CN111068159A - Music feedback depression mood adjusting system based on electroencephalogram signals - Google Patents

Music feedback depression mood adjusting system based on electroencephalogram signals Download PDF

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CN111068159A
CN111068159A CN201911376716.0A CN201911376716A CN111068159A CN 111068159 A CN111068159 A CN 111068159A CN 201911376716 A CN201911376716 A CN 201911376716A CN 111068159 A CN111068159 A CN 111068159A
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music
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electroencephalogram
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depression
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胡斌
蔡涵书
肖寒
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Lanzhou University
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Abstract

According to the music feedback depressed emotion adjusting system based on the electroencephalogram signals, the mapping relation between the electroencephalogram signals and the music signals is analyzed, and corresponding music feedback training is carried out on a trainee, so that the purpose of improving the emotion of a depressed patient is achieved. The method comprises the following steps: the electroencephalogram signal acquisition module is used for acquiring resting electroencephalogram signals of the trainee; the electroencephalogram signal data processing module is used for preprocessing the acquired electroencephalogram signals, the feedback music generating module is used for segmenting and integrating the preprocessed electroencephalogram signals to obtain the mapping relation between the electroencephalogram signals and the music signals, and the mapping relation is compared in the constructed feedback music type reference library to obtain the feedback music types for music feedback training; the feedback training adjusting module is used for performing feedback training on the trainee by adopting feedback music which is adaptive to the type of the feedback music to realize adjustment of depression emotion; and the data storage and analysis module is used for storing and analyzing the process and the result of the emotional adjustment of the trainee.

Description

Music feedback depression mood adjusting system based on electroencephalogram signals
Technical Field
The invention relates to a music feedback depression mood adjusting system based on electroencephalogram signals, and belongs to the technical field of medical auxiliary systems.
Background
The brain nerves can generate weak electric field fluctuation when in activity, and the electric field can generate rhythmic fluctuation when tens of thousands of nerves are in activity simultaneously. This fluctuation can be measured from the scalp, which is the brain wave. The brain waves have the following characteristics: it is a spontaneous electrical potential generated by cranial nerve activity and always present in the central nervous system; the electroencephalogram signals are very weak; the anti-interference performance is weak, and the robustness is poor; it is a random signal and has the characteristics of non-stability, non-Gaussian and non-linearity; can reflect the state and change of the nervous system.
The electroencephalogram signals can reflect the emotional changes of human beings in real time. The research of the brain electrical signal can be applied to the aspects of understanding the brain activity mechanism, the cognitive process of people, diagnosing brain diseases and the like. The resting state electroencephalogram signals are mainly used for researching the comparative analysis between mental disease patients and healthy people, and the induced electroencephalogram signals are mainly used for researching the change of cognitive functions of the patients. Therefore, the brain electrical signals are collected, and the physiological and psychological states of people can be monitored through analysis.
Modern music therapy originally originated in the united states and is a comprehensive application discipline integrating music, medicine and psychology. Definitions of music treatments are set forth in the book "define music treatments" published in 1989 by professor k. bruscia professor of famous music therapist of the university of tempele, usa: music therapy is a systematic interventional process in which therapists use various forms of music experience and therapeutic relationships developed during therapy as motivations for therapy to help the subject achieve a healthy goal.
Music therapy methods can be divided into accepted, improvised and recreated music therapy. As one of the methods of music therapy, recreating music therapy emphasizes not only allowing a person to be treated to listen to music but also to personally participate in various music-related activities. The musical performance and singing do not require the person to be treated to receive any musical training or to have any musical skills, but the re-creative musical treatment is designed for those persons who do not have any musical skills.
Music therapy is a means of psychological treatment, so it follows some treatment principles identical to general psychological treatment, such as privacy principle, friend-making principle, etc. In addition, there are some special treatment principles for creative music therapy.
a. And (5) a progressive principle. The recreating music therapy is to play music gradually according to the psychological characteristics of visitors. From the viewpoint of the selection of music, it is to be progressive.
b. Learning and heuristic rules. The learning and inspiring principle refers to teaching and guiding visitors who do not know music during music therapy, and introducing the background of music creation and the mood of musicians to the visitors.
c. Experience principles. During treatment, visitors experience their mood or feelings with mind according to the atmosphere created by music.
Music therapy is not a random, isolated intervention process, but rather a rigorous, scientific, systematic intervention process that includes assessment, long-and short-term therapy establishment, therapy plan establishment and implementation, and efficacy evaluation; music therapy is to use all the activities related to music as means, such as listening, singing, playing, musical composition, music and other arts, rather than just listening to music; the music therapy process must go through three factors including music, the person being treated and a trained music therapist.
