Music adjusting system for depressive mood

Abstract

The utility model provides a music adjusting system for depressive mood. The music adjusting system comprises a signal acquisition module, a music playing module and a music playing module, wherein the signal acquisition module is used for acquiring electroencephalogram signals and electrocardiosignals; the signal processing module is used for processing the electroencephalogram signals and the electrocardiosignals acquired by the signal acquisition module; the interaction module is used for acquiring emotion evaluation information; the adjusting module is used for playing a processing result of the signal processing module and music corresponding to the emotional condition evaluation information; and the feedback module is used for outputting the processing result and the emotion change in the adjustment process of the played music. According to the system, music parameters are comprehensively regulated and controlled by obtaining effective physiological data and results of traditional Chinese medicine five-state emotional questionnaires, so that real-time regulation and control of user emotional states are achieved, the brain-computer interaction efficiency is improved, and depressive symptoms are effectively relieved; meanwhile, the related functions of acquisition, analysis and processing and music playing of the equipment are realized on the microcontroller and the peripheral circuit, so that the overall power consumption of the equipment is reduced, and the portability of the equipment is improved.

Description

Technical Field

[0001] The present application belongs to the field of bioelectronic technology and medical auxiliary intervention system technology, and in particular, relates to a music regulation system for depressive emotions. Background Art

[0002] Emotion is a person's attitude experience and corresponding behavioral response to objective events. It is affected by various factors in the objective world and also affects a person's thinking, behavior and physical health. In particular, negative emotions received from the outside world will affect a person's psychological and physiological health and lead to the occurrence of diseases. The fast-paced and high-pressure characteristics of today's society have exacerbated the occurrence of a series of mental disorders caused by emotions, such as depression and anxiety. Therefore, timely emotional state assessment and effective emotional regulation are very necessary. Accurate emotional assessment can help users better understand their emotional state so as to better deal with their emotions. Currently, the commonly used assessment methods include self-report method, behavioral observation method, cognitive task method and physiological indicator method. Among them, the physiological indicator method judges the emotions of the subject through indicators such as EEG, ECG, and skin conductivity. In particular, the emotion assessment method based on electrophysiological signals (Electroencepha logram (EEG) and electrocardiogram (ECG)) has been widely used because it objectively and accurately describes the macroscopic manifestations of the central nervous system and autonomic nervous system related to emotions.

[0003] EEG is a medical imaging technique that can measure the scalp electrical activity generated by the brain and record the potential fluctuations caused by the ion current in the neurons in the brain in a chronological order. EEG is divided into different bands according to frequency to correspond to different physiological and psychological states. Different external stimuli will cause energy changes in different bands. For example, in the state of music, the brain energy shows a significant decrease in the α segment and an increase in the θ segment. Moreover, compared with unpleasant music, pleasant music can cause more θ waves in the frontal region. The physiological activity of the brain can directly reflect the emotional activity of an individual. Researchers point out that emotional changes can lead to physiological changes, including brain activity and autonomic nervous system (ANS) activity. In modern medicine, the brain belongs to the nervous system and belongs to the central nervous system, and the heart belongs to the circulatory system and belongs to the autonomic nervous system. The extensive neural connection between the brain and the heart constitutes the physiological basis that affects the heart-to-brain pathway. For example, the brain directly controls the heart through the sympathetic and parasympathetic branches of the autonomic nervous system. Heartbeat evoked potential (HEP) is a signal from the heart to the brain with characteristic changes in brain waves, which reflects the cortical processing of cardiac interoception. HEP can sense emotions and assess emotional states and their changes by measuring the synchronization and dynamic interaction between the brain and heart.

[0004] Music is a means of self-expression and emotional release. It is composed of basic elements that affect people's emotional experience. People are affected by music in various ways, including listening to music, singing, playing musical instruments, music creation, and music training. All these different forms of "music interaction" become a means of intervention in the context of a therapeutic relationship. Studies have shown that music intervention can trigger physiological and psychological reactions in the human body, so music intervention has been widely used in the intervention treatment of some mental disorders. When participants appreciate music, implementers need to fully observe their emotional changes, physiological reactions, and psychological reactions, and adjust the music repertoire in a timely manner according to these reactions to give full play to the role of music therapy.

