Individualized music healing system based on brain wave regulation and control

Abstract

The invention discloses a personalized music healing system based on brain wave regulation and control, and relates to the technical field of signal processing. The system comprises a brain wave signal acquisition and decomposition unit, an emotional characteristic processing unit and a music healing signal generation unit, the brain wave signal acquisition and decomposition unit is used for acquiring brain wave signals, performing multi-scale decomposition on the brain wave signals to obtain a signal decomposition result, and calculating standardized brain wave energy density; the emotional feature processing unit is used for constructing a five-dimensional emotional feature vector according to the signal decomposition result and the standardized brain wave energy density; performing emotion dynamic feature extraction to obtain three-dimensional emotion dynamic features; and the music healing signal generation unit is used for performing music basic parameter mapping and music time sequence structure generation based on the five-dimensional emotion feature vector and the three-dimensional emotion dynamic feature to obtain a finally generated healing music signal. According to the invention, a more efficient, stable and effective music healing effect is realized.

Description

Technical Field

[0001] The present disclosure relates to, but is not limited to, the field of signal processing technology, and in particular to a personalized music therapy system based on brainwave regulation. Background Art

[0002] The present invention "Personalized music therapy system based on brainwave regulation" aims to use the real-time collection and analysis of brainwave signals to provide users with highly personalized music therapy services to meet the needs of relaxation, regulation and relief under different emotional states. At present, music therapy, as a non-drug intervention method, has been widely used to reduce stress, improve mood, enhance concentration and other aspects. Existing music therapy technology mainly focuses on the fields of standard music playback, frequency resonance therapy and emotional self-regulation music. However, there are still significant deficiencies in the existing technology, mainly concentrated in the aspects of real-time, personalization and dynamic emotional adaptation.

[0003] First, in terms of standard music playback, there are a large number of music therapy products designed for specific emotions or psychological states on the market. For example, some applications provide music playlists with different functions such as relaxation, meditation, and tranquility. However, most of these systems rely on pre-recorded music content and cannot generate or adjust music content in real time according to the user's current emotional state. When choosing music, users still need to choose suitable music based on personal preferences, past experience, or manual recommendations. However, emotional states change dynamically, and the effect of music therapy may be affected as the user's environment changes and psychological reactions differ. Existing standard music therapy methods often lack specificity when facing subtle changes in user emotions, making it difficult to achieve the best therapeutic effect. Furthermore, fixed music content has poor adaptability to changes in user emotions, and lacks an emotional feedback mechanism, making it impossible to adjust the music content in time to maintain the continuity of the therapeutic effect. Secondly, frequency resonance therapy, as another popular music therapy method, introduces sounds of specific frequencies into the user's auditory or body resonance system to achieve the effect of relieving anxiety and relaxing the body and mind. This type of therapy is usually based on specific frequencies, such as alpha waves and theta waves, which are associated with the natural fluctuations of the brain and are believed to help people enter a state of relaxation or deep meditation through a resonance effect. Although this type of therapy has a certain relaxation effect, it is often targeted at specific frequencies and does not fully consider the user's real-time emotional state and emotional fluctuations. In addition, frequency resonance therapy mainly relies on the continuous input of specific frequencies in operation, and it is difficult to adjust according to the user's individual emotional needs. For users with large emotional fluctuations, a single frequency may be effective in a certain emotional state, but may appear single or ineffective in other emotional states. Summary of the invention

[0004] The present disclosure provides a personalized music therapy system based on brainwave regulation, which achieves a more efficient, stable and effective music therapy effect through real-time, personalized, intelligent and refined design. The system can dynamically adjust the frequency, rhythm and harmonic structure of the music with brainwave signals as input to match the user's emotional state at different times, thereby providing users with continuous and deep emotional guidance and soothing effects.

[0005] In order to solve the above problems, the technical solution of the present invention is achieved as follows:

[0006] A personalized music therapy system based on brainwave regulation, the system comprising: a brainwave signal acquisition and decomposition unit, an emotion feature processing unit and a music therapy signal generation unit; the brainwave signal acquisition and decomposition unit is used to acquire brainwave signals, and by combining the brainwave signals with wavelet basis functions of different brainwave signal frequency bands, the brainwave signals are multi-scale decomposed to obtain signal decomposition results, and based on the signal decomposition results, the standardized brainwave energy density is calculated; the emotion feature processing unit is used to construct a five-dimensional emotion feature vector according to the signal decomposition results and the standardized brainwave energy density; based on the five-dimensional emotion feature vector, emotion dynamic feature extraction is performed to obtain three-dimensional emotion dynamic features; the music therapy signal generation unit is used to perform music basic parameter mapping based on the five-dimensional emotion feature vector and the three-dimensional emotion dynamic features to generate a music basic parameter vector; based on the three-dimensional emotion dynamic features, harmonic structure generation is performed to obtain harmonic frequency response; based on the harmonic frequency response, music time series structure generation is performed to obtain a finally generated healing music signal.

[0007] Furthermore, the brain wave signal frequency bands include: delta band, theta band, alpha band, beta band and gamma band.

[0008] Furthermore, the brain wave signal acquisition and decomposition unit performs multi-scale decomposition on the brain wave signal by the following formula to obtain the signal decomposition result X(f, t):

[0009]

[0010] Where n is an integer subscript index; t′ is the time integral variable; t is time; a n is the time scale factor, corresponding to the period of the brain wave signal frequency band; a1 = 0.5 seconds, corresponding to the δ band; a2 = 0.167 seconds, corresponding to the θ band; a3 = 0.1 seconds, corresponding to the α band; a4 = 0.05 seconds, corresponding to the β band; a5 = 0.025 seconds, corresponding to the γ band; f nis the center frequency of each brain wave signal frequency band; f1 = 2 Hz, corresponding to the center frequency of the δ band; f2 = 6 Hz, corresponding to the center frequency of the θ band; f3 = 10 Hz, corresponding to the center frequency of the α band; f4 = 2 Hz, corresponding to the center frequency of the β band; f5 = 40 Hz, corresponding to the center frequency of the γ band; σ n is the energy attenuation width of each brain wave signal frequency band; σ1 = 1Hz, corresponding to the energy attenuation width of the δ band; σ2 = 2Hz, corresponding to the energy attenuation width of the θ band; σ3 = 2.5Hz, corresponding to the energy attenuation width of the α band; σ4 = 8.5Hz, corresponding to the energy attenuation width of the β band; σ5 = 20Hz, corresponding to the energy attenuation width of the γ band; S(t) is the brain wave signal, S(t′) is the brain wave signal in integral form; f is the frequency; ψ n (·) is the wavelet basis function of the nth frequency band.