Depression belongs to the main category of mood disorders. In severe cases, psychotic symptoms such as hallucinations and delusions may occur. The existing treatment and intervention means for psychological medical diseases have more defects, which mainly comprise:
1) treatment of depression has drawbacks: the existing depression treatment methods comprise Chinese and western medicine treatment, psychological counseling treatment and the like, and the treatment methods have side effects with different degrees. Mainstream psychological treatment aiming at depression, such as cognitive behavior treatment, neglects the richness of life of patients and the importance of emotional experience to a certain extent, emphasizes the rationality and objectivity excessively, and the treatment process and effect mainly depend on therapists;
2) generally, the electroencephalogram-based music therapy mainly intervenes in the mood of a subject by listening to music, and this music therapy related to listening is called an accepted music therapy, but it does not have universality because each person has a different stress response to music. The intervention method belongs to a lower-level intervention means, and is easy to cause the patient to generate immunity and cannot play a good role;
3) the electroencephalogram signal acquisition equipment has no universality: the medical electroencephalogram signal acquisition instrument is complex in equipment and high in cost, and requires a specially-assigned person to take charge of acquisition; the portable electroencephalogram acquisition instrument has the advantages that the number and the position of electroencephalogram acquisition electrodes are uncertain, the modes of the electroencephalogram acquisition electrodes and data transmission, the cost and the application field are different, the power consumption is high, and the A/D conversion digit is low;
4) data modeling and analysis lack relative reliability: the modeling data is less, the contained information amount is small, and the data model has no good balance and effectiveness. The electroencephalogram extraction algorithm is not good, and pure physiological electroencephalogram signals cannot be obtained. The single data analysis method causes the results of feature extraction and selection to lack representativeness and accuracy. These disadvantages render it impossible to give a rationalised adjuvant therapy for the characteristic information of the subject.
Disclosure of Invention
The invention provides a music feedback depression mood regulating system based on electroencephalogram signals, which is characterized in that depression electroencephalogram signals are collected and processed, the mapping relation between the electroencephalogram signals and music signals is analyzed, feedback music under different emotional audio stimuli is generated, and the purpose of improving the mood of a depression patient is achieved through corresponding feedback music training.
The technical scheme of the invention is as follows:
1. a music feedback depression mood regulating system based on electroencephalogram signals is characterized by comprising: the system comprises an electroencephalogram signal acquisition module, an electroencephalogram signal data processing module, a feedback music generation module, a feedback training adjustment module and a data storage and analysis module; the electroencephalogram signal acquisition module is used for acquiring resting electroencephalogram signals of the trainee; the electroencephalogram signal data processing module is used for preprocessing the acquired electroencephalogram signals, the feedback music generating module is used for dividing and integrating the preprocessed electroencephalogram signals, analyzing the mapping relation between the electroencephalogram signals and the music signals, and comparing the electroencephalogram signals and the music signals in the constructed feedback music type reference library to obtain feedback music types for music feedback training; the feedback training adjusting module is used for performing feedback training on the trainee by adopting feedback music which is adaptive to the type of the feedback music to realize adjustment of depression emotion; the data storage and analysis module is used for storing and analyzing the process and the result of the trainee emotion regulation.
2. The electroencephalogram signal acquisition module adopts a three-lead system, the electrode position adopts a 10-20 system electrode method which is widely adopted internationally, and the selected electrodes are as follows: fp1, Fp2 and Fpz are positioned at the forehead, are not interfered by hairs, and medical patch type wet electrodes are used, so that the interference of contact impedance of the electrodes is avoided.
3. The electroencephalogram signal data processing module comprises the following steps of carrying out segmentation processing on the acquired electroencephalogram signals: and intercepting the acquired continuous electroencephalogram signals into electroencephalogram signal segments with proper length, wherein each segment of electroencephalogram data is overlapped with part of electroencephalogram data of the previous segment.
4. The electroencephalogram signal data processing module comprises the step of denoising and filtering segmented electroencephalogram signals, the step of denoising is carried out by adopting improved dynamic AR model parameters and wavelet analysis, the frequency bands lower than 0.5Hz and higher than 50Hz in the electroencephalogram signals are removed by using an FIR band-pass filter, and the step of dividing the electroencephalogram signals according to frequency and extracting waveform characteristics of four frequency bands of the electroencephalogram signals delta, theta and α is also included.
5. The feedback music generation module comprises an electroencephalogram data segmentation step, a step of representing each frequency band by corresponding characters d, T, a and b, wherein d represents a delta wave band, T represents a theta wave band, a represents an α wave band, and b represents a β wave band, a step of calculating an average value mu and a standard deviation sigma of waveform data of each frequency band, a step of calculating a segmentation threshold value of each frequency band respectively and selecting an optimal threshold value from a plurality of segmentation threshold values, wherein the calculation formula of the segmentation threshold value is that T is mu +/-n sigma, mu represents the average value of the waveform data of each frequency band after frequency division, sigma represents the standard deviation of the waveform data of each frequency band after frequency division, n represents the ratio of the average value to the standard deviation and is used for calculating the segmentation threshold values under different proportions of the average value to the standard deviation and selecting the optimal threshold value, a step of representing a waveform diagram of an electroencephalogram signal by corresponding four frequency bands, and analyzing active time points of each frequency band waveform according to the optimal threshold values selected after calculation to obtain an electroencephalogram signal time sequence relation corresponding to each frequency band.
6. The feedback music generation module comprises the step of electroencephalogram segment integration: according to the electroencephalogram signal time sequence relation corresponding to each frequency band obtained by a segmentation algorithm, comparing which frequency bands are contained at the same time point, adding the characters represented by the frequency bands into a time sequence, and overlapping and arranging the characters corresponding to the four frequency bands in the sequence to form a complete electroencephalogram character sequence.
7. The feedback music generation module comprises the following steps of constructing a feedback music type reference library: firstly, acquiring depression electroencephalogram signals of known depression patients under positive, neutral and negative specific emotion audio stimulation, and carrying out frequency band division, electroencephalogram data segmentation and electroencephalogram segment integration on the acquired electroencephalogram signals to obtain an integrated electroencephalogram character sequence; according to the stimulation of corresponding positive, neutral and negative different emotion audios, positive, neutral and negative emotion labels are respectively marked on the integrated electroencephalogram character sequences, specific cyclic segments of the electroencephalogram character sequences marked with the different emotion labels exist in the whole electroencephalogram signal, the cyclic segment of the electroencephalogram character sequence with the largest frequency of occurrence within a certain time is selected as a feedback music type corresponding to the emotion label, and three feedback music types of positive, neutral and negative are obtained.