[0005] Chinese utility model patent: ZL202111375419.1 A headphone-type emotional stress regulation system based on high-frequency electrocardiogram, discloses a headphone-type emotional stress regulation device based on high-frequency electrocardiogram, wherein the headphone-type high-frequency electrocardiogram signal collects the user's electrocardiogram signal and skin sympathetic nerve activity signal, and preprocesses and extracts features of the collected signals to obtain the user's emotional label; the emotion improvement unit is divided into a music feedback module and an electrical stimulation module, and the two functions can be used separately or simultaneously for emotional intervention. Chinese utility model patent: ZL202211538278.5 Mental stress assessment and relief system and method based on music-coordinated electrical stimulation, discloses a mental stress assessment and relief system based on music-coordinated electrical stimulation, including an EEG acquisition module, an EEG analysis module, a music playback module and a transcranial electrical stimulation module; wherein the EEG acquisition module is used to collect and process the user's EEG signals; the EEG analysis module is used to analyze the EEG conversion signals and detect and judge the user's stress state; the music playback module and the transcranial electrical stimulation module are used to play music and output stimulation current to the user when the user is in a high-voltage state to relieve the user's mental stress. In terms of analysis methods, some recent studies have attempted to use mathematical methods to combine brain data with peripheral signals related to autonomic nervous system activity. The detection of the coupling of the two systems can rely on the estimation of information transmission between the two coupled oscillators. Some researchers have also applied informatics methods to study this field, using synthetic data generation models to couple the cortical activity physiological excitation model and the heartbeat generation model in multiple systems to achieve the construction of a brain-heart interaction model.

[0006] After sorting out the existing technologies, the following problems still exist:

[0007] 1. Although research in neuroscience, psychology, and cognitive science has shown that most psychological activities and cognitive behaviors can be expressed through EEG, the analysis of a single physiological system of the brain or heart ignores the complex multivariate information under the brain-heart interaction. This complexity and diversity cannot be displayed by a single subsystem. Most of the existing solutions for using physiological signals to evaluate emotional states only involve one physiological signal, such as using EEG or ECG signals alone as the basis for evaluating the user's emotional state. However, a single physiological signal is not convincing enough in evaluating the user's emotional state level, and it is necessary to combine multiple physiological signals for comprehensive and accurate evaluation.

[0008] 2. Most of the current physiological signal acquisition devices are large and expensive, with poor portability and are easily restricted by the environment. In addition, there are some portable devices, but either the acquisition function is relatively simple or the acquisition, analysis, evaluation and feedback adjustment functions are not taken into account. There is a lack of a comprehensive system and portable devices that integrate monitoring and treatment, which is a poor experience for users.

[0009] 3. Most of the existing ECG signal acquisition devices use disposable Ag / AgCl wet electrodes, which require the electrodes to be in direct and close contact with the skin, and a few use dry electrodes. This will increase the discomfort caused to the user during the acquisition process, and in severe cases, the user's skin may be allergic. At the same time, it is also a challenge to determine the position of such wet electrodes when using them, which requires professional guidance and is not universal.

[0010] 4. In terms of analysis, the brain and heart have long been studied separately through specialized techniques and methods that characterize specific single-system dynamics at the cortical / subcortical or peripheral level. With the development of multimodal data, the dynamic data of the brain and heart are collected simultaneously, but the data analysis is performed separately. Even if there is a need, researchers have provided a series of methods to describe the coupling between the two systems, which leads to the lack of mutual information between the two signals. In summary, portable devices and monitoring methods for this mutual information are particularly necessary.

[0011] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Utility Model Content

[0012] In view of the shortcomings of the prior art mentioned above, the utility model provides a music regulation system for depressive emotions, which selects corresponding music to play to regulate the user's emotions by collecting EEG signals and ECG signals and obtaining user emotion assessment results through questionnaires.

[0013] The present application provides a music regulation system for depressive emotions, the system comprising: a signal acquisition module for collecting electroencephalogram signals and electrocardiogram signals; a signal processing module for processing the electroencephalogram signals and electrocardiogram signals collected by the signal acquisition module; an interaction module for acquiring emotion assessment information; an adjustment module for playing the analysis results of the signal processing module and the music corresponding to the emotion status assessment information; and a feedback module for outputting the processing results and the emotion changes during the music adjustment process.

[0014] According to one embodiment of the present application, the signal acquisition module includes an ECG acquisition unit for acquiring ECG signals, including: a non-contact electrode for acquiring ECG signals by forming a coupling capacitance with the skin; an instrument amplifier for suppressing the common-mode signal of the human body by increasing the input impedance of the ECG signal input channel; a first signal preprocessing circuit for amplifying and denoising the ECG signal; a first analog-to-digital converter for converting the processed ECG signal into an ECG digital signal; a capacitance detection circuit for detecting the size of the coupling capacitance formed between the non-contact electrode and the skin; and a first microcontroller for controlling the first wireless transmission module to output the ECG digital signal.

[0015] According to one embodiment of the present application, the signal acquisition module includes an EEG acquisition unit for collecting EEG signals, including: stick-type electrodes for collecting EEG signals; a second signal preprocessing circuit for amplifying and denoising the EEG signals; a second analog-to-digital converter for converting the processed EEG signals into EEG digital signals; and a second microcontroller for controlling the second wireless transmission module to output the EEG digital signals.