[0011] Furthermore, the brain wave signal acquisition and decomposition unit calculates the standardized brain wave energy density D(f, t) based on the signal decomposition result by the following formula:

[0012]

[0013] Among them, Δf is the frequency integration window width, ranging from 1 Hz to 3 Hz; f′ is the frequency integration variable; |·| is the amplitude operator.

[0014] Furthermore, the emotion feature processing unit constructs a five-dimensional emotion feature vector E(t) based on the signal decomposition result and the standardized brain wave energy density, which is expressed as:

[0015]

[0016] Among them, E1(t) is the relaxation-tension dimension element; E2(t) is the focus-distraction dimension element; E3(t) is the stability-fluctuation dimension element; E4(t) is the coordination-chaos dimension element; E5(t) is the harmony-imbalance dimension element; Phase(·) is the phase extraction function; Sync(·) is the synchronization calculation function.

[0017] Furthermore, the emotion feature processing unit extracts the emotion dynamic feature by the following formula to obtain the emotion dynamic feature Φ(t):

[0018]

[0019] Among them, Φ1(t) is the accumulated emotion intensity; Φ2(t) is the emotion change rate; Φ3(t) is the emotion complexity change rate; ||·||1 is the L1 norm operation; ||·||2 is the L2 norm operation; E(t′) is the integral form of the five-dimensional emotion feature vector E(t).

[0020] Furthermore, the music therapy signal generating unit performs music basic parameter mapping based on the five-dimensional emotion feature vector and the three-dimensional emotion dynamic feature through the following formula to generate the music basic parameter vector M(t):

[0021]

[0022] Among them, f b (t) is the basic frequency, based on the standard pitch of 440Hz; A(t) is the amplitude envelope; T(t) is the speed, the unit is BPM, and the value range is 60-180.

[0023] Furthermore, the music therapy signal generating unit generates a harmonic structure according to the three-dimensional emotional dynamic characteristics through the following formula to obtain a harmonic frequency response H(f, t):

[0024]

[0025] B(t)=B 0· (1+Φ2(t));

[0026] σ h (t) = 2 + 3Φ3 (t);

[0027] Among them, c j (t) is the j-th order harmonic coefficient; B(t) is the harmonic bandwidth function; σ h (t) is the harmonic broadening coefficient; B0 is the harmonic basic bandwidth, which is the set value; j is the integer subscript index.

[0028] Furthermore, the music therapy signal generating unit generates a music time sequence structure according to the harmonic frequency response through the following formula to obtain the final generated healing music signal:

[0029]

[0030] Among them, S(t) is the final generated healing music signal; W(f, t) is the time-varying modulation window function; g i (·) is the Sigmoid modulation function; i is an integer subscript index; k i is the modulation slope coefficient, where k1=2.0, k2=1.5, k3=1.0; x0 is the modulation midpoint, with a value of 0.5.

[0031] A personalized music therapy system based on brain wave regulation of the present invention has the following beneficial effects: the present invention has extremely high real-time performance. Through the collection and multi-dimensional analysis of brain wave signals, the system can quickly and accurately obtain the user's immediate emotional state information. Different from the traditional music therapy technology that requires the user to actively select music or rely on pre-set music content, the present system can complete the identification and analysis of emotional state in a very short time by real-time monitoring of brain wave signals, such as δ waves, θ waves, α waves, etc. Combined with the algorithm of multi-dimensional emotional feature vector and dynamic emotional feature extraction, the system can accurately capture emotional fluctuations, so that the generated music content is adjusted in real time with the user's emotional changes, and the "emotion-music" closed-loop feedback is realized, so as to give adaptive music feedback at the first time when the user's emotions change. Through such a real-time design, the user can immediately get the emotional guidance and relief of music when the mood fluctuates or the psychological state changes, which helps to reduce anxiety, relax the mood, relieve stress and other psychological healing effects. Secondly, the present invention has the advantage of high personalization. Traditional music therapy is often based on a fixed music playlist or simple frequency adjustment, which is difficult to fully consider the unique emotional response and individual needs of each user. The present invention generates a unique emotional feature map for each user through multi-dimensional feature extraction and analysis based on brain wave signals. Whenever a user enters a different emotional state, the system will generate exclusive music content according to the user's current emotion, including real-time adjustment of key parameters such as fundamental frequency, rhythm, amplitude envelope, so that the music content is highly matched with the user's emotions. At the same time, the system further refines the sound quality level of the music by dynamically adjusting the bandwidth, broadening coefficient and center frequency of the harmonic frequency response in the harmonic structure, so that the music not only meets the user's emotional needs in frequency and rhythm, but also provides more detailed emotional support in terms of sound texture and atmosphere. Such personalized settings can help users enter the healing state more deeply, thereby improving the effect and durability of music therapy. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A schematic diagram of the system structure of a personalized music therapy system based on brainwave regulation provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present disclosure clearer and more understandable, the present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure and are not used to limit the present disclosure.

[0034] Example 1, reference Figure 1:A personalized music therapy system based on brainwave regulation, the system comprising: a brainwave signal acquisition and decomposition unit, an emotion feature processing unit and a music therapy signal generation unit; the brainwave signal acquisition and decomposition unit is used to acquire brainwave signals, and by combining the brainwave signals with wavelet basis functions of different brainwave signal frequency bands, the brainwave signals are multi-scale decomposed to obtain signal decomposition results, and based on the signal decomposition results, the standardized brainwave energy density is calculated; the emotion feature processing unit is used to construct a five-dimensional emotion feature vector according to the signal decomposition results and the standardized brainwave energy density; based on the five-dimensional emotion feature vector, emotion dynamic feature extraction is performed to obtain three-dimensional emotion dynamic features; the music therapy signal generation unit is used to perform music basic parameter mapping based on the five-dimensional emotion feature vector and the three-dimensional emotion dynamic features to generate a music basic parameter vector; based on the three-dimensional emotion dynamic features, harmonic structure generation is performed to obtain a harmonic frequency response; based on the harmonic frequency response, music time series structure generation is performed to obtain a final generated healing music signal.