8. The feedback music generation module comprises a step of comparing in a constructed feedback music type reference library: and judging the emotion label of the electroencephalogram signal of the trainee by comparing the times of the electroencephalogram character segments of positive, neutral and negative feedback music types in the constructed feedback music reference library appearing in the electroencephalogram signal character sequence of the trainee, and generating the feedback music type required by the trainee.
9. The feedback training adjusting module comprises the following steps of music feedback training: selecting feedback music which is adaptive to the required feedback music type according to the acquired feedback music type required by the trainee, and adjusting the emotion of the trainee to be maintained in a positive and relaxed state; the electroencephalogram signal character sequence with the positive label represents that the physiological and psychological states of the depression patient are positive emotional states, and corresponding feedback music is selected to be neutral and slightly positive music stimulation; the electroencephalogram signal character sequence with the negative label represents that the physiological and psychological state of the depression patient is a negative emotional state, and corresponding feedback music is selected to be positive music stimulation, so that the emotion of the depression patient is raised to be positive; the electroencephalogram signal character sequence with the neutral label shows that the physiological and psychological states of the depression patient are normal, and the corresponding feedback music is selected to be music stimulation with positive polarity and neutral polarity.
10. The feedback training adjustment module comprises the steps of breathing training: the trainee is led to focus on breathing and to perform a digital inversion of the difference in the rhythm of breathing until the end of the number.
The invention has the technical effects that:
the invention provides a music feedback depression emotion regulating system based on electroencephalogram signals, which is based on a music treatment principle, collects, processes and analyzes the electroencephalogram signals in real time, maps processed electroencephalogram data segments onto the music signals, converts the electroencephalogram signals into the music signals, and achieves the purpose of improving the emotion of a depression patient through corresponding feedback music training according to feedback music types obtained under different emotional audio stimuli.
Drawings
FIG. 1 is a schematic structural diagram of a music feedback depression mood regulating system based on electroencephalogram signals;
FIG. 2 is a diagram of the International 10-20 System and electrode site selection;
FIG. 3 is a schematic diagram of the segmented acquisition and processing of brain electrical data.
FIG. 4 is a flow chart of building a reference library of feedback music types;
FIG. 5 is a schematic diagram of electroencephalogram signal segmentation.
FIG. 6 is a schematic diagram of brain electrical signal integration.
FIG. 7 is a flowchart of the operation of the electroencephalogram based music feedback depressive mood regulating system of the present invention;
figure 8 is a hypothalamic-pituitary-adrenal axis effect map.
Fig. 9 is a diagram of the mechanism of action of music feedback to regulate the mood of a depressed patient.
Detailed Description
Embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
As shown in fig. 1, the invention is a structural schematic diagram of a music feedback depression mood regulating system based on electroencephalogram signals.
A music feedback depressive mood regulation system based on electroencephalogram signals, comprising: the system comprises an electroencephalogram signal acquisition module, an electroencephalogram signal data processing module, a feedback music generation module, a feedback training adjustment module and a data storage and analysis module; the electroencephalogram signal acquisition module is used for acquiring resting electroencephalogram signals of the trainee; the electroencephalogram signal data processing module is used for preprocessing the acquired electroencephalogram signals, the feedback music generating module is used for dividing and integrating the preprocessed electroencephalogram signals, analyzing the mapping relation between the electroencephalogram signals and the music signals, and comparing the electroencephalogram signals and the music signals in the constructed feedback music type reference library to obtain feedback music types for music feedback training; the feedback training adjusting module is used for performing feedback training on the trainee by adopting feedback music which is adaptive to the type of the feedback music to realize adjustment of depression emotion; the data storage and analysis module is used for storing and analyzing the process and the result of the trainee emotion regulation.
The electroencephalogram signal acquisition module adopts a three-lead generalized electroencephalogram acquisition system (associated patent: CN201520628152.6) to acquire electroencephalogram signals. Compared with the traditional electroencephalogram acquisition system, electroencephalogram acquisition under a pervasive environment has new characteristics on acquisition and processing of electroencephalograms: the electroencephalogram can be collected at any time and any place, and the fewer the leads are, the better the lead is; the acquisition process is as simple as possible, and complex tasks are prevented from inducing electroencephalogram; the electroencephalogram signal processing process is rapid, and the occupied resources are less.
The electrode placement position for electroencephalogram acquisition is shown in fig. 2, and in this embodiment, reference is made to a 10-20 system electrode method widely adopted internationally. With the three lead system, because the prefrontal brain areas have strong correlation with mood changes and mental disorders, the electrodes selected are: fp1, Fp2 and Fpz, three electrodes are positioned at the forehead and are not interfered by hairs, and medical patch type wet electrodes are used, so that the interference of contact impedance of the electrodes is avoided; the collected electroencephalogram signals on the Fp1, Fp2 and Fpz electrodes are transmitted to the data processing module through the Bluetooth transmission equipment.