[0016] According to one embodiment of the present application, the first signal preprocessing circuit and / or the second preprocessing circuit include: a high-pass filter for removing the DC component in the ECG signal; an amplifier circuit for gaining the ECG signal with the DC component removed; and a low-pass filter for removing 50Hz power frequency interference and high-frequency electromyographic interference noise.

[0017] According to one embodiment of the present application, the signal analysis module includes: a denoising unit for denoising the physiological electrical signal; a feature extraction unit for extracting emotion feature index information from the denoised physiological electrical signal; a classification unit for classifying the emotion feature index information; and a learning unit for learning and establishing a model based on the classified emotion feature index information.

[0018] According to one embodiment of the present application, the denoising unit includes: an EEG signal denoising component for removing electrooculogram artifacts; and an EEG signal denoising component for adjusting the frequency of the EEG signal to be consistent with the frequency of the EEG signal.

[0019] According to one embodiment of the present application, the interaction module includes: an output unit for outputting emotion assessment information; and an input unit for a user to perform a selection operation.

[0020] According to one embodiment of the present application, the adjustment module includes: a database for storing music; and a playback unit for reading music from the database for playback.

[0021] According to one embodiment of the present application, the feedback module includes: an interface unit for outputting the processing result and the emotional change; and a display unit for receiving the analysis result and the emotional change from the interface unit and displaying them.

[0022] According to one embodiment of the present application, the feedback module also includes: a communication unit for sending the processing results and the emotional changes to a remote device via a mobile network; the communication unit includes: a WiFi component and a 4G / 5G component.

[0023] As described above, the music regulation system for depression of the present application has the following beneficial effects:

[0024] 1. The utility model not only collects EEG and ECG signals simultaneously, but also systematically and comprehensively analyzes the time domain and frequency domain characteristics of the two physiological signals to improve the accuracy of the evaluation results. At the same time, it uses heartbeat evoked potential (HEP) as an indicator of cortical processing of cardiac signals to explore the interaction between cardiac afferent signals and related cerebral cortical processing during music regulation, and explores the potential connection between the emotional state regulation process and cortical processing of cardiac signals based on the changes in HEP ​​under music intervention.

[0025] 2. The utility model not only integrates the emotional questionnaire with Chinese medicine characteristics, but also the traditional Chinese medicine theory on emotion is based on the Chinese historical and cultural foundation, which is more in line with the cultural and language characteristics of our users. At the same time, the system can comprehensively and accurately evaluate the user's emotional state based on the results of the emotional questionnaire and objective physiological characteristics. According to the emotional state and the theoretical guidance of "selecting music according to emotional dialectics" in Chinese medicine, different types of music can be adjusted for the user, forming a closed-loop feedback and adjusting the user's emotions in real time. This flexible selection of multiple evaluation methods is more conducive to accurately and comprehensively studying and explaining human psychological movements and emotional states.

[0026] 3. In view of the problem that the existing ECG signal acquisition schemes easily cause discomfort to users, the utility model uses multiple capacitive coupling electrodes. The electrodes do not need to directly contact the human skin. They are separated from the skin by a single layer of clothing to collect multi-channel ECG signals through coupling, which reduces the discomfort caused to users during the acquisition process. At the same time, it has high universality. Users only need to wear it like wearing clothes, and do not need to learn professional knowledge to determine the placement of the electrodes. The purpose of collecting multi-channel ECG signals is to improve the quality of the collected ECG signals. The system automatically selects the best channel by detecting the size of the coupling capacitance formed between the electrodes and the human body.

[0027] 4. The utility model realizes the multi-channel acquisition of non-contact ECG signals, and can obtain multi-channel ECG signals while reducing the discomfort of the subjects; at the same time, a channel selection mechanism and a signal quality evaluation algorithm are added to ensure that the collected ECG signals have high quality.

[0028] 5. The collection and analysis algorithms involved in this utility model, as well as the music playback and other functions, are all implemented on the STM32F4 series microcontroller and peripheral circuits, which reduces the overall power consumption of the device and improves the portability of the device. It does not require a host computer and has high real-time performance. The system has very low power consumption and can run stably for a long time; at the same time, the system is small in size and easy to wear and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 This is a schematic diagram of the structure of the music regulation system for depression according to an embodiment of the present application.

[0031] Figure 2 This is a schematic diagram of the structure of the music regulation system for depression according to an embodiment of the present application.

[0032] Figure 3 This is a schematic diagram of the physical structure of the music regulation system for depression according to an embodiment of the present application.

[0033] Figure 4 This is a circuit diagram of a non-contact ECG electrode according to an embodiment of the present application.

[0034] Figure 5 This is a schematic diagram of the signal processing flow of an embodiment of the present application.