[0035] Specifically, the personalized music therapy system based on brainwave regulation collects the user's brainwave signals in real time, accurately converts the emotional state into a personalized music therapy plan, and realizes the detailed regulation of the user's emotions. The system includes a brainwave signal acquisition and decomposition unit, an emotional feature processing unit, and a music therapy signal generation unit. Each unit is responsible for the core steps from brainwave signal acquisition to emotional feature extraction and then to music therapy generation, and finally forms a real-time adaptive music therapy system. The system is first started by the brainwave signal acquisition and decomposition unit, which collects the user's original brainwave signal through the brainwave sensor device. The brainwave signal is a complex time series data, which contains many frequency band information, such as δ, θ, α, β, γ, etc. These frequency bands correspond to different psychological and physiological states of the user. Therefore, in order to accurately extract emotional information, the system performs multi-band decomposition of the brainwave signal through a specific wavelet basis function, separates the original signal into components of different frequency bands, thereby retaining its time and frequency characteristics. In this process, the application of wavelet basis function is the core technology. Traditional signal processing relies on Fourier transform, which loses time information when extracting frequency domain features. However, wavelet transform has good time-frequency localization characteristics and can flexibly adjust the time scale for different frequency bands, thereby more accurately capturing signal features in frequency bands such as δ, θ, and α. Specifically, the system selects wavelet basis functions that match the characteristics of each frequency band, and controls the time scale factor of the wavelet basis function to enable it to have maximum responsiveness in a specific frequency band, thereby ensuring the accuracy and stability of signal decomposition.

[0036] In addition, the system further reduces the interference of non-target frequency band signals through spectrum energy smoothing. The frequency characteristics are attenuated by using Gaussian attenuation function, so that the energy of each frequency band is mainly concentrated near the center frequency, while the signal energy away from the center frequency gradually decreases. After the above processing, the system obtains the signal decomposition result with frequency concentration characteristics, which provides a solid foundation for the subsequent emotional feature extraction. Next, the system calculates the energy density of the decomposition result and normalizes it to obtain the standardized brain wave energy density. The purpose of standardization is to ensure that the energy density of each frequency band can participate in the emotional feature extraction process fairly. Through normalization, the system can effectively eliminate the interference caused by uneven frequency band energy in the signal, thereby improving the accuracy and robustness of emotion recognition. The emotional feature processing unit further constructs a five-dimensional emotional feature vector based on the standardized brain wave energy density. The five dimensions of this emotional feature vector correspond to the main emotional states of the user, including relaxation-tension, concentration-dispersion, stability-fluctuation, coordination-chaos and harmony-imbalance. These dimensions are constructed through comprehensive analysis of frequency and time characteristics, so as to extract key emotional features from complex brain wave data.

[0037] For example, in a relaxed state, users usually show enhanced delta and theta waves, while in a tense state, they are accompanied by higher beta and gamma waves. Through the combination of these frequency band features, the system can derive accurate emotional features. The construction of emotional feature vectors is not limited to the static features of the signal. The system also takes into account the dynamic changes of the user's emotions. Therefore, on the basis of the emotional feature vector, the system further extracts the dynamic features of emotions. The emotional dynamic feature vector contains three main components, namely, the accumulated amount of emotional intensity, the rate of emotional change, and the rate of change of emotional complexity. These three components respectively characterize the accumulated intensity, rate of change, and complexity change of the user's emotions in the current time period. This design fully considers the dynamic properties of emotions, enabling the system to respond to changes in user emotions in real time. For example, when the emotional intensity is detected to be constantly accumulating, the system can gradually alleviate the user's emotions by adjusting the volume, rhythm and other parameters of the music, thereby achieving a deep healing effect. The emotional feature processing unit not only statically models the user's emotional state, but also further captures the dynamic change trend of the user's emotions, so that the system's music generation can adapt to the subtle fluctuations of emotions in real time. After acquiring the emotional characteristics and dynamic characteristics, the music therapy signal generation unit maps them to the basic parameters of the music. These basic parameters include the frequency, amplitude and speed of the music, which are the core of achieving the adaptation of music and emotions. In order to make the music more in line with emotional needs, the system maps the emotional feature vector and the emotional dynamic feature vector to frequency, rhythm and volume respectively. For example, when the user is nervous and unstable, the system will automatically reduce the speed of the music and adjust the frequency to make the music softer and achieve a relaxing effect.

[0038] In this parameter mapping process, the system converts the information on the emotional dimension into specific parameters of the music signal through a certain mapping function to ensure the synchronous matching between the emotional state and the music attributes. After generating the basic music parameters, the system further generates the harmonic structure. The harmonic structure generation is based on different harmonic frequency responses. Through multi-level frequency superposition, the music signal has rich harmony in frequency and time. This multi-harmonic frequency response not only enhances the sense of hierarchy of the music, but also enables the music to resonate deeply and enhance the user's healing experience. The generation of the harmonic structure superimposes multiple harmonic components, so that the music signal has a higher frequency resolution, and can automatically adjust the harmonic distribution under different emotional states, providing users with a continuous emotional regulation effect. Finally, based on the generation of the harmonic structure, the system uses the timing structure generation module to form the final music healing signal. The timing structure generation ensures the continuity of the music signal in time and frequency through the time-varying modulation function, so that the music can smoothly transition and remain adapted to the user's current emotional state. Through a series of frequency, time and amplitude adjustments, the generation process of music signals not only follows the guidance of emotional characteristics, but also has adaptability, so that music can achieve smooth parameter regulation in the dynamic changes of emotions, avoiding the discomfort caused to users by sudden rhythm or volume changes. In summary, the personalized music therapy system based on brainwave regulation is based on brainwave signals, and through multi-step decomposition, feature extraction and dynamic parameter generation, it can achieve accurate recognition and real-time response to user emotions. Unlike traditional music therapy systems, this system can not only achieve higher resolution in emotion recognition, but also closely associate emotional changes with music attributes through multi-level parameter mapping, and achieve personalized and real-time music therapy effects. This innovative brainwave regulation method breaks through the limitation of traditional healing systems that only rely on static music to achieve soothing effects, and can truly dynamically adjust music according to the user's real-time emotional state, thereby providing a deeper emotional healing experience.

[0039] Embodiment 2: The brain wave signal frequency bands include: δ band, θ band, α band, β band and γ band.