The electroencephalogram signal data processing module is used for preprocessing the acquired electroencephalogram signals, and firstly, the acquired electroencephalogram signals are subjected to segmentation processing: because the feedback music needs to have continuity, rhythm sense and timeliness and reflect the physiological state of the depression patient in real time, the length of the original electroencephalogram signal processed in real time each time is moderate, and if the intercepted electroencephalogram signal is too long, the music fragment converted from the processed electroencephalogram data is lack of timeliness and cannot accurately reflect the real-time physiological state of the depression patient; if the intercepted electroencephalogram signal is too short, the physiological state of the patient cannot be effectively represented, so that the converted music piece loses continuity and rhythm sense and feedback effectiveness, and the emotion adjusting effect of feedback training is reduced. The invention selects the 6s brain electrical signal as the data for one time of processing, wherein the data of the first 3s needs to be overlapped in each time of processing, namely the original brain electrical data of the first 6s is acquired in the first processing, and the 6s data processed in each time is the 3s original brain electrical data acquired at this time and the last 3s original brain electrical data acquired at the last time, as shown in fig. 3. Therefore, data of 6s each time is used for judging the physiological state of the depression patient and converting the state into feedback music of 3s, so that the depression patient can obtain feedback information brought by the music every 3s, and the effectiveness of the music and the timeliness of the feedback information are ensured.
The electroencephalogram signal processing module is used for denoising the acquired electroencephalogram signals by adopting improved dynamic AR model parameters and wavelet analysis, removing frequency bands lower than 0.5Hz and higher than 50Hz in the electroencephalogram signals by using an FIR band-pass filter, simultaneously dividing the electroencephalogram signals according to frequency, and extracting waveform characteristics of four frequency bands of the electroencephalogram signals delta, theta and α, and preprocessing the electroencephalogram signals of each lead marked with the emotion label into four frequency bands of a delta wave band, a theta wave band, a α wave band and a β wave band.
Delta wave: 1-3 Hz and the amplitude of 20-200 mu V mainly reflect the state that people are in deep sleep or have serious organic brain diseases.
θ wave: 4-7 Hz, the amplitude is 10-40 μ V, which mainly reflects the early sleep stage, meditation, drowsiness and emotional depression of people.
α wave at 8-13 Hz and amplitude at 30-50 μ V, mainly reflecting that human is in waking, resting and eye-closing state.
β wave of 14-30 Hz and amplitude of 5-20 μ V, which mainly reflects the state of mental stress, emotional excitement or hyperactivity, active thinking and concentration.
The feedback music generation module includes a step of constructing a reference library of feedback music types. Fig. 4 is a flowchart of constructing a reference library of feedback music types.
The feedback music type reference library is constructed based on the latest data model of a 973 project (project code: 2014CB744600) experiment, and electroencephalogram signal character segments corresponding to different emotional stimuli obtained by the experiment are used as basic index parameters of the system. Therefore, the following operations are carried out for constructing an index parameter library and further forming a generation model of the feedback music, and the specific flow is as follows:
firstly, stimulating a depression patient by adopting specific emotions of audio (positive, middle and negative) with internationally clear emotional states, and collecting and recording electroencephalogram signals under different emotional stimuli; the active states of the human body in different frequency bands can have differences, wherein the activity is the amplitude of the waveform of each frequency band. Therefore, the electroencephalogram signals marked with the emotion labels are subjected to electroencephalogram frequency band division, and waveforms divided into four frequency bands are different; then, carrying out electroencephalogram data segmentation and electroencephalogram segment integration, selecting time points at which each frequency band waveform is active by designing a threshold value of each frequency band waveform, comparing the active waveforms processed in the four frequency bands according to a time sequence, recording a time sequence relation of frequency band activity under different emotional stimuli, namely an electroencephalogram character sequence, finding out a time sequence relation segment with more occurrence times from the whole time sequence relation, and using the time sequence relation segment to represent an emotional state of a depression patient under stimulation, namely forming an index parameter by the time sequence relation segment and the corresponding emotional audio stimulation; and finally, designing feedback music, converting each time sequence relation segment into a feedback music segment, and forming a mapping relation between each feedback music segment and the corresponding emotional stimulus to obtain feedback music types corresponding to different emotional stimuli.
The electroencephalogram data segmentation adopts a threshold selection method related to statistics such as mean values, standard deviations and the like, and aims to obtain a frequency band waveform which has physiological significance after segmentation and can describe main physiological information before electroencephalogram signal segmentation. The method enables the number of the segmentation sections of each frequency band of the electroencephalogram signal to be maximum, and simultaneously, the segmentation threshold value of each frequency band of the electroencephalogram signal is optimal. Not only the characteristics of the brain electrical data before segmentation are reserved, but also the simplification of the segmented brain electrical data is ensured. Electroencephalogram data are represented by symbols d, t, a and b, wherein each symbol corresponds to a frequency band, and therefore the electroencephalogram data can be represented by superposition and arrangement of the symbols. The threshold selection method is divided into two parts: firstly, searching and selecting an optimal threshold value of each frequency band; in a second step, the selected threshold is used for segmentation of the corresponding band waveform.
The method uses the statistical characteristics of the electroencephalogram data to calculate the optimal threshold. First, the mean and standard deviation (μ, σ) of the waveform data of each frequency band are calculated. This is two input variables to the threshold calculation method. Then, the division threshold value of each frequency band is calculated respectively, and an optimal threshold value is selected from a plurality of division threshold values. The calculation formula of the segmentation threshold is as follows: t ═ μ ± n · σ, μ denotes an average value of waveform data of each frequency band after frequency division; sigma represents the standard deviation of waveform data of each frequency band after frequency division; n represents the ratio of the mean to the standard deviation,the method is used for calculating the segmentation threshold values under different proportions of the average value and the standard deviation and selecting the optimal threshold value. The process of selecting the optimal threshold is as follows: initializing n0And increasing n step by stepiCalculating niThreshold T in the transformation processiAnd i is the number of calculation thresholds. When one or more data of the frequency band waveform exceeds the corresponding threshold value TiThe excess portion is marked as a segment by the time-series length of the waveform. Repeating the segmentation process on the whole frequency band to obtain the threshold value TiThe length of each segment and the total number of segments in the lower frequency band. Analysis of each threshold T by comparisoniThe optimal threshold value can be selected according to the segment density map. The threshold value with the maximum number of acquired segments in the corresponding frequency band is selected, and each frequency band on each lead has the corresponding optimal threshold value. Finally, we will represent the oscillogram of the electroencephalogram signal by its corresponding four frequency bands, as shown in fig. 5. In the figure, 4 frequency bands are divided under a whole brain wave, and a waveform segment (short strip) of each frequency band represents that the waveform of the frequency band is in an active state at the time point, and is also a character segment divided according to a threshold value.