[0035] Figure 6 This is a flow chart of the music regulation method for depressive mood according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0037] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0038] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in this article, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this article, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination". Furthermore, as used in this article, the singular forms "one", "one" and "the" are intended to also include plural forms, unless there is an opposite indication in the context. It should be further understood that the terms "comprising" and "including" indicate the presence of the described features, steps, operations, elements, components, projects, kinds, and / or groups, but do not exclude the presence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds, and / or groups. The terms "or", "and / or", "including at least one of the following" used in this application, etc. can be interpreted as inclusive, or mean any one or any combination. For example, “comprising at least one of the following: A, B, C” means “any of the following: A; B; C; A and B; A and C; B and C; A and B and C”, and for another example, “A, B or C” or “A, B and / or C” means “any of the following: A; B; C; A and B; A and C; B and C; A and B and C”. An exception to this definition will only occur when a combination of elements, functions, steps or operations are inherently mutually exclusive in some manner.

[0039] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0040] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0041] It should be noted that, in this article, step codes such as S1, S2, etc. are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial limitation on the order. When implementing the step, those skilled in the art may execute S4 first and then S3, etc., but these should all be within the scope of protection of this application.

[0042] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0043] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0044] refer to Figure 1 and Figure 2 As shown, an embodiment of the present application provides a music regulation system for depressive emotions, the system comprising: a signal acquisition module, a signal processing module, an interaction module, a regulation module and a feedback module.

[0045] In an embodiment of the present application, before music regulation of the user's emotions is performed based on the present system, the signal acquisition module is first used to collect the EEG signal at the user's forehead, and to collect the ECG signal at the chest using a capacitive coupling electrode; in an embodiment of the present application, in order to more accurately collect the user's physiological electrical signals, when collecting EEG signals, the EEG signals at the three lead positions on the user's forehead are collected, and when collecting the user's ECG signals, multiple capacitive coupling electrodes are used to collect multiple-channel ECG signals at the user's chest. By acquiring EEG signals and ECG signals at multiple different positions, the collected user's physiological electrical signals can be made more accurate.

[0046] After the system completes the collection of the user's physiological electrical signals through the signal acquisition module, it further uses the signal processing module to remove noise from the collected EEG signals and ECG signals, and analyzes and learns the characteristics of the two physiological electrical signals, and finally determines the user's emotional information based on the currently collected EEG signals and ECG signals.

[0047] Before starting music adjustment, the system also collects the user's current emotional information in the form of a questionnaire through the interactive module. For example, the five emotions of traditional Chinese medicine are presented in the form of a questionnaire. The emotional assessment information of the user's current emotion is finally obtained through the user's selection results on the questionnaire.

[0048] After obtaining the user's current emotion assessment information through the interaction module and processing the physiological electrical signal through the signal processing module, the processing result is obtained, and the corresponding music is selected and played through the adjustment module to adjust the user's emotion.

[0049] In the process of regulating the user's emotions through the regulation module, the system also outputs the emotion information obtained by post-processing the real-time collected physiological electrical signals through the feedback module, so as to timely observe the real-time emotion information of the user during the regulation process.

[0050] In one embodiment of the present application, the ECG acquisition unit includes: a non-contact electrode for collecting ECG signals by forming a coupling capacitance with the skin; an instrument amplifier for suppressing the common mode signal of the human body by increasing the input impedance of the ECG signal input channel; a first signal preprocessing circuit for amplifying and denoising the ECG signal; a first analog-to-digital converter for converting the processed ECG signal into an ECG digital signal; a capacitance detection circuit for detecting the size of the coupling capacitance formed between the non-contact electrode and the skin; and a first microcontroller for controlling the first wireless transmission module to output the ECG digital signal.