[0040] Specifically, the delta band usually refers to the frequency range of 0.5 to 4 Hz, and brain wave activity in this frequency band is usually associated with deep sleep states and restorative processes. High-intensity delta wave activity is common in unconscious states, such as deep sleep or coma, representing rest and recovery of the body and brain. Therefore, when the system detects higher delta waves, it may indicate that the user is in a state of deep relaxation or unconsciousness, and it is suitable to generate gentle, low-frequency music to maintain relaxation. The theta band is usually between 4 and 8 Hz, and brain wave activity in this frequency band is associated with meditation, light sleep, and relaxation. High-intensity theta wave activity indicates a relaxed but alert state, which usually occurs in dreaming or deep meditation. This state indicates that the user may be in a state of emotional relaxation but not fully awake, and soft, progressive music is suitable to maintain this calm state. The frequency range of the alpha band is between 8 and 13 Hz, and is associated with a relaxed, calm and stable emotional state. Alpha waves usually appear when resting with eyes closed, meditating or relaxing, indicating that the user is in a state of awake relaxation. When higher alpha waves are detected, it indicates that the user may be in a state of balance between relaxation and concentration. At this time, the generated music can be adjusted to a moderate rhythm and volume to help the user maintain a comfortable state. The beta band generally refers to the frequency range between 13 and 30 Hz, which is associated with daily wakefulness, concentration, and high-intensity thinking activities. High-intensity beta waves indicate that the user is in a state of alertness, concentration, or anxiety and tension. The enhancement of the beta band is usually accompanied by improved concentration, but it may also cause increased anxiety or stress. When high beta waves are detected, the system can help the user relax and reduce tension and anxiety by generating soothing music. The gamma band is a frequency above 30 Hz, which is usually associated with high-level cognitive processing, complex thinking and consciousness activities. Gamma waves represent the active state of the brain when processing complex information, which is usually seen in high-intensity learning, information integration, or rapid decision-making. The increase in gamma wave activity may indicate that the user is in a state of high concentration and cognitive activity. The system can use relatively high-frequency and slightly faster music to maintain or enhance the user's cognitive ability.

[0041] Embodiment 3: The brain wave signal acquisition and decomposition unit performs multi-scale decomposition on the brain wave signal by the following formula to obtain the signal decomposition result X(f, t):

[0042]

[0043] Where n is an integer subscript index; t′ is the time integral variable; t is time; a n is the time scale factor, corresponding to the period of the brain wave signal frequency band; a1 = 0.5 seconds, corresponding to the δ band; a2 = 0.167 seconds, corresponding to the θ band; a3 = 0.1 seconds, corresponding to the α band; a4 = 0.05 seconds, corresponding to the β band; a5 = 0.025 seconds, corresponding to the γ band; f nis the center frequency of each brain wave signal frequency band; f1 = 2 Hz, corresponding to the center frequency of the δ band; f2 = 6 Hz, corresponding to the center frequency of the θ band; f3 = 10 Hz, corresponding to the center frequency of the α band; f4 = 20 Hz, corresponding to the center frequency of the β band; f5 = 40 Hz, corresponding to the center frequency of the γ band; σ n is the energy attenuation width of each brain wave signal frequency band; σ1 = 1Hz, corresponding to the energy attenuation width of the δ band; σ2 = 2Hz, corresponding to the energy attenuation width of the θ band; σ3 = 2.5Hz, corresponding to the energy attenuation width of the α band; σ4 = 8.5Hz, corresponding to the energy attenuation width of the β band; σ5 = 20Hz, corresponding to the energy attenuation width of the γ band; S(t) is the brain wave signal, S(t′) is the brain wave signal in integral form; f is the frequency; ψ n (·) is the wavelet basis function of the nth frequency band.

[0044] Specifically, in the formula, the time integral variable t′ represents the sampling time point of the brain wave signal S(t′), and t, as the current output time point, reflects the real-time response of the system to the brain wave signal at a specific time. n , the system can localize the signal to different time scales and realize feature extraction of δ, θ, α and other bands. Time scale factor a n The role of is to stretch or compress the wavelet basis function to adapt it to the periodic characteristics of different frequency bands. For example, for the low-frequency δ band, a larger time scale factor (such as a1 = 0.5 seconds) makes the time window covered by the wavelet basis function longer, thereby capturing the changes in low-frequency components. On the contrary, for the high-frequency γ band, a smaller time scale factor (such as a5 = 0.025 seconds) enables the wavelet basis function to capture the rapidly changing high-frequency components in detail. This design ensures that when the system performs multi-band decomposition, it can dynamically adjust the decomposition process according to the frequency characteristics of each frequency band, thereby retaining the rich details of the brain wave signal. The center frequency f in the formula n It is used to locate the main frequency components of each brain wave frequency band, so that the system can concentrate the signal characteristics of different frequency bands in their corresponding frequency areas. The center frequency of the δ band is 2Hz, representing the user's deep relaxation state, while the center frequency of the γ band is 40Hz, indicating the user's high-intensity cognitive activity. By setting the center frequency, the system can ensure that the decomposition of each frequency band is concentrated within a specific frequency range, making it easier to capture the psychological state reflected by the frequency band. At the same time, in order to reduce the interference of other frequency bands on the target frequency band signal, the formula introduces the frequency attenuation width σ n , using the Gaussian decay function The frequency characteristics are smoothed. The Gaussian function maximizes the signal energy near the center frequency of each frequency band, and the energy gradually decays as the distance from the center frequency increases. This smoothing process not only enhances the stability of the characteristics within the frequency band, but also reduces the interference from other frequency bands, and improves the accuracy of signal decomposition. The time-frequency distribution X(f, t) generated by the present invention through this multi-scale decomposition formula is actually the characteristic distribution of the brain wave signal at time t and frequency f. By converting S(t) into X(f, t), the system can observe the energy changes of brain waves at different time and frequency components. In this way, the system can further analyze the multi-band characteristics of brain waves to form a feature vector that reflects the user's psychological state. The final generated emotional feature vector is a five-dimensional structure, based on which the system can identify and track the real-time dynamics of the user's emotions. As a result, the system not only passively collects the user's brain wave data, but also forms a detailed "emotional map" at the data level through the multi-band decomposition of the formula. The map not only provides a static snapshot of the current emotional state, but also provides a basis for the system to flexibly adjust the music parameters in the generation of music therapy signals.