The method comprises the following steps of integrating electroencephalogram signals, wherein the integration aims to combine four segmented characters which have physiological significance and can describe main physiological information before electroencephalogram signal segmentation into a character sequence of the electroencephalogram signals, a segmentation algorithm also provides waveform segments of the electroencephalogram signals consisting of the segmented characters corresponding to frequency band waveforms, compares which electroencephalogram frequency bands are contained at the same time point, and adds characters represented by the electroencephalogram frequency bands into a time sequence, as shown in FIG. 6, a part of electroencephalogram intercepted in FIG. 6 comprises α and β wave segments at a first time point, ab is added into the time sequence as a combination, tab is added into the time sequence, 2, the characters corresponding to the four frequency bands in the electroencephalogram signal sequence form a complete electroencephalogram character sequence by superposition and arrangement, the generated segment type character sequences have specific cycle periods for the whole electroencephalogram signals, the segment type character sequences with the largest number of occurrence in the character sequence generated under the current audio stimulation are selected as a characteristic under the audio stimulation, 3, and a plurality of electroencephalogram character segments which are selected from a telecommunication stimulation table, and a plurality of electroencephalogram data which are generated after the electroencephalogram signal is subjected to the time stimulation, are selected as follows, and the electroencephalogram data are integrated, and the electroencephalogram data, the electroencephalogram data are respectively, and the frequency of the electroencephalogram data are selected as follows, the electroencephalogram data after the electroencephalogram data are selected:
Figure BDA0002341172760000081
to obtain: the electroencephalogram signal character segment corresponding to the positive label is (ab) (abd) (abt) (abd); the electroencephalogram signal character segment corresponding to the negative label is (ab) (abt) (abdt) (bd); the electroencephalogram signal character segment corresponding to the neutral label is (bd) (bdt) (bt); the 3 character segments are the results obtained based on the electroencephalogram data of a large number of known depression patients under different emotional audio stimuli, and are used as basic index parameters of a feedback music type reference library. () The symbols a, b, d, and t in the inside represent frequency bands, which are selected by a segmentation algorithm, and represent that the frequency bands are disordered at this time, for example, (ab) and (ba) are the same.
The feedback training adjusting module comprises the following steps of music feedback training: selecting feedback music which is adaptive to the required feedback music type according to the acquired feedback music type required by the trainee, and adjusting the emotion of the trainee to be maintained in a positive and relaxed state; the electroencephalogram signal character sequence with the positive label represents that the physiological and psychological states of the depression patient are positive emotional states, and corresponding feedback music is selected to be neutral and slightly positive music stimulation; the electroencephalogram signal character sequence with the negative label represents that the physiological and psychological state of the depression patient is a negative emotional state, and corresponding feedback music is selected to be positive music stimulation, so that the emotion of the depression patient is raised to be positive; the electroencephalogram signal character sequence with the neutral label shows that the physiological and psychological states of the depression patient are normal, and the corresponding feedback music is selected to be music stimulation with positive polarity and neutral polarity.
The feedback training adjustment module further comprises a step of respiratory training: the trainee is led to focus on breathing and to perform a digital inversion of the difference in the rhythm of breathing until the end of the number.
The data storage module comprises creation, inquiry, modification, deletion and data storage of trainee information, and the system carries out emotion adjustment process and result on the trainee. All data is stored by the database support system (DBSS). The data storage is mainly divided into two types, the first type is basic information of a person receiving training, and the first type comprises the following steps: the number of testers, name, gender, age, protocol under training, number of times under training, contact address, etc. The second category is the electroencephalographic data of the subject at the last training session. The first category data is essentially unchanged, except for the protocol and number of trainings being varied. The second type of data can change frequently, and only the electroencephalogram signal data during the last training is stored. In addition, the system can also store the data as a file in an XML format for later network transmission and remote calling, and then result evaluation and data mining are carried out.
Fig. 7 is a flow chart of the work of the music feedback depression mood regulating system based on electroencephalogram signals. The method comprises the following steps:
1) acquiring an electroencephalogram signal: collecting electroencephalogram signals of three electrode positions including FP1, FP2 and FPz of a subject in a normal state;
2) brain electric data processing: carrying out data processing on the acquired electroencephalogram signals;
3) generating feedback music: segmenting and integrating the electroencephalogram signals to obtain a feedback music character sequence corresponding to the electroencephalogram signals of the testee, and judging corresponding emotion labels;
4) music feedback training: selecting feedback music matched with the emotion label to perform music feedback training on the subject,
5) analysis of subject mood: the mood of the subject is adjusted to be maintained in an active, relaxed state.