[0051] refer to Figure 3As shown, in the embodiment of the present application, in order to meet the requirements of wearable and portable, the ECG acquisition unit adopts a non-contact ECG signal acquisition unit, and the non-contact ECG signal acquisition unit is integrated into a stretch vest. The non-contact ECG signal acquisition unit has multiple input channels. When using, it can be directly put on and adjusted to the appropriate tightness, which improves the universality of product use. At the same time, the ECG signal acquisition unit introduces a channel selection function to automatically select an input channel with better signal quality. In a preferred implementation, since the existing ECG signal acquisition scheme has the problems of easily causing discomfort to the subject and cumbersome acquisition steps, the present system adopts a non-contact electrode acquisition scheme. The non-contact electrode is a capacitive electrode, which can collect ECG signals by forming a coupling capacitor with the human skin without direct contact with the human skin, avoiding the shortcomings of the traditional ECG signal acquisition scheme. Since the ECG signal is a low-frequency signal, the equivalent impedance formed between the capacitive electrode and the human body is very high. In order to avoid excessive attenuation of the input signal amplitude, a voltage follower based on bootstrap technology and a capacitor neutralization circuit is integrated on the back of the capacitive electrode to increase the input impedance. Electrode schematic diagram and physical shape Figure 4 As shown. In the embodiment of the present application, since the ECG signal collected directly through the non-contact electrode is a differential signal, the instrument amplifier is further used as the analog input front end to improve the common mode rejection ratio of the circuit to suppress the interference of the common mode signal of the human body. Further, the ECG signal is amplified and denoised by a signal preprocessing circuit. In the embodiment of the present application, the signal preprocessing circuit is composed of a high-pass filter, an amplifier circuit and a low-pass filter, wherein the high-pass filter is a first-order RC high-pass filter circuit with a cut-off frequency of 0.5Hz, which is used to remove the DC component in the ECG signal; then the weak physiological signal after the DC component is removed is amplified in phase with a gain of 50 through the amplifier circuit; and then the noise such as 50Hz power frequency interference and high-frequency electromyographic interference is removed through a low-pass filter. In a preferred embodiment, the low-pass filter is a Bessel active low-pass filter with a cut-off frequency of 45Hz. The linear phase characteristics of the Bessel filter can reduce the degree of distortion of the signal during the filtering process. After the collected ECG signal is denoised by the signal preprocessing circuit, the denoised ECG signal is converted into an ECG digital signal by an analog-to-digital converter for subsequent analysis and processing. In a preferred implementation, since the physiological electrical signal of the human body is a weak signal with a very small amplitude, the typical amplitude of the ECG signal is 1mV, and the amplitude of the EEG signal is even smaller, only at the uV level. In order to ensure the accuracy of the collected signal, a high-precision, low-noise 24-bit analog-to-digital converter ADS1299 is used. At the same time, ADS1299 has 8 independent channels and input selectors to meet the needs of multi-channel signal acquisition.

[0052] In an embodiment of the present application, the ECG acquisition unit also includes a capacitance detection circuit for detecting the size of the coupling capacitance formed between the above-mentioned non-contact electrode and the human body. When the elastic garment integrated with the non-contact ECG acquisition unit is put on, since each person's body posture cannot be exactly the same, the contact degree between the capacitive electrode and the human body is not exactly the same, so the quality of the collected signal is also uneven. In order to solve this problem, the system uses multiple pairs of capacitive electrodes, distributed on both sides of the heart position, to ensure the quality of the collected signal to a certain extent. And the size of the coupling capacitance formed between the electrode and the human body is used as the basis for channel selection, that is, when the coupling capacitance is small, it is considered that the electrode is in good contact with the human body. Since the analog-to-digital converter ADS1299 has an input channel selector, it is possible to select an input channel with better electrode contact according to the size of the coupling capacitance, further ensuring the quality of the collected ECG signal.

[0053] In an embodiment of the present application, the microcontroller of the ECG acquisition unit uses a low-power MCU, is connected to the analog-to-digital converter for communication, and drives the wireless transceiver module to send the ECG digital signal to the signal processing module. In this embodiment, the microcontroller is also used to select the input channel of the analog-to-digital converter, set the sampling rate of the analog-to-digital converter, and control the analog-to-digital converter to sample and receive the sampled ECG data signal. In a preferred embodiment, the wireless transceiver module can be a WiFi module for near-field communication, such as 2.4GHz WiFi, 5GHz WiFi, or infrared mode, etc. The above is limited to the distance description and cannot be used as a limitation on the protection scope of this application. Other connection methods based on the above concept are all within the protection scope of this application and will not be repeated here.

[0054] The EEG signal acquisition unit includes: a stick-on electrode for acquiring EEG signals; a second signal preprocessing circuit for amplifying and denoising the EEG signals; a second analog-to-digital converter for converting the processed EEG signals into EEG digital signals; and a second microcontroller for controlling a second wireless transmission module to output the EEG digital signals.

[0055] According to research, the activity of the frontal lobe of the brain is closely related to the emotional state of a person. Therefore, this system selects the Fp1, Fp2 and Fpz electrode positions on the forehead specified by the 10-20 international standard lead system as the source of EEG signal acquisition, and selects the mastoid process behind the ears on both sides as the source of reference signal. In the embodiment of the present application, the EEG signal acquisition unit is integrated on a hairband, such as Figure 3As shown, the electrode uses a medical-grade wet electrode to meet the needs of universal EEG acquisition. The EEG signal of the user whose emotions are to be adjusted is obtained through the wet electrode. Further, the EEG signal is amplified and denoised by a signal preprocessing circuit. In an embodiment of the present application, the signal preprocessing circuit is composed of a high-pass filter, an amplifier circuit and a low-pass filter, wherein the high-pass filter is a first-order RC high-pass filter circuit with a cut-off frequency of 0.5Hz, which is used to remove the DC component in the EEG signal; and then the weak physiological signal after the DC component is removed is amplified in phase with a gain of 50 by the amplifier circuit; and then the noise such as 50Hz power frequency interference and high-frequency electromyographic interference is removed by a low-pass filter. In a preferred embodiment, the low-pass filter is a Bessel active low-pass filter with a cut-off frequency of 45Hz. The linear phase characteristics of the Bessel filter can reduce the degree of distortion of the signal during the filtering process. After the collected EEG signal is denoised by the signal preprocessing circuit, the EEG signal after denoising is converted into an EEG digital signal by an analog-to-digital converter for subsequent analysis and processing. In a preferred implementation, since the physiological electrical signals of the human body are weak signals with very small amplitudes, the typical amplitude of the ECG signal is 1mV, and the amplitude of the EEG signal is even smaller, only at the uV level. In order to ensure the accuracy of the collected signals, a high-precision, low-noise 24-bit analog-to-digital converter ADS1299 is used. At the same time, ADS1299 has 8 independent channels and input selectors to meet the needs of multi-channel signal acquisition.

