's ability to properly respond to stress, causing people to experience extreme tension, anxiety, and other negative emotions.
    FIG. 9 shows a mechanism of action of music feedback to regulate the mood of depressed patients.
    Long-term depression is the main symptom of depressed patients, as a result of which the perception path of depressed patients differs from that of ordinary people. This path of perception involves the perception of external information that provokes emotional and physiological responses to external information, which in turn affects the response of the limbic system of the brain. The negative feedback regulation of the HPA axis also causes a number of physiological responses. Depressive symptoms make it easier for the patient to perceive external stimuli such as sad music, pictures and videos. When depressed patients are constantly stimulated by these negative emotions, they often feel helpless, useless, have self-reproach, low self-esteem and, in severe cases, hallucinations. In doing so, they become depressed, nervous, anxious, self-denying, etc. In this way, the structure of the limbic system of depressed patients will process the stimulus signals received over a long period of time. The constant negative emotional state will cause glucocorticoid levels to rise and nerve synapses to decrease, and will induce profound apoptosis of neurons in the prefrontal lobe, hypothalamus and hippocampus, further destroying the negative feedback regulation mechanism of the HPA axis. As a result, the patient's brain activity is abnormally disturbed and their ability to stress is poor, which in turn leads to a number of physiological changes such as endocrine disorders and severe emotional effects. In view of these physiological responses, the present invention provides a system for regulating a depressed mood through music feedback based on EEG signals. EEG signals from a depressed patient are recorded in a feedback loop in order to monitor the patient's mood. The segmentation and integration algorithms of the EEG signals find the music signals that are mapped by the patient's EEG signals, and the feedback music is generated in accordance with the emotional type of the music signals. This procedure converts the EEG biosignals into tangible music signals in order to help the patient to learn something about his physical and mental state and to perceive internal information through the feedback music. Based on this, the system designs a number of music feedback regulation training programs including breathing training and music training for the patient. Through the patient's emotional and psychological reactions to the internal information, the patient's limbic system reacts, and the HPA axis performs negative feedback regulation to regulate the patient's physiological response. When the depressed patient receives music feedback training for emotional regulation, his parasympathetic nerves release inhibitory hormones such as acetylcholine. These inhibitory hormones lower the level of various hormones in the blood and apparently promote homeostasis in the body, thus lowering blood pressure, slowing breathing, relieving tension and anxiety, and maintaining the balance of the internal environment, thereby achieving the regulation of negative emotions.
    It should be noted that the specific implementations described above may assist those skilled in the art to more fully understand the present invention, but in no way limit the present invention. All technical solutions and improvements that are made without departing from the spirit and scope of the present invention fall within the scope of protection of the present invention.
    权利要求:
    Claims (10)
    [1]
    A system for regulating a depressed mood by music feedback based on an electroencephalogram (EEG) signal, comprising an EEG signal acquisition module, an EEG signal data processing module, a feedback music generation module, a feedback training regulation module and a data storage / analysis module , wherein the EEG signal acquisition module is configured to receive an EEG rest signal from a person; the EEG signal data processing module is designed to process the captured EEG signal; the feedback music generation module is designed to segment and integrate the processed EEG signal, to analyze an association between the EEG signal and a music signal and to compare it in a reference library created for feedback music types in order to obtain a feedback music type to the music - Receive feedback training; the feedback training regulation module is designed to use feedback music matching the feedback music type in order to carry out feedback training for the person so as to achieve the regulation of the depressed mood; the data storage / analysis module is designed to store and analyze a course and results of the emotional regulation of the person.
    [2]
    2. The system for regulating a depressed mood by music feedback based on an EEG signal according to claim 1, wherein a three-pole system is used in the EEG signal acquisition module; Electrode positions are selected according to the worldwide 10-20 electrode placement system, comprising Fp1, Fp2 and Fpz, located on the forehead and not disturbed by hair; the electrodes are medical sticky wet electrodes that avoid the interference of electrode contact impedance.
    [3]
    3. The system for regulating a depressed mood by music feedback based on an EEG signal according to claim 1, wherein the EEG signal data processing module segments the detected EEG signal as follows: dividing a detected continuous EEG signal into EEG signal segments with of moderate length, and overlaying each segment of EEG data with a portion of EEG data in a previous segment.