[0045] Embodiment 4: The brain wave signal acquisition and decomposition unit calculates the standardized brain wave energy density D(f, t) based on the signal decomposition result by the following formula:

[0046]

[0047] Among them, Δf is the frequency integration window width, ranging from 1 Hz to 3 Hz; f′ is the frequency integration variable; |·| is the amplitude operator.

[0048] Specifically, in this formula, |X(f, t)| 2 Represents the energy intensity of the signal decomposition result at a certain frequency f and time t. By calculating this value, the energy information carried by the frequency component f of the brain wave signal at a given time can be reflected. However, since the energy of the brain wave signal is not evenly distributed in the frequency spectrum, the signal needs to be processed by a standardization method to ensure that the overall relative energy intensity is more comparable. To achieve this goal, the denominator integrates the energy within a frequency window to obtain the total energy value. Specifically, the frequency integration window width Δf is used to limit the integration range to between f-Δf and f+Δf, ensuring that the energy integration is limited to the specified frequency band. In this way, the system can achieve local normalization in each frequency band, so that the energy density D(f, t) of each frequency band is relatively balanced, so as not to affect the accuracy of emotion feature extraction due to excessively high or low energy in a specific frequency band. In addition to the above standardization, the formula also introduces the Gaussian attenuation function This term is used to smooth the spectral distribution of brain wave signals by nThe energy density is maximized near the center frequency, and the energy is gradually attenuated away from the center frequency. This smooth attenuation effect is determined by the parameter σ n Control, different frequency bands select different attenuation widths according to their characteristics. For example, the attenuation width of the low-frequency δ wave is small to ensure that the signal energy is mainly concentrated near the center frequency, while the attenuation width of the high-frequency γ wave is large to adapt to the wider energy distribution in the high-frequency signal. Through the smoothing of the Gaussian attenuation function, the system can ensure that only representative spectral components are processed in each frequency band, while reducing the interference of non-target frequency bands, thereby improving the feature extraction accuracy of brain wave signals.

[0049] Embodiment 5: The emotion feature processing unit constructs a five-dimensional emotion feature vector E(t) based on the signal decomposition result and the standardized brain wave energy density, which is expressed as:

[0050]

[0051] Among them, E1(t) is the relaxation-tension dimension element; E2(t) is the focus-distraction dimension element; E3(t) is the stability-fluctuation dimension element; E4(t) is the coordination-chaos dimension element; E5(t) is the harmony-imbalance dimension element; Phase(·) is the phase extraction function; Sync(·) is the synchronization calculation function.

[0052] Specifically, the first element E1(t) of the emotion feature vector represents the emotional dimension of relaxation-tension, and its calculation method is based on the logarithmic transformation of the energy density and signal amplitude of the δ band. δ waves are usually associated with a deep relaxation state. Therefore, the system can identify the user's relaxation level by logarithmic processing of the energy of the δ wave frequency band. Logarithmic transformation can not only amplify subtle changes in low energy, but also avoid over-amplification at high energy. This processing method can accurately capture the changes in the state of relaxation or tension, thereby providing a basis for the generation of relaxing music. The second element E2(t) of the emotion feature vector is used to describe the state of concentration-distraction. The system determines this eigenvalue by calculating the frequency gradient of the θ band. The θ wave often appears in a state of relaxation but maintaining a certain degree of concentration. By analyzing the changes in its frequency, the system can derive the user's concentration. For example, when the θ wave frequency gradient increases, it indicates that the user may gradually turn to a state of concentration, while the low frequency gradient shows a more dispersed emotional state. Accurate identification of the concentration-distraction dimension helps adjust the rhythm and melody of the music to meet the needs of the user when concentrating or distracting. The third dimension element E3(t) is used to identify stable-fluctuating emotional states. The system calculates this feature by integrating the time gradient of the alpha band signal. The alpha wave band usually appears in a state of mild concentration or relaxation, and the change in the time gradient indicates the stability of the signal in time. When the time gradient is small, the user's mood is relatively stable; when the time gradient is large, the user's mood may fluctuate or be unstable. The system can use this feature to generate music that changes steadily or gradually, so that the music can play a reconciling role when the mood fluctuates and guide the user back to a more stable state. The coordination-chaos dimension is represented by the fourth element E4(t) of the feature vector. The system calculates this dimension by analyzing the phase characteristics of the beta band signal. The beta wave band is usually associated with a state of concentration and alertness, and phase consistency indicates the degree of time synchronization of the signal. When the phase characteristics show high synchronization, the user's mood is in a coordinated state; when the phase synchronization is low, it indicates that the user's mood may be chaotic. The recognition of this dimension enables the system to generate music with a more coordinated rhythm and provide users with guidance for emotional balance. The fifth element, E5(t), represents the harmony-imbalance dimension, which is identified by the synchronization characteristics of the gamma band. The gamma wave frequency band is generally related to high-level cognitive activities and information integration states. Higher synchronization indicates emotional harmony, while low synchronization represents an imbalanced state. When the user's gamma wave synchronization is high, the system can generate brisk music to maintain emotional harmony; when the synchronization is low, the rhythm and frequency of the music can be adjusted to a more moderate state to help the user restore balance.

[0053] Embodiment 6: The emotion feature processing unit extracts emotion dynamic features by the following formula to obtain the emotion dynamic feature Φ(t):

[0054]

[0055] Among them, Φ1(t) is the accumulated emotion intensity; Φ2(t) is the emotion change rate; Φ3(t) is the emotion complexity change rate; ||·||1 is the L1 norm operation; ||·||2 is the L2 norm operation; E(t′) is the integral form of the five-dimensional emotion feature vector E(t).