[0056] In an embodiment of the present application, the microcontroller of the EEG acquisition unit uses a low-power MCU, is connected to the analog-to-digital converter for communication, and drives the wireless transceiver module to send the EEG digital signal to the signal processing module. In this embodiment, the microcontroller is also used to select the input channel of the analog-to-digital converter, set the sampling rate of the analog-to-digital converter, and control the analog-to-digital converter to sample and receive the sampled EEG data signal. In a preferred embodiment, the wireless transceiver module can be a WiFi module for near-field communication, such as 2.4GHz WiFi, 5GHz WiFi, or infrared mode, etc. The above is limited to the distance description and cannot be used as a limitation on the scope of protection of this application. Other connection methods based on the above conception are all within the scope of protection of this application and will not be repeated here.

[0057] In one embodiment of the present application, the signal analysis module first performs denoising on the collected EEG signals and ECG signals through a denoising unit. Since EEG signals contain a large amount of noise when they are collected through sensors, although the system has completed the removal of basic noise through hardware circuits such as EEG acquisition units during the collection process, the biggest interference to EEG signals is the electrooculogram artifact. In order to meet the needs of portable and low-power consumption, the system uses a threshold method to remove electrooculogram artifacts when further denoising the EEG signals. Specifically, the peak method is first used to detect the maximum value, and then combined with the Gaussian attenuation function, the electrooculogram area is accurately located, and the data in this area is discarded to obtain pure EEG data EEG. Among them, the Gaussian function is as follows:

[0058] y=a*exp(-(xb) 2 / 2c 2 ) (1)

[0059] EEG OA =EEG OAband *y (2)

[0060] EEG of the electrooculogram area using the peak method OAband After the Gaussian attenuation function y, we can get the accurate EEG area containing eye electroencephalogram (EEG). OA .

[0061] EEG pure =EEC O -EEC OA (3)

[0062] Pure EEC data pure It is equivalent to the original EEG signal O Subtract the noisy EEG OA .

[0063] After the EEG signal is denoised, the feature extraction unit is used to further extract EEG feature information from the EEG signal. According to the valence hypothesis, the left hemisphere of the human brain processes positive emotions while the right hemisphere processes negative emotions. The researchers used EEG to compare positive and negative emotions and found that positive and negative emotions have a lateralized effect in the frontal area of ​​the brain, and positive emotions have higher activation in the left hemisphere. In particular, the asymmetry of the alpha band (8-13Hz) indicates personality traits. Individuals with left hemisphere activation usually use more positive methods, while those with increased right hemisphere activation tend to use negative response methods. Studies have shown that in the application of depression treatment, the left hemisphere alpha advantage is shifted to the right by reducing the left hemisphere alpha activity or increasing the right hemisphere alpha activity, the asymmetry index is shifted to the right to rebalance the activation level to support the left hemisphere, and the theta / beta ratio of the left prefrontal cortex is reduced as indicators of emotion regulation. In this system: Since beta waves are related to alert or excited mental states, and alpha waves dominate in a relaxed state, beta / alpha is a reasonable treatment for a person's arousal state and has been widely used by researchers. Therefore, the emotional state characteristics used in this system are:

[0064] Feature1=(β FP1 +β FPx +β FP2 ) / (α FP1 +α FPx +α FP2 ) (4)

[0065] A large number of EEG studies have shown that the left frontal lobe area is associated with more positive emotions and memories, while the right hemisphere is more involved in negative emotions. Therefore, the emotion regulation indicator used in this system is the frontal lobe alpha lateralization:

[0066] Feature2=α FD1 -α FD2 (5)

[0067] In the embodiment of the present application, when collecting ECG signals, the mixed noise has been processed and removed by the signal preprocessing circuit in the ECG acquisition unit. In order to make it consistent with the EEG signal in the later processing, the sampling rate of the ECG signal is controlled to 250 Hz.