The invention designs a biological information feedback depression mood adjusting method taking music as a feedback means for depression patients, and the adjusting process is as follows: firstly, the expert uses medical instruments to measure physiological or pathological information of hypoesthesia or cognitive error of the depression patient, and combines the intrinsic biological information with external physical performance to intuitively feed back the intrinsic biological information to the patient through perception modes such as vision, hearing and the like. The intrinsic biological information is the electroencephalogram signal measured by the medical instrument, and the intrinsic biological information is a music signal converted from the electroencephalogram signal by the segmentation and integration treatment in a visual feedback mode. Then, the expert determines the physiological and psychological state of the patient himself from the music information received by the depression patient, for whom a specific feedback training is designed. Finally, through feedback training for a certain time, the depression patient can clearly determine the physiological or pathological state of the patient through feedback music, can freely control certain physiological functions, and can realize quick adjustment of certain simple emotions. The specific feedback training process is as follows:
the training process is divided into two parts: respiratory training, music training.
The first part of respiratory training mainly uses a respiratory regulation method:
① during the whole training process, we need to ensure the quiet of the experimental environment to eliminate the effect of the experimental environment on the feedback training, we let the depressed patients sit on the stool, while both feet are flat on the ground, both hands are naturally placed on the thighs, and the upper half of the body is kept in an upright sitting posture, ② the depressed patients close the eyes, feel and relax the body, but to avoid the depressed patients to get to sleep, we require the back to keep straight during the whole training process, and the spirit is in a waking state, ③ guides the depressed patients to focus on the breath, and to perform differential reciprocal counting according to the rhythm of breath, such as the number of the inhaled times is 10, the number of the next exhaled times is 8, … …, until the number is finished, if the depressed patients make a mistake during the reciprocal counting, the depressed patients should timely adjust the attention, ④ the depressed patients should focus on the mental and relaxed state of the depressed patients due to the training time, so as to avoid the overlong hand and foot numbness, emotional feeling, the mind and the relaxed state of the depressed patients during the breathing training process, and the mental state of the relaxed state of the depressed patients can be achieved.
The second part of music training takes music feedback as a main regulation method, and takes an imagination relaxation method as an auxiliary regulation method:
① during the music training process, we let the depression patients sit on the stool and keep a relatively comfortable sitting posture, we will wear the electroencephalogram acquisition equipment for the depression patients and explain the function and operation process of the system, let the depression patients make sure the physiological and psychological states represented by each feedback music, ② the depression patients will receive music signals with their own physiological or psychological state information from the data processing module, these music signals are feedback music pieces with certain emotional stimulation selected specially by the test person group, these music signals will be generated in real time according to the change of the own electroencephalogram signal, ③ will maintain the mood of the depression patients in a positive and relaxed state through the stable stimulation of these feedback music and the imagination of the expert.
The generation of the feedback music is determined according to the electroencephalogram signal character sequence with positive, negative and neutral emotion labels. Each section of electroencephalogram signal can obtain an electroencephalogram signal character sequence through a segmentation and integration algorithm, the emotion label of the electroencephalogram signal of the trainee is judged by comparing the times of electroencephalogram character segments of positive, neutral and negative feedback music types generated in the feedback music generation module appearing in the electroencephalogram signal character sequence of the trainee, and the feedback music type required by the trainee is generated, wherein the electroencephalogram signal character sequence with the positive label represents that the physiological psychological state of the tristimania patient is a positive emotional state, and the corresponding music segment is music stimulation with neutral and positive polarity. This stimulus need not be too intense and can maintain the emotional state of the depressed patient. And the electroencephalogram signal character sequence with the negative label represents that the physiological and psychological states of the depression patient are negative emotional states, and the corresponding music segment is positive music stimulation. In this state, the depressed patients need strong positive stimulation to raise their emotion to a positive state. In addition, the electroencephalogram character sequence with a neutral label indicates that the physiological and psychological states of the depression patients are normal states, namely no likes and sads, and the corresponding music segments should be music stimuli with positive polarity and neutral bias. Different international classical music has corresponding emotion types, the classical music corresponding to representative emotion states is selected, and an emotion type feedback music library is constructed. The musical stimuli for intervention in musical training are selected from the built music library. This generates some type of feedback music required by the patient, i.e. the second step of the musical training is completed.
The invention adopts a music intervention method to regulate the depressed mood, and the action mechanism of the music intervention is as follows:
physiological/physical effects. Music can cause various physiological reactions, such as blood pressure reduction, respiration slowing, skin temperature rise, muscle potential reduction, blood vessel volume increase, blood norepinephrine and epinephrine content reduction, and the like, thereby obviously promoting homeostasis of a human body, reducing stress and anxiety, and promoting relaxation. Research shows that auditory information influences the activity of amygdala, hippocampus and the like, a network is formed among the edge system structures, and the network plays a crucial role in emotional processing.
Interpersonal/social effects. Music is a social non-language communication artistic form, and music activities (including singing, musical instrument playing, creation and the like) are social interaction activities per se, and people can feel safe and pleasant interpersonal interaction environments in the music activities, so that the language ability, the correct social behaviors, the ability of cooperating with other people and the confidence and self evaluation are improved.
Psychological/emotional effects. Music has a great influence on human emotions, and music therapists change the human emotions through music and finally change the cognition and the behavior of people. The music plays a unique catalytic role, the music is used for regulating emotion not aiming at the increase of the regulated person in the aspect of music ability, but is used for changing the emotion, behavior and thought concept of the treated person through the psychological experience of the music, and the psychology of the patient is improved and grown through the changes, so that the music has stronger adaptability to the environment.