[0056] Specifically, the first element Φ1(t) of the emotion dynamic feature vector represents the cumulative amount of emotion intensity. In order to enable the system to reflect the cumulative changes in user emotions, this feature is calculated by integrating the L2 norm of the five-dimensional emotion feature vector and adding an exponential decay term. Specifically, this integral gradually accumulates the L2 norm value of E(t′) from time 0 to the current time t, and multiplies it by an exponential decay factor e at each moment. -0.1(t-t′). Through this exponential decay, the system can give higher weights to more recent emotional states, while gradually reducing the impact on emotional states in the past, thereby achieving a "memory decay" effect. This design allows the system to retain historical information on emotional intensity while paying attention to the user's current emotions, providing an overall accumulated value of emotional fluctuations. This feature not only helps the system identify the user's emotional trends, but also enables the system to generate more continuous music based on the user's accumulated emotional state, so that the music can conform to the cumulative trend of emotions in rhythm and melody, enhancing the user's healing experience. The second element of the emotional dynamic feature vector, Φ2(t), is used to describe the rate of change of emotions. This feature captures the volatility of emotions at a specific time point by calculating the time derivative of the five-dimensional emotional feature vector E(t). The sum of the squares of this derivative represents the instantaneous change intensity of the emotional state in different dimensions. In order to ensure that this feature is not limited to instantaneous changes, the product of the L2 norm of the emotional feature vector and the weight factor 0.5 is further introduced to make the emotional change rate have a more refined characterization between the stable and fluctuating states. When the rate of change of emotion is large, it means that the emotion is in an unstable fluctuation. At this time, the system can adjust the rhythm and melody of the music to make it more gentle, so as to subtly calm the user's emotional fluctuations; when the rate of change of emotion is small, it means that the emotional state is relatively stable. At this time, the music can maintain the current state without frequent changes. This feature extraction method of emotion change rate enables the system to sensitively perceive the fast and slow changes of user emotions and adjust the music style in real time so that the music always keeps pace with the user's emotional changes and provides dynamic support. The third element Φ3(t) represents the rate of change of emotion complexity. Emotional complexity is an indicator to measure the diversity and complexity of emotional states. This complexity characteristic is described by the ratio of the L1 norm and the L2 norm of the five-dimensional emotion feature vector. The system can capture the distribution characteristics of emotional states in different dimensions and characterize the rate of complexity change by the derivative of the inverse tangent function. When the complexity of the emotional state changes greatly, it means that the user's emotions have fluctuated significantly in different feature dimensions. At this time, the system can generate richer and more layered music to adapt to the complex emotional state and help users find a balance in emotional diversity. When the complexity change rate is small, it indicates that the user's emotions are more consistent or single. At this time, the music can choose a simpler structure to avoid overly complex melodies interfering with the user's calm emotional state. The introduction of the emotion complexity change rate allows the system to respond to the user's emotional characteristics more carefully at the level of music style, so that the generated music can switch freely between the diversity and simplicity of emotions, making the healing effect more profound.

[0057] Embodiment 7: The music therapy signal generating unit performs music basic parameter mapping based on the five-dimensional emotion feature vector and the three-dimensional emotion dynamic feature through the following formula to generate the music basic parameter vector M(t):

[0058]

[0059] Among them, f b (t) is the basic frequency, based on the standard pitch of 440Hz; A(t) is the amplitude envelope; T(t) is the speed, the unit is BPM, and the value range is 60-180.

[0060] Specifically, in this formula, the fundamental frequency f b (t) is the fundamental frequency of the entire music. The formula adds the relaxation-tension dimension E1(t) and the concentration-distraction dimension E2(t) in the user's emotional characteristics, and adjusts the frequency in combination with the emotional intensity accumulation Φ1(t). The formula for the fundamental frequency is 440Hz is used as the base frequency, which represents the standard pitch of music. The addition of emotional features makes the frequency fluctuate with the change of emotions. When the user is nervous or focused, the base frequency will increase accordingly, and the tone of the music will become higher and brighter, thus forming synchronization with the user's nervous state to a certain extent. When the user's emotions are more relaxed or scattered, the base frequency will decrease, and the tone of the music will become low and peaceful accordingly. The amplitude of the frequency adjustment is reasonably controlled by the factor in the form of the square root of the emotional intensity, which ensures that the frequency adjustment will not be too extreme and the music is always within a gentle adjustment range. Such a basic frequency setting allows the pitch of the music to gradually increase when the user is nervous, become clear when focused, and fall back when the user is relaxed, thus naturally guiding the user's emotions to return to peace. The amplitude envelope A(t) determines the dynamic range and volume change of the music, which directly affects the user's auditory experience. The amplitude is calculated as the ratio of the accumulated emotional intensity to the rate of emotional change, and multiplied by the hyperbolic tangent function of the L2 norm of the five-dimensional emotional feature, that is, This design makes the volume higher when the emotional intensity is high and the rate of change is low, which can better drive the expression of emotions. When the rate of change of emotions increases, the amplitude envelope is reduced to avoid the music dynamics caused by emotional fluctuations being too intense, so that the volume changes more smoothly and reduces the stimulation to the user's emotions. The hyperbolic tangent function provides a nonlinear transformation here, so that the volume can gradually increase when the emotional fluctuations are small, ensuring the softness of the volume. Through such a dynamic adjustment mechanism, the system can moderately increase the volume under tense conditions and guide the user's emotions to be soothed. When the emotions are more volatile, the volume changes are appropriately smoothed to make the music more continuous and soft, prevent excessive volume from stimulating emotions, and ensure the stability of the emotional healing process. The speed of music T(t) represents the number of beats per minute (BPM), which controls the rhythm and overall speed of the music. This parameter directly affects the adaptability of music to the user's emotional changes. The formula calculates the speed by combining the sigmoid function with the emotional change rate Φ2(t) and the emotional complexity change rate Φ3(t). The final value range is between 60 and 180 BPM. The speed is calculated as T(t)=60+120·sigmoid(Φ2(t)+Φ3(t)), where the Sigmoid function provides a nonlinear response, allowing the speed to be sensitively adjusted according to emotional fluctuations and changes in complexity. When the rate of change of emotions and the rate of change of emotional complexity are large, the speed will increase rapidly, and the rhythm of the music will become active to meet the needs of users with higher emotional activities; when the rate of change of emotions and the rate of change of complexity are small, the rhythm of the music will slow down, so that the user's mood returns to a stable state, which is suitable for a state of calm emotions. The nonlinear effect of the Sigmoid function ensures that the increase or decrease of the speed will not be too abrupt, but a gradual transition, so that users can get natural emotional adjustment as the music changes during emotional fluctuations.

[0061] Embodiment 8: The music therapy signal generating unit generates a harmonic structure according to the three-dimensional emotional dynamic characteristics through the following formula to obtain a harmonic frequency response H(f, t):

[0062]

[0063] B(t)=B0·(1+Φ2(t));

[0064] σ h (t) = 2 + 3Φ3 (t);

[0065] Among them, c j (t) is the j-th order harmonic coefficient; B(t) is the harmonic bandwidth function; σ h (t) is the harmonic broadening coefficient; B0 is the harmonic basic bandwidth, which is the set value; j is the integer subscript index.

