Personalized brainwave-inducing audio generation method and system

Abstract

The present application relates to the technical field of neuroacoustics, in particular to a personalized brainwave induction audio generation method and system, comprising: step S1, extracting multi-dimensional features of an input audio signal, the extracted dimensions including at least time domain features, frequency domain features, pitch features, emotion features and complexity features; step S2, based on the multi-dimensional features extracted in step S1, parallelly calculating parameters of four neuroacoustic induction mechanisms; step S3, according to the features obtained in step S1 and the four-mechanism parameters obtained in step S2, fusing signals corresponding to the four mechanisms through a weight distribution strategy and a signal synthesis formula to generate a final brainwave induction audio. The present application has the advantages that: four mechanisms of rhythm synchronization, harmonic resonance, neural oscillation coupling and binaural beat effect are fused, mechanism parameters are dynamically adjusted through analysis of music features, and efficient personalized brainwave induction is realized.

Description

Technical Field

[0001] This invention relates to the field of neuroacoustics, specifically to a method and system for generating personalized brainwave-induced audio. Background Technology

[0002] Brainwaves are electrical oscillations generated by the activity of neurons in the brain. They are classified into five categories based on frequency: delta (<4Hz), theta (4–8Hz), alpha (8–14Hz), beta (14–30Hz), and gamma (>30Hz). These correspond to different states of consciousness, such as unconsciousness, subconsciousness, bridging consciousness, conscious awareness, and high concentration, and directly determine an individual's emotions, behavior, and learning performance. Brainwave training technology uses external rhythmic stimulation to synchronize brain electrical activity to a target frequency, thereby rapidly regulating the state of consciousness. Acoustic induction has become the mainstream method due to its ease of implementation. Among these methods, "binaural beats" have the longest history: In 1839, Dove discovered that when the left and right ears listen to two sinusoidal sounds with a frequency difference of less than 30Hz and a carrier frequency of less than 1500Hz, the central auditory system generates a "third tone" equal to the frequency difference, thereby stimulating the brain to synchronize with that frequency difference. After Oster confirmed in 1973 that it could quantitatively affect the brain and endocrine system, binaural beat frequency was used to assist in meditation, pain relief, sleep induction, and postoperative opioid dosage reduction.

[0003] However, existing technologies generally use a single-frequency sine wave as the carrier wave, resulting in a monotonous sound lacking musicality. The brain easily adapts to this, and the induction efficiency decreases over time. Furthermore, the frequency template is fixed (e.g., a universal 10Hz), failing to consider individual real-time EEG differences. This necessitates expert manual parameter selection, leading to high experimental and adjustment costs and hindering personalized and continuous application needs. Therefore, there is an urgent need for a brainwave induction scheme that combines musicality, real-time adaptive optimization of stimulation parameters, low cost, and universal applicability to everyday scenarios. Moreover, existing brainwave induction techniques primarily employ fixed-frequency audio with a single mechanism, failing to fully utilize the synergistic effects of multiple neuroacoustic mechanisms. Traditional methods suffer from the following technical problems: limited effectiveness of a single binaural beat frequency mechanism, lack of multi-mechanism synergy; failure to consider the synchronous coupling effect of musical rhythm and neural oscillations; neglect of the enhancing effect of harmonic resonance on brainwave induction; and lack of adaptive adjustment mechanisms based on individual neural characteristics. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for generating personalized brainwave-induced audio, which can achieve efficient personalized brainwave induction by integrating multiple neuroacoustic mechanisms and dynamically optimize the parameters of each mechanism according to music characteristics.

[0005] To achieve the above objectives, a personalized brainwave-induced audio generation method and system are designed, comprising: Step S1, extracting multidimensional features of the input audio signal, including but not limited to: time-domain features, frequency-domain features, pitch features, emotional features, and complex features; Step S2, based on the multidimensional features extracted in Step S1, parallelly calculating parameters of one or more neuroacoustic induction mechanisms, including: rhythm synchronization mechanism parameters, harmonic resonance mechanism parameters, neural oscillation coupling mechanism parameters, and binaural beat frequency effect parameters; Step S3, fusing the features obtained in Step S1 with the parameters of one or more neuroacoustic induction mechanisms in Step S2 using a weighting strategy and a signal synthesis formula to generate the final brainwave-induced audio.