    [4]
    4. The system for regulating a depressed mood by music feedback based on an EEG signal according to claim 3, wherein the EEG signal data processing module performs noise reduction and filtering on the segmented EEG signal as follows: application of an improved dynamic model parameter of an augmented reality (AR) and a wavelet analysis for noise suppression, and using a band pass filter with finite impulse response (FIR) to remove a frequency band below 0.5 Hz and a frequency band above 50 Hz from the EEG signal; and dividing the EEG signal according to the frequency and extracting the waveform characteristics of four frequency bands (δ, θ, α and β) of the EEG signal.
    [5]
    5. System for regulating a depressed mood by music feedback based on an EEG signal according to claim 4, wherein the feedback music generation module segments the EEG data as follows: firstly, the frequency bands are represented by corresponding characters d, t, a and b, where d is the δ band, t is the θ band, a is the α band and b is the β band; Calculating an average µ and a standard deviation σ of waveform data of each frequency band; then separately calculating a segmentation threshold for each frequency band and selecting an optimal threshold from a plurality of segmentation thresholds, the segmentation threshold being calculated by: T = µ ± n · σ, where µ represents an average of the waveform data of each frequency band after frequency division; σ represents a standard deviation of the waveform data of each frequency band after frequency division; n represents a mean to standard deviation ratio used to calculate the segmentation threshold among various mean to standard deviation ratios and to select the optimal threshold; and finally, plotting a waveform of the EEG signal through the corresponding four frequency bands, analyzing a point in time at which the waveform of each frequency band is active according to the optimal threshold value of each frequency band selected after the calculation, and obtaining a chronological relationship of the EEG- Signal corresponding to each frequency band.
    [6]
    6. The system for regulating a depressed mood by music feedback based on an EEG signal according to claim 5, wherein the feedback music generation module integrates EEG segments as follows: determining frequency bands at the same time according to the chronological relationship of the EEG signal that corresponds to each frequency band obtained by using a segmentation algorithm; Adding characters representing the frequency bands to a time sequence; and superimposing and arranging the characters corresponding to the four frequency bands in the sequence to form a complete EEG character sequence.
    [7]
    7. The system for regulating a depressed mood by music feedback based on an EEG signal according to claim 6, wherein the feedback music generation module creates a reference library for feedback types of music as follows: first of all, detecting existing depressive EEG signals of a depressed patient among positive ones , neutral and negative specific emotional audio stimuli, and performing frequency division, EEG data segmentation, and EEG segment integration on the captured EEG signals to obtain integrated EEG character sequences; Marking the integrated EEG character sequences with positive, neutral and negative emotional markings in each case according to the corresponding positive, neutral and negative emotional audio stimuli, the EEG character sequences marked with different emotional markings having specific cyclic segments throughout the EEG signal; Selecting a cyclical segment of the EEG character sequence that occurs most frequently in a certain period of time as the feedback music type according to the emotional marker, and receiving three feedback music types: positive, neutral and negative.
    [8]
    The system for regulating a depressed mood by music feedback based on an EEG signal according to claim 7, wherein the feedback music generation module compares in the created reference library for feedback music types as follows: comparing a number of times for the EEG character segments the positive, neutral and negative feedback music types of the created reference library for feedback music types appear in the EEG signal character sequence of the person; Determining an emotional tag of the person's EEG signal; and generating a feedback music style appropriate for the person.
    [9]
    9. The system for regulating a depressed mood by music feedback based on an EEG signal according to claim 8, wherein the feedback training regulation module performs the music feedback training as follows: selecting feedback music matching the feedback music type that is for the person is suitable, and regulating the emotion of the person in a positive and relaxed state, wherein an EEG signal signal sequence with a positive marker indicates that the depressed patient has a positive physiological and psychological state, and the corresponding feedback music to be selected is neutral to positive Musical stimulus should be; wherein an EEG signal sequence with a negative mark indicates that the depressed patient has a negative physiological and psychological state, and the corresponding feedback music to be selected should be a positive musical stimulus in order to improve the mood of the depressed patient towards a positive state; wherein an EEG signal character sequence with a neutral marking indicates that the depressed patient has a normal physiological and psychological state, and the corresponding feedback music to be selected should be a positive to neutral musical stimulus.
    [10]
    10. The system for regulating a depressed mood by music feedback based on an EEG signal according to any one of claims 1 to 9, wherein the feedback training regulation module performs breathing training as follows: instructing the person to concentrate on breathing and counting down with a difference according to a rhythm of breathing until the countdown ends.
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    同族专利:
    公开号 | 公开日
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    申请号 | 申请日 | 专利标题
    CN201911376716.0A|CN111068159A|2019-12-27|2019-12-27|Music feedback depression mood adjusting system based on electroencephalogram signals|
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