[0066] Specifically, in this formula, the harmonic frequency response H(f, t) is composed of the superposition of harmonic signals of different orders. The harmonic coefficient c of each order is j (t) is a time variable function, which distributes the energy and width of each harmonic to different frequencies by distributing the amplitude envelope A(t) and adjusting the broadening coefficient. Harmonic coefficient c j (t) is calculated as That is, by gradually attenuating the amplitude envelope A(t) according to order j, and adding a harmonic broadening coefficient σ h (t) The exponential decay term associated with this design ensures that the high-order harmonics are gradually weakened, thereby constructing a harmonic frequency response with a gradually decreasing characteristic, making the music's hierarchical structure richer while maintaining a sense of harmony. h (t) is dynamically adjusted based on the value of the emotion complexity change rate Φ3(t). When Φ3(t) is large, it means that the user's emotion complexity is high, so the broadening coefficient of the harmonic structure is also increased accordingly, making the harmonic frequency response wider and presenting a more complex and three-dimensional sound effect; when the emotion complexity is low, the broadening coefficient is reduced, making the distribution of harmonics more concentrated, so as to reflect a simpler and more harmonious sound quality. The bandwidth function B(t) controls the coverage range of the frequency in the harmonic structure and changes dynamically based on the value of the harmonic basic bandwidth B0 and the emotion change rate Φ2(t). The bandwidth function is calculated as B0·(1+Φ2(t)), that is, based on the basic bandwidth B0, it is amplified according to the size of the emotion change rate Φ2(t). When the emotion change rate is high, the user's emotion fluctuates greatly, and the bandwidth is also increased accordingly, so that the harmonic structure of the music covers a wider frequency range, thereby producing a richer sound layer; when the emotion change rate is low, the bandwidth shrinks, the harmonic structure of the music is more concentrated, and the sound quality tends to be simple and calm. The adjustment of bandwidth brings a more complex sound experience when the emotions fluctuate violently, and provides a pure tone when the emotions are calm, so that users can get appropriate sound guidance in different emotional states. The entire harmonic frequency response is completed by the superposition of each order of harmonic components. The formula uses To control the frequency distribution of each harmonic frequency, f b (t) as the fundamental frequency determines the frequency center of the harmonics, while the bandwidth function B(t) ensures the range of frequency distribution. This design makes the harmonic structure of the music have a sense of hierarchy in frequency and can be dynamically adjusted with the changes of the fundamental frequency and bandwidth. For example, when the user is in a state of high tension or concentration, the fundamental frequency f b(t) increases, the bandwidth expands, making the music brighter and wider, thus synchronizing the sound frequency with the user's emotions; when the user is in a state of lower emotional fluctuation, the fundamental frequency decreases and the bandwidth narrows, making the music softer and more focused, forming a relaxing listening experience. This dual regulation of fundamental frequency and harmonic bandwidth ensures that the music structure can be consistent with emotional fluctuations.

[0067] Embodiment 9: The music therapy signal generating unit generates a music time sequence structure according to the harmonic frequency response by the following formula to obtain the final generated healing music signal:

[0068]

[0069] Among them, S(t) is the final generated healing music signal; W(f, t) is the time-varying modulation window function; g i (·) is the Sigmoid modulation function; i is an integer subscript index; k i is the modulation slope coefficient, where k1=2.0, k2=1.5, k3=1.0; x0 is the modulation midpoint, with a value of 0.5.

[0070] Specifically, the first part of the formula defines the final healing music signal S(t), which is composed of the harmonic frequency response H(f, t), the time-varying modulation window function W(f, t) and the complex exponential function e j2πft The product of is formed by integrating the frequency f. This integration process integrates the corresponding characteristics of each frequency component into a single time domain signal S(t), thereby obtaining a comprehensive audio signal. Through the frequency characteristics of the harmonic frequency response, the system can capture the changes in the user's emotional state when generating music signals, and the complex exponential function makes the phase and amplitude of each frequency component change dynamically in time, achieving a smooth transition of the music, thereby improving the continuity and listening experience of the music. The music signal generated in this way is not only a simple frequency superposition, but is further optimized in the timing structure, so that the music can be adjusted accordingly with the real-time changes in the user's emotional state. The introduction of the time-varying modulation window function W(f, t) plays an important role in generating healing music signals. This function realizes the timing control of each frequency component through a combination of Gaussian distribution and Sigmoid function. Specifically, the Gaussian distribution term of the window function The frequency response is mainly concentrated on the fundamental frequency f b(t), and the width of the response range is controlled by the bandwidth B(t). The bandwidth B(t) is affected by the dynamic characteristics of the user's emotions. When the emotions fluctuate greatly, the bandwidth increases, so that the frequency response covers a wider range and forms a richer frequency structure; when the emotions are stable, the bandwidth narrows, making the frequency distribution more concentrated and creating a softer auditory effect. This bandwidth control mechanism ensures that the frequency distribution of the music signal in different emotional states can change with the emotional fluctuations, providing users with more accurate emotional guidance.

[0071] In addition, the Sigmoid modulation function g in the time-varying modulation window function W(f, t) i (Φ i (t)) further enhances the personalization of the music signal. The modulation function g i (·) Dynamically adjust the characteristics of the audio signal through each component of the three-dimensional emotional dynamic characteristics, reflecting the impact of the accumulated emotional intensity, the rate of emotional change, and the rate of change of emotional complexity on the music signal. The form of each modulation function is where k i is the modulation slope coefficient, which controls the speed of change of the Sigmoid function, and x0 is the modulation midpoint. i The settings of are 2.0, 1.5, and 1.0 to reflect the influence of different emotional characteristics on music modulation. The high-slope modulation function will produce a faster response, making the music adapt more quickly when the emotions fluctuate strongly; while the low-slope modulation function provides a gentle adjustment effect, making the music gradually adjust when the emotions are stable to maintain the stability of the music. This modulation function setting enables the music signal to have corresponding adaptability and response speed under different emotional states, so that the music is closer to the user's inner state in terms of hearing. On this basis, the system's complex exponential function e j2πft This ensures the continuous transition of frequency components in time, avoiding the disharmony caused by sudden changes in frequency in the music signal. This complex exponential term makes the music signal smoothly transition in time by gradually adjusting the frequency components, so that users can get a stable and comfortable music experience in different emotional states. The healing music signal generated in this way is not only coordinated in pitch, volume and rhythm, but also consistent in frequency distribution and timing structure, so that the music and the user's emotional fluctuations echo each other, thereby achieving a more effective emotional healing effect.