[0006] Preferably, the present invention further includes: the time-domain features include at least: beat speed, beat intensity, and beat stability; the frequency-domain features include at least: fundamental frequency and harmonic structure; the pitch features include at least: musical tonality; the emotional features include at least: emotional value; the complexity features include at least: rhythmic complexity and harmonic complexity; step 2 further includes: step S2.1 calculating rhythm synchronization mechanism parameters: calculating the main beat frequency f_rhythm based on the beat speed, calculating the beat synchronization target frequency f_sync based on the set target brainwave frequency f_target and the main beat frequency f_rhythm, and calculating the time-domain signal S_rhythm(t) generated by the rhythm synchronization mechanism based on the synchronization target frequency f_sync; step S2.2 calculating harmonic... Wave resonance mechanism parameters: Calculate the optimal harmonic matching f_resonance with the target brainwave frequency based on the music fundamental frequency f_fundamental and the set target brainwave frequency f_target; calculate the time-domain signal S_harmonic(t) generated by the harmonic resonance mechanism based on the optimal harmonic matching f_resonance; Step S2.3 Calculate neural oscillation coupling mechanism parameters: Calculate the neural oscillation coupling frequency f_coupling based on the set target brainwave frequency f_target and the modulation frequency f_modulation set according to the specific induction task; calculate the time-domain signal S_coupling(t) generated by the neural oscillation coupling mechanism based on the neural oscillation coupling frequency f_coupling; Step S2.4. Calculate the binaural beat frequency parameters: Calculate the fundamental carrier frequency f_carrier based on the music key and the fundamental frequency lookup table key_frequency_map; calculate the binaural beat frequency difference Δf_binaural based on the set target brainwave frequency f_target and correction factors; calculate the left ear frequency f_left based on the fundamental carrier frequency f_carrier; calculate the right ear frequency f_right based on the fundamental carrier frequency f_carrier and the binaural beat frequency difference Δf_binaural; and calculate the time-domain signal S_binaural(t) generated by the binaural beat frequency effect mechanism based on the left ear frequency f_left and the right ear frequency f_right. The weight allocation strategy includes: calculating the rhythm synchronization mechanism weight W_rhythm based on the rhythm complexity rhythm_complexity, and calculating the harmonic resonance mechanism weight W_harmoni based on the harmonic richness harmonic richness. c. Based on the neural compatibility calculated by the system from the user's historical brainwave feedback data, the weight W_coupling of the neural oscillation coupling mechanism is calculated. The weight W_binaural of the binaural beat frequency mechanism is calculated based on the weights W_rhythm (rhythm), W_harmonic (harmonic resonance), and W_coupling. Finally, the synthesized audio signal S_total(t) is obtained by jointly calculating the time-domain signals S_rhythm(t) generated by the rhythm synchronization mechanism, S_harmonic(t) generated by the harmonic resonance mechanism, S_coupling(t) generated by the neural oscillation coupling mechanism, S_binaural(t) generated by the binaural beat frequency mechanism, and the weights W_rhythm, W_harmonic, W_coupling, and W_binaural.

[0007] Preferably, the present invention further includes: step S1 specifically includes: the extracted time-domain features including: beat speed, beat intensity, beat stability, and microbeat variation; the extracted frequency-domain features including: fundamental frequency, harmonic structure, spectral centroid, spectral bandwidth, and spectral roll-off point; the extracted pitch features including: musical tonality, mode type, chord progression, and interval relationship; the extracted emotional features including: energy value, emotional value, tension, and activity; and the extracted complexity features including: MFCC coefficients, hue vector, rhythmic complexity, and harmonic complexity.