[0068] In order to explore the brain-heart interaction of participants during the music adjustment process, the utility model analyzes the cerebral cortex processing of the heartbeat signals of participants during the music intervention process through heartbeat evoked potential (HEP). Heartbeat evoked potential (HEP) uses the R wave of the electrocardiogram as the signal average timing source. The utility model adopts the classic Pan-tompkins algorithm to identify and mark the R peak of the preprocessed electrocardiogram signal. The core idea of ​​the Pan-tompkins algorithm is to identify continuous electrocardiogram fluctuations by calculating the key data such as the tilt degree, amplitude height and specific width of each (QRS) wave group that reflects the change in the left and right ventricular depolarization potential and time. The algorithm uses a digital transition band noise filter to improve safety and adaptively adjust the threshold QRS, and the parameters of the waveform are significantly changed, which further improves the agility. At the same time, the algorithm has high real-time performance and occupies less computing resources, meeting the system's requirements for simultaneous collection and analysis. The specific implementation process of the algorithm is as follows Figure 5 As shown. Then, the position of the marked R peak is used as the stimulation point, and the collected EEG data is divided into a time period of 200 to 600ms relative to the R peak. The frontal electrodes collected by this system represent the cortical representation of visceral afferent signals, so the average heart evoked potential (HEP) waveform of the frontal pole midline (Fpz) is mainly analyzed. In order to meet the requirements of low time complexity and computational complexity of portable devices, this system uses the superposition average method to extract the heart evoked potential (HEP).

[0069] After the EEG features and ECG features are extracted, the extracted feature information is classified and processed by the classification unit. In the embodiment of the present application, when a person listens to music, the feature indicators extracted from the EEG signals and ECG signals will change accordingly. This change in physiological feature indicators can be considered as a sign of a change in emotional state. The machine learning method is used to classify the changes in human physiological signal features. By learning and building a model for a large amount of physiological signal feature data, the accuracy of emotional state judgment can be higher and the results can be more convincing.

[0070] This system combines the features of the above physiological signals and the results of the emotional state evaluation table through the learning unit, and uses the k-means clustering algorithm to establish an emotional state model, and uses the k-nearest neighbor (knn) algorithm to identify emotional states. Specifically, this system first collects physiological signals from 100 users, extracts the features of physiological signals and the evaluation results of the five-state emotional questionnaire as the data source of the training set, and performs feature fusion to achieve complementary advantages between multiple features:

[0071] Feature all =a×Feature scale +b×(Feature1+Feature2+FeatureHEP ) (6)

[0072] Among them, a and b are the optimal weight parameters of emotion recognition trained offline. The fused features are clustered into five categories: thinking, fear, joy, sadness, and anger using the k-means clustering algorithm, and the trained emotional state model is deployed to the system on chip. When it is officially used, the user's fused features are obtained through formula (6), and the fused features are processed using the knn algorithm to complete the emotional state prediction and output the current emotional state evaluation result.

[0073] In the implementation of the present application, the interactive module includes: an output unit for outputting emotion assessment information; an input unit for receiving emotion assessment options. Specifically, by setting a high-resolution, large-size display screen as an output unit, for example, a large-size LCD screen is used to display an emotion state scale for emotion assessment, and the user selects the output emotion state scale through an input unit, such as a touch screen, a mouse, a keyboard, and a voice control input method, thereby completing the assessment of the user's current emotion. In the embodiment of the present application, the emotional state scale assessment uses the "Chinese Medicine Five-State Emotion Questionnaire", which has good reliability and validity. Preliminary actual test results show that the questionnaire has the characteristics of simple and easy operation, strong practicality, and easy-to-grasp judgment criteria. It provides an emotion measurement tool with Chinese medicine characteristics and clinical practicality for the diagnosis and identification of emotional disorders in modern clinical practice, and can effectively improve the application ability of Chinese medicine psychology in clinical practice.

[0074] In the implementation mode of the present application, the system also selects the corresponding music from the database storing the music through the adjustment module according to the result of the emotional state scale assessment and the processing result of the signal processing module, and then plays it through the playback unit to adjust the current mood of the user. In the embodiment of the present application, the music database contains music of the five modes of Jiao, Zhi, Gong, Shang, and Yu in the five tones of traditional Chinese medicine. The microcontroller of the playback unit is connected to the audio decoding chip through the I2S interface, selects the appropriate music from the music database, and plays it after encoding and decoding by the low-power, high-quality audio codec chip.

[0075] In the implementation mode of the present application, the system also displays the processing results corresponding to the EEG signals and ECG signals processed in real time by the signal processing module and the emotional state assessment results on the above-mentioned display screen through the feedback module for timely viewing by medical staff and the user himself. In another implementation mode, the feedback module also sends the processing results and emotional state assessment results to a remote device through a set communication unit, such as 2.4GHz WiFi, 5.0GHz WiFi, 4G / 5G, etc., for viewing, for example, to the user's family to understand the user's status in time, or to a remote doctor to assist in providing an adjustment plan.