[0072] The preferred embodiments of the present disclosure are described above with reference to the accompanying drawings, but the scope of the present disclosure is not limited thereby. Any modification, equivalent substitution and improvement made by those skilled in the art without departing from the scope and essence of the present disclosure shall be within the scope of the present disclosure.

Claims

1. A personalized music therapy system based on brainwave regulation, characterized in that: The system includes: a brain wave signal acquisition and decomposition unit, an emotion feature processing unit and a music therapy signal generation unit; the brain wave signal acquisition and decomposition unit is used to acquire brain wave signals, and by combining the brain wave signals with wavelet basis functions of different brain wave signal frequency bands, the brain wave signals are multi-scale decomposed to obtain signal decomposition results, and based on the signal decomposition results, the standardized brain wave energy density is calculated; the emotion feature processing unit is used to construct a five-dimensional emotion feature vector according to the signal decomposition results and the standardized brain wave energy density; based on the five-dimensional emotion feature vector, the emotion dynamic feature is extracted to obtain a three-dimensional emotion dynamic feature; the music therapy signal generation unit is used to map music basic parameters based on the five-dimensional emotion feature vector and the three-dimensional emotion dynamic feature to generate a music basic parameter vector; based on the three-dimensional emotion dynamic feature, a harmonic structure is generated to obtain a harmonic frequency response; based on the harmonic frequency response, a music time series structure is generated to obtain a finally generated healing music signal.

2. The personalized music therapy system based on brainwave regulation as claimed in claim 1, characterized in that: Brain wave signal frequency bands include: delta band, theta band, alpha band, beta band and gamma band.

3. The personalized music therapy system based on brainwave regulation as claimed in claim 2, characterized in that: The brain wave signal collection and decomposition unit performs multi-scale decomposition on the brain wave signal by the following formula to obtain the signal decomposition result X(f, t): Where n is an integer subscript index; t′ is the time integral variable; t is time; a n is the time scale factor, corresponding to the period of the brain wave signal frequency band; a1 = 0.5 seconds, corresponding to the δ band; a2 = 0.167 seconds, corresponding to the θ band; a3 = 0.1 seconds, corresponding to the α band; a4 = 0.05 seconds, corresponding to the β band; a5 = 0.025 seconds, corresponding to the γ band; f n is the center frequency of each brain wave signal frequency band; f1 = 2 Hz, corresponding to the center frequency of the δ band; f2 = 6 Hz, corresponding to the center frequency of the θ band; f3 = 10 Hz, corresponding to the center frequency of the α band; f4 = 20 Hz, corresponding to the center frequency of the β band; f5 = 40 Hz, corresponding to the center frequency of the γ band; σ n is the energy attenuation width of each brain wave signal frequency band; σ1 = 1Hz, corresponding to the energy attenuation width of the δ band; σ2 = 2Hz, corresponding to the energy attenuation width of the θ band; σ3 = 2.5Hz, corresponding to the energy attenuation width of the α band; σ4 = 8.5Hz, corresponding to the energy attenuation width of the β band; σ5 = 20Hz, corresponding to the energy attenuation width of the γ band; S(t) is the brain wave signal, S(t′) is the brain wave signal in integral form; f is the frequency; ψ n (·) is the wavelet basis function of the nth frequency band.

4. The personalized music therapy system based on brainwave regulation as claimed in claim 3, characterized in that: The brain wave signal acquisition and decomposition unit calculates the standardized brain wave energy density D(f, t) based on the signal decomposition result by the following formula: Among them, Δf is the frequency integration window width, ranging from 1 Hz to 3 Hz; f′ is the frequency integration variable; |·| is the amplitude operator.

5. The personalized music therapy system based on brainwave regulation as claimed in claim 4, characterized in that: The emotion feature processing unit constructs a five-dimensional emotion feature vector E(t) based on the signal decomposition result and the standardized brain wave energy density, which is expressed as: Among them, E1(t) is the relaxation-tension dimension element; E2(t) is the focus-distraction dimension element; E3(t) is the stability-fluctuation dimension element; E4(t) is the coordination-chaos dimension element; E5(t) is the harmony-imbalance dimension element; Phase(·) is the phase extraction function; Sync(·) is the synchronization calculation function.

6. The personalized music therapy system based on brainwave regulation as claimed in claim 5, characterized in that: The emotion feature processing unit extracts the emotion dynamic feature by the following formula to obtain the emotion dynamic feature Φ(t): Among them, Φ1(t) is the accumulated emotion intensity; Φ2(t) is the emotion change rate; Φ3(t) is the emotion complexity change rate; ||·||1 is the L1 norm operation; ||·||2 is the L2 norm operation; E(t′) is the integral form of the five-dimensional emotion feature vector E(t).

7. The personalized music therapy system based on brainwave regulation as claimed in claim 6, characterized in that: The music therapy signal generating unit performs music basic parameter mapping based on the five-dimensional emotion feature vector and the three-dimensional emotion dynamic feature through the following formula to generate the music basic parameter vector M(t): Among them, f b (t) is the basic frequency, based on the standard pitch of 440Hz; A(t) is the amplitude envelope; T(t) is the speed, the unit is BPM, and the value range is 60-180.

8. The personalized music therapy system based on brainwave regulation as claimed in claim 7, characterized in that: The music therapy signal generating unit generates a harmonic structure according to the three-dimensional emotional dynamic characteristics through the following formula to obtain a harmonic frequency response H(f, t): B(t)=B0·(1+Φ2(t)); s h (t)=2+3φ3(t); Among them, c j (t) is the j-th order harmonic coefficient; B(t) is the harmonic bandwidth function; σ h (t) is the harmonic broadening coefficient; B0 is the harmonic basic bandwidth, which is the set value; j is the integer subscript index.

9. The personalized music therapy system based on brainwave regulation as claimed in claim 8, characterized in that: The music healing signal generating unit generates a music time sequence structure according to the harmonic frequency response by the following formula to obtain the final generated healing music signal: Among them, S(t) is the final generated healing music signal; W(f, t) is the time-varying modulation window function; g i (·) is the Sigmoid modulation function; i is an integer subscript index; k i is the modulation slope coefficient, where k1=2.0, k2=1.5, k3=1.0; x0 is the modulation midpoint, with a value of 0.5.

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