[0008] Preferably, the present invention further includes: the rhythm synchronization mechanism parameters in step S2 specifically include: main beat frequency: f_rhythm = BPM / 60; where BPM represents the tempo of the music, in beats per minute, obtained through multi-dimensional music feature extraction in step S1; beat harmonic sequence: f_harm_n = f_rhythm * n; where n represents the order of the beat harmonics, taking values ​​of (1, 2, 3, 4, 5); calculating beat synchronization intensity: S_rhythm = beat_stability * beat_intensity; where beat_stability represents the beat intensity. Stability is used to quantify the uniformity of the beat, and beat_intensity represents the beat intensity, used to quantify the strength of the beat. Both are obtained through multi-dimensional music feature extraction in step S1. The target frequency for beat synchronization is: f_sync = f_target * (1 + α * sin (2π * f_rhythm * t)); where f_target represents the target brainwave frequency, which is set by the specific induction task; α represents the synchronization depth coefficient, α ∈ [0.1, 0.3], and t represents the time variable; the rhythm synchronization mechanism generates a time-domain signal: S_rhythm(t) = sin (2π * f_sync * t).

[0009] Preferably, the present invention further includes: the harmonic resonance mechanism parameters in step S2 specifically include: extracting the fundamental frequency of music from multidimensional music features: f_fundamental; harmonic sequence: f_harmonic_k=f_fundamental*k; where k represents the harmonic index, taking values ​​of 1, 2, 3, ..., 10; optimal harmonic matching with the target brainwave frequency: k_optimal=argmin|f_target-f_fundamental*k|; f_resonance=f_fundamental*k_optimal; where k_op timal represents the harmonic index that minimizes |f_target-f_fundamental*k|, f_target represents the target brainwave frequency, which is set by the specific induction task, and f_resonance represents the selected optimal harmonic resonance frequency; harmonic resonance intensity: R_harmonic=1 / (1+|f_target-f_resonance|); where R_harmonic represents the harmonic resonance intensity, with a value range of 0-1; the harmonic resonance mechanism generates a time-domain signal: S_harmonic(t)=sin(2π*f_resonance*t).

[0010] Preferably, the present invention further includes: the neural oscillation coupling mechanism parameters in step S2 specifically include: neural oscillation coupling frequency: f_coupling=f_target+β*cos(2π*f_modulation*t); where β represents the coupling strength coefficient, used to control the modulation amplitude; f_target represents the target brainwave frequency, set by the specific induction task; f_modulation represents the modulation frequency set according to the specific induction task; defining the coupling phase relationship: φ_coupling=2π*f_rhythm*t+φ_offset; where φ_coupling represents the instantaneous phase of neural oscillation coupling; φ_offset represents the initial phase offset, set by the system according to the specific induction task; coupling coefficient: C_neural=tanh(|f_target-f_rhythm| / σ); where C_neural represents the neural oscillation coupling coefficient, with a value range of 0-1; σ is the coupling sensitivity parameter, used to determine the steepness of the output curve; the neural oscillation coupling mechanism generates a time-domain signal: S_coupling(t)=sin(2π*f_coupling*t).

[0011] Preferably, the present invention further includes: the binaural beat frequency effect parameters in step S2 specifically include: calculating the fundamental carrier frequency: f_carrier = key_frequency_map(music_key); where key_frequency_map() represents a fundamental frequency lookup table indexed by music tonality, and music_key is the music tonality; binaural beat frequency difference: Δf_binaural = f_target * correction_factors; where correction_factors represents correction coefficients for individual differences and hardware system, and f_target represents the target brainwave frequency, determined by... Body-induced task settings; Left ear frequency: f_left=f_carrier; Right ear frequency: f_right=f_carrier+Δf_binaural; Binaural beat modulation depth: M_binaural=0.3+0.4*valence_factor; where M_binaural represents the binaural beat modulation depth; valence_factor represents the music emotional value factor, obtained through multi-dimensional music feature extraction in step S1; Binaural beat effect mechanism generates time-domain signal: S_binaural(t)=sin(2π*f_left*t)-sin(2π*f_right*t).

[0012] Preferably, the present invention further includes: the weight allocation strategy in S3 specifically includes: dynamically allocating the weights of the four mechanisms according to the music features and target state extracted in step S1: W_rhythm=0.2+0.3*rhythm_complexity; W_harmonic=0.15+0.25*harmonic_richness; W_coupling=0.25+0.2*neural_compatibility; W_binaural=0.40.1*(W_rhythm+W_harmonic+W_coupling); wherein, W_rhythm represents the weight of the rhythm synchronization mechanism; rhythm_complexity represents the rhythm complexity, obtained through multi-dimensional music feature extraction in step S1; W_harmonic represents the weight of the harmonic resonance mechanism; harmonic_richness represents the harmonic complexity, obtained through multi-dimensional music feature extraction in step S1; W_coupling represents the weight of the neural oscillation coupling mechanism; neural_compatibility represents the neural compatibility, calculated by the system based on the user's historical brainwave feedback data; W_binaural represents the weight of the binaural beat frequency mechanism, used to ensure that the sum of the four weights is 1.