[0076] refer to Figure 6 As shown, the music adjustment system for depression of the utility model, before adjusting the user's emotions, first starts the evaluation, and evaluates the user's emotions through the emotional state scale. For example, after the user wears the device, first completes the scoring of the "Chinese Medicine Five-State Emotion Questionnaire", and saves and displays the score results of the emotional scale. Then, the collection of physiological electrical signals such as EEG signals and ECG signals for 5 minutes is started. After the collection is completed, the collected physiological electrical signals are pre-processed by the signal processing module such as denoising, amplification, and frequency modulation, and then the EEG features and ECG features are extracted. The system combines the emotional state evaluation results and the above-mentioned physiological signal characteristics to give the user's emotional state evaluation results (thinking, fear, joy, sadness, anger), and then selects the music in the corresponding music library according to this result. The music selection is based on the results of syndrome differentiation and the principle of emotion overcoming emotion to select music that counteracts the patient's emotions, as shown in Table 1.

[0077]

[0078] Table 1

[0079] After the music starts playing, physiological signals are continuously collected, the user's emotional state under the regulation of music is analyzed in real time, and the real-time changes of the frontal lobe lateralization feature Feature2 are displayed. According to existing research results, if the value of the frontal lobe lateralization feature Feature2 begins to decrease, the system will continue to select music with the same musical features as the music, enhance the user's left frontal lobe activity and train the user's emotional regulation ability.

[0080] The user's scale measurement results, physiological signal data, and emotional state assessment results will be saved in the memory card for the user to view or further process the data later.

[0081] The above are only specific embodiments of the present application. The above scenarios are only examples and do not constitute a limitation on the application scenarios of the technical solutions provided in the embodiments of the present application. The technical solutions of the present application can also be applied to other scenarios. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0082] In the present application, the same or similar terminology concepts, technical solutions and / or application scenario descriptions are generally described in detail only the first time they appear. When they appear again later, they are generally not repeated for the sake of brevity. When understanding the technical solutions and other contents of the present application, for the same or similar terminology concepts, technical solutions and / or application scenario descriptions that are not described in detail later, reference can be made to the previous related detailed descriptions.

Claims

1. A music regulation system for depression, characterized in that: The system comprises: A signal acquisition module for acquiring EEG signals and ECG signals; A signal processing module for processing the EEG signals and ECG signals collected by the signal collection module; An interactive module for obtaining emotion assessment information; An adjustment module for playing the processing result of the signal processing module and the music corresponding to the emotional status assessment information; A feedback module for outputting the processing results and the emotional changes during the adjustment of the playing music; Wherein, the signal acquisition module comprises an ECG acquisition unit for acquiring ECG signals, and the ECG acquisition unit comprises: a non-contact electrode for acquiring ECG signals by forming a coupling capacitor with the skin; An instrumentation amplifier for suppressing common-mode signals of the human body by increasing the input impedance of the ECG signal input channel; A first signal preprocessing circuit for amplifying and denoising the electrocardiogram signal; A first analog-to-digital converter for converting the processed ECG signal into an ECG digital signal; A capacitance detection circuit for detecting the magnitude of the coupling capacitance formed between the non-contact electrode and the skin; A first microcontroller for controlling the first wireless transmission module to output the ECG digital signal; Wherein, the signal acquisition module includes an EEG acquisition unit for acquiring EEG signals, and the EEG acquisition unit includes: a stick-type electrode for acquiring EEG signals; A second signal preprocessing circuit for amplifying and denoising the EEG signal; A second analog-to-digital converter for converting the processed EEG signal into an EEG digital signal; A second microcontroller for controlling the second wireless transmission module to output the EEG digital signal; Wherein, the signal processing module includes: A denoising unit for denoising the EEG signal and the ECG signal; A feature extraction unit for extracting emotional feature index information from the denoised EEG signal and ECG signal; A classification unit for classifying the emotion characteristic indicator information; The interaction module includes: An output unit for outputting emotion assessment information; An input unit used for users to perform selection operations.

2. The system according to claim 1, characterized in that The first signal preprocessing circuit and / or the second signal preprocessing circuit comprises: A high-pass filter for removing a DC component from the electrocardiogram signal; An amplifier circuit for gaining an electrocardiographic signal with a DC component removed; A low-pass filter used to remove 50Hz power frequency interference and high-frequency myoelectric interference noise.

3. The system according to claim 1, characterized in that The denoising unit comprises: An EEG signal denoising component for removing eye artifacts.

4. The system according to any one of claims 1 to 3, characterized in that The adjustment module includes: a music database for storing music; A playing unit is used for reading music from the database for playing.

5. The system according to any one of claims 1 to 3, characterized in that The feedback module includes: an interface unit for outputting the processing result and the emotion change; A display unit is used to receive the processing result and the emotion change from the interface unit and display them.

6. The system according to claim 5, characterized in that The feedback module also includes: A communication unit for sending the processing result and the emotion change to a remote device via a mobile network; The communication unit includes: a WiFi component and a 4G / 5G component.

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