Depression is also known as depressive disorder, and is characterized clinically by marked and persistent mood depression. The symptoms of patients with depressive disorders are diverse and can be divided into affective and somatic symptoms. Affective symptoms are generally the most significant and prevalent manifestations of symptoms in patients with depressive disorders, including: depressed mood, anhedonia, guilt, low self-esteem, etc. The physical symptoms of patients with depressive disorders are diverse and involve symptoms of various organs of the body. The manifestation of symptoms is influenced by various factors, both physiological and psychological. Congenital depression has genetic tendency, acquired depression is accompanied by stress reaction and change of cranial nerve structure, and long-term tension and sadness can cause damage and atrophy of neurons in the brain structure, most obviously, hippocampus.
Stress causes atrophy of the limbic system leading to depression, and the hypothalamic-pituitary-adrenal (HPA) axis is a complex collection of limbic structures whose primary functions are to control stress and regulate physical activity. Under normal circumstances, the process of regulation of the hypothalamic-pituitary-adrenal axis is as follows, see fig. 8: as a regulatory center of the endocrine system, secretion of the hormone releasing hormone by the hypothalamus causes the pituitary to secrete the hormone. These pro-hormones will follow the blood flow to reach different endocrine glands and promote the secretion of hormones from the respective endocrine glands. When there are excessive hormones in the blood and the homeostasis is disturbed, these hormones in turn inhibit the function of the hypothalamus and pituitary. When the organism is stressed, the glucocorticoid level is increased circularly, but some areas of the brain of the patient with depression tendency, such as thalamus, hippocampus, etc., may be stimulated by glucocorticoid with high concentration for a long time to reduce the number of synapses, which will seriously cause the apoptosis of neurons in the hippocampus. Atrophy of the hippocampus is associated with abnormal activity of the hypothalamic-pituitary-adrenal axis, increased glucocorticoid levels, and disruption of negative feedback regulation mechanisms. Atrophy of these brain structures will cause some endocrine systems to fail to operate properly, causing emotional disturbances and weakening the body's ability to respond correctly to stimuli, causing people to be in a negative mood such as extreme stress, anxiety, etc.
As shown in fig. 9, it is a diagram of the action mechanism of music feedback to regulate the mood of the depressed patients.
Prolonged mood depression is a major symptom in depressed patients, which makes the perception pathway of depressed patients different from that of normal persons. The perception path comprises emotion and physiological reactions of external information caused by the perception of the external information, so that the brain marginal system reaction is influenced, and a series of physiological reactions are caused by negative feedback regulation of an HPA axis; symptoms of depression can also lead to patients more easily perceiving external stimuli such as music, pictures and videos with sad emotions. When the patient is continuously stimulated by the negative emotional information, the depression patient often has helplessness and useless feelings on the basis of the depressed mood, and the hallucinations occur in the serious cases of self-help and self-low. At the same time, their psychological reactions become depressed mood, anxiety, self-negative, and the like. Thus, the processing of the received stimulation signals by the limbic system structures of the brain of depressed patients has long been associated with increased glucocorticoid levels and decreased numbers of neurosynaptic events, and, in severe cases, neuronal apoptosis in the prefrontal lobe, hypothalamus, and hippocampus, under conditions of constant negative emotion. Further undermining the negative feedback regulation mechanism of the HPA axis. At this time, the brain activity is abnormally disordered and the stress capability is poor, so that a series of physiological changes such as endocrine dyscrasia, serious emotion influence and the like of a patient are caused. Aiming at the physiological reactions, the invention designs a music feedback depression mood regulating system based on the electroencephalogram signals. In a feedback loop, music signals mapped by electroencephalograms of patients are found by collecting the electroencephalograms of the patients and using the electroencephalograms as emotion monitoring signals of the patients with depression through the segmentation and integration algorithm of the electroencephalograms, and feedback music is generated according to the property categories of the music signals; the method for converting the electroencephalogram biological signals into music signals which can be understood by the depression patients can enable the brain biological signals to be clear of the physiological and psychological states of the patients through music feedback and sense internal information, on the basis, the system designs a series of training schemes comprising breathing training and music training for the patients to perform music feedback regulation training, the emotion and psychological reactions of the internal information are reacted to cause brain edge system reactions, and then the physiological reactions are regulated through negative feedback regulation of an HPA axis. When a depression patient receives music feedback emotion regulation training, parasympathetic nerves release inhibitory hormones such as acetylcholine, so that the level of various hormones in blood is reduced, homeostasis such as blood pressure reduction, breathing slowing, stress anxiety reduction and internal environment homeostasis maintenance is obviously promoted, and negative emotion regulation is realized.
It should be noted that the above-mentioned embodiments enable a person skilled in the art to more fully understand the invention, without restricting it in any way. All technical solutions and modifications thereof without departing from the spirit and scope of the present invention are covered by the protection scope of the present invention.

Claims (10)

1. A music feedback depression mood regulating system based on electroencephalogram signals is characterized by comprising: the system comprises an electroencephalogram signal acquisition module, an electroencephalogram signal data processing module, a feedback music generation module, a feedback training adjustment module and a data storage and analysis module; the electroencephalogram signal acquisition module is used for acquiring resting electroencephalogram signals of the trainee; the electroencephalogram signal data processing module is used for preprocessing the acquired electroencephalogram signals, the feedback music generating module is used for dividing and integrating the preprocessed electroencephalogram signals, analyzing the mapping relation between the electroencephalogram signals and the music signals, and comparing the electroencephalogram signals and the music signals in the constructed feedback music type reference library to obtain feedback music types for music feedback training; the feedback training adjusting module is used for performing feedback training on the trainee by adopting feedback music which is adaptive to the type of the feedback music to realize adjustment of depression emotion; the data storage and analysis module is used for storing and analyzing the process and the result of the trainee emotion regulation.