[0013] Preferably, the present invention further includes: the signal synthesis formula in S3 specifically includes: S_total(t) = W_rhythm*S_rhythm(t) + W_harmonic*S_harmonic(t) + W_coupling*S_coupling(t) + W_binaural*S_binaural(t); S_total(t) represents the final synthesized audio signal; S_rhythm(t) represents the time-domain signal generated by the rhythm synchronization mechanism; W_rhythm represents the weight of the rhythm synchronization mechanism; S_harmonic(t) represents the time-domain signal generated by the harmonic resonance mechanism; W_harmonic represents the weight of the harmonic resonance mechanism; S_coupling(t) represents the time-domain signal generated by the neural oscillation coupling mechanism; W_coupling represents the weight of the neural oscillation coupling mechanism; S_binaural(t) represents the time-domain signal generated by the binaural beat frequency effect mechanism; W_binaural represents the weight of the binaural beat frequency mechanism; and the final output signal is S_total(t).

[0014] The present invention also provides a personalized brainwave-induced audio generation system for the method, comprising: a music feature analysis module for performing step S1; a four-mechanism parameter calculation module for performing step S2; a multi-mechanism fusion algorithm module for performing step S3; and a signal synthesis and output module for outputting the final brainwave-induced audio.

[0015] Preferably, the present invention further includes: the music feature analysis module comprising: a time-domain feature extraction submodule, a frequency-domain feature extraction submodule, a pitch feature extraction submodule, an emotion feature extraction submodule, and a complex feature extraction submodule.

[0016] Compared with the prior art, the advantages of this invention are: This invention discloses a personalized brainwave-induced audio generation method and system based on multiple neuroacoustic mechanisms. This method innovatively integrates four mechanisms: rhythm synchronization, harmonic resonance, neural oscillation coupling, and binaural beat frequency effect. By analyzing musical characteristics and dynamically adjusting the parameters of each mechanism, it achieves efficient personalized brainwave induction. This invention possesses technical advantages such as multi-level personalization, dynamic adaptability, and self-learning ability, providing a complete solution for the industrial application of neuroacoustic technology. Attached Figure Description

[0017] Figure 1 This is a block diagram of the personalized brainwave induction algorithm of the present invention. Detailed Implementation

[0018] To make the purpose, principle and structure of the present invention clearer, the following specific embodiments are further described.