2. The system for regulating music feedback depression mood according to claim 1, characterized in that the electroencephalogram signal acquisition module adopts a three-lead system, the electrode position adopts a 10-20 system electrode method widely adopted internationally, and the selected electrodes are respectively: fp1, Fp2 and Fpz are positioned at the forehead, are not interfered by hairs, and medical patch type wet electrodes are used, so that the interference of contact impedance of the electrodes is avoided.
3. The system for regulating music feedback depression mood according to electroencephalogram signal, characterized in that the electroencephalogram signal data processing module comprises the step of carrying out segmented processing on the acquired electroencephalogram signal: and intercepting the acquired continuous electroencephalogram signals into electroencephalogram signal segments with proper length, wherein each segment of electroencephalogram data is overlapped with part of electroencephalogram data of the previous segment.
4. The music feedback depression mood regulating system based on electroencephalogram signals as claimed in claim 3, wherein the electroencephalogram signal data processing module comprises a step of denoising and filtering segmented electroencephalogram signals, wherein the step of denoising and filtering is carried out by adopting improved dynamic AR model parameters and wavelet analysis, the frequency bands lower than 0.5Hz and higher than 50Hz in the electroencephalogram signals are removed by using an FIR band-pass filter, and the step of dividing the electroencephalogram signals according to frequency and extracting waveform characteristics of four frequency bands of the electroencephalogram signals delta, theta and α is further included.
5. The system for regulating music feedback depression mood according to electroencephalogram signals, as claimed in claim 4, characterized in that the feedback music generation module comprises a step of dividing electroencephalogram data, wherein each frequency band is represented by corresponding characters d, T, a and b, wherein d represents a delta band, T represents a theta band, a represents an α band, and b represents a β band, an average value mu and a standard deviation sigma of waveform data of each frequency band are calculated, then, a division threshold of each frequency band is calculated respectively, an optimal threshold is selected from a plurality of division thresholds, a calculation formula of the division threshold is that T is mu +/-n-sigma, mu represents an average value of waveform data of each frequency band after frequency division, sigma represents a standard deviation of waveform data of each frequency band after frequency division, n represents a ratio of the average value to the standard deviation, and is used for calculating the division threshold under different ratios of the average value to the standard deviation and selecting the optimal threshold, finally, a waveform diagram of electroencephalogram signals is represented by corresponding four frequency bands, and time analysis points corresponding to the frequency bands are obtained.
6. The electroencephalogram signal based music feedback depressive mood regulation system according to claim 5, wherein the feedback music generation module includes a step of electroencephalogram segment integration: according to the electroencephalogram signal time sequence relation corresponding to each frequency band obtained by a segmentation algorithm, comparing which frequency bands are contained at the same time point, adding the characters represented by the frequency bands into a time sequence, and overlapping and arranging the characters corresponding to the four frequency bands in the sequence to form a complete electroencephalogram character sequence.
7. The electroencephalograph signal based music feedback depressive mood modulation system according to claim 6, wherein the feedback music generation module includes a step of constructing a reference library of feedback music types: firstly, acquiring depression electroencephalogram signals of known depression patients under positive, neutral and negative specific emotion audio stimulation, and carrying out frequency band division, electroencephalogram data segmentation and electroencephalogram segment integration on the acquired electroencephalogram signals to obtain an integrated electroencephalogram character sequence; according to the stimulation of corresponding positive, neutral and negative different emotion audios, positive, neutral and negative emotion labels are respectively marked on the integrated electroencephalogram character sequences, specific cyclic segments of the electroencephalogram character sequences marked with the different emotion labels exist in the whole electroencephalogram signal, the cyclic segment of the electroencephalogram character sequence with the largest frequency of occurrence within a certain time is selected as a feedback music type corresponding to the emotion label, and three feedback music types of positive, neutral and negative are obtained.
8. The electroencephalograph signal based music feedback depressive mood modulation system according to claim 7, wherein the feedback music generation module includes a step of comparison in a constructed reference library of feedback music types: and judging the emotion label of the electroencephalogram signal of the trainee by comparing the times of the electroencephalogram character segments of positive, neutral and negative feedback music types in the constructed feedback music reference library appearing in the electroencephalogram signal character sequence of the trainee, and generating the feedback music type required by the trainee.
9. The electroencephalograph signal based music feedback depressive mood adjustment system according to claim 8, wherein the feedback training adjustment module includes a step of music feedback training: selecting feedback music which is adaptive to the required feedback music type according to the acquired feedback music type required by the trainee, and adjusting the emotion of the trainee to be maintained in a positive and relaxed state; the electroencephalogram signal character sequence with the positive label represents that the physiological and psychological states of the depression patient are positive emotional states, and corresponding feedback music is selected to be neutral and slightly positive music stimulation; the electroencephalogram signal character sequence with the negative label represents that the physiological and psychological state of the depression patient is a negative emotional state, and corresponding feedback music is selected to be positive music stimulation, so that the emotion of the depression patient is raised to be positive; the electroencephalogram signal character sequence with the neutral label shows that the physiological and psychological states of the depression patient are normal, and the corresponding feedback music is selected to be music stimulation with positive polarity and neutral polarity.
10. The EEG-based music feedback depressive mood regulation system according to one of claims 1 to 9, characterized in that the feedback training regulation module comprises a step of breathing training: the trainee is led to focus on breathing and to perform a digital inversion of the difference in the rhythm of breathing until the end of the number.
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