[0019] See Figure 1This invention provides a personalized brainwave-induced audio generation method and system, comprising: Step S1, extracting multidimensional features of the input audio signal, wherein the extracted dimensions include at least time-domain features, frequency-domain features, pitch features, emotional features, and complex features; wherein the time-domain features include at least: beat speed, beat intensity, and beat stability; the frequency-domain features include at least: fundamental frequency and harmonic structure; the pitch features include at least: musical tonality; the emotional features include at least: emotional value; and the complex features include at least: rhythmic complexity and harmonic complexity; Step S2, calculating the parameters of the rhythm synchronization mechanism, harmonic resonance mechanism, neural oscillation coupling mechanism, and binaural beat frequency effect based on the features obtained in Step S1; Step S3, calculating the parameters of four neuroacoustic induction mechanisms in parallel based on the multidimensional features extracted in Step S1: Step S2.1 Calculating the rhythm synchronization mechanism parameters: calculating the main beat frequency f_rhythm based on the beat speed, and calculating the parameters based on the set target brainwave frequency f_target and... Step S2.1 Calculate the target frequency f_sync of the beat synchronization based on the main beat frequency f_rhythm, and generate the time-domain signal S_rhythm(t) based on the target frequency f_sync; Step S2.2 Calculate the harmonic resonance mechanism parameters: calculate the optimal harmonic match f_resonance with the target brainwave frequency based on the music fundamental frequency f_fundamental and the set target brainwave frequency f_target; calculate the harmonic resonance mechanism based on the optimal harmonic match f_resonance to generate the time-domain signal S_harmonic(t); Step S2.3 Calculate the neural oscillation coupling mechanism parameters: calculate the neural oscillation coupling frequency f_coupling based on the set target brainwave frequency f_target and the modulation frequency f_modulation set according to the specific induction task, and calculate the neural oscillation coupling mechanism based on the neural oscillation coupling frequency f_coupling to generate the time-domain signal S_coupling(t); Step S2.4. Calculate the binaural beat frequency effect parameters: Calculate the fundamental carrier frequency f_carrier based on the music key and the fundamental frequency lookup table key_frequency_map; calculate the binaural beat frequency difference Δf_binaural based on the set target brainwave frequency f_target and correction factors; calculate the left ear frequency f_left based on the fundamental carrier frequency f_carrier; calculate the right ear frequency f_right based on the fundamental carrier frequency f_carrier and the binaural beat frequency difference Δf_binaural; calculate the time-domain signal S_binaural(t) generated by the binaural beat frequency effect mechanism based on the left ear frequency f_left and the right ear frequency f_right; Step S3: Based on the features obtained in step S1 and the four-mechanism parameters obtained in step S2, fuse the signals corresponding to the four mechanisms to generate the final brainwave induced audio through a weight allocation strategy and signal synthesis formula; the weight allocation strategy includes: calculating the rhythm synchronization mechanism weight W_rhythm based on the rhythm complexity rhythm_complexity, and calculating the harmonic complexity h_rhythm_complexity based on the harmonic complexity h_rhythm_complexity. The harmonic resonance mechanism weight W_harmonic is calculated using `monic_richness`. The neural oscillation coupling mechanism weight W_coupling is calculated based on the neural compatibility (calculated by the system using historical EEG feedback data). The binaural beat frequency mechanism weight W_binaural is calculated based on the rhythm synchronization mechanism weight W_rhythm, the harmonic resonance mechanism weight W_harmonic, and the neural oscillation coupling mechanism weight W_coupling. Finally, the synthesized audio signal S_total(t) is obtained by jointly calculating the time-domain signals generated by the rhythm synchronization mechanism S_rhythm(t), the harmonic resonance mechanism S_harmonic(t), the neural oscillation coupling mechanism S_coupling(t), and the binaural beat frequency mechanism S_binaural(t), along with the weights W_rhythm, W_harmonic, W_coupling, and W_binaural.

[0020] Step S1 specifically includes: extracted time-domain features including: beat speed, beat intensity, beat stability, and microbeat variation; extracted frequency-domain features including: fundamental frequency, harmonic structure, spectral centroid, spectral bandwidth, and spectral roll-off point; extracted pitch features including: musical tonality, mode type, chord progression, and interval relationship; extracted emotional features including: energy value, emotional value, tension, and activity; and extracted complexity features including: MFCC coefficients, hue vector, rhythmic complexity, and harmonic complexity.

[0021] The rhythm synchronization mechanism parameters in step S2 specifically include: Main beat frequency: f_rhythm = BPM / 60; where BPM represents the tempo of the music, measured in beats per minute, obtained through multi-dimensional music feature extraction in step S1; Beat harmonic sequence: f_harm_n = f_rhythm * n; where n represents the order of the beat harmonics, taking values ​​of (1, 2, 3, 4, 5); Calculating beat synchronization intensity: S_rhythm = beat_stability * beat_intensity; where beat_stability represents beat stability, used for... The uniformity of the beat is quantified, and beat_intensity represents the beat intensity, which is used to quantify the strength of the beat. Both are obtained through multi-dimensional music feature extraction in step S1. The target frequency for beat synchronization is: f_sync=f_target*(1+α*sin(2π*f_rhythm*t)); where f_target represents the target brainwave frequency, which is set by the specific induction task; α represents the synchronization depth coefficient, α∈[0.1,0.3], and t represents the time variable; the rhythm synchronization mechanism generates a time-domain signal: S_rhythm(t)=sin(2π*f_sync*t).

[0022] The harmonic resonance mechanism parameters in step S2 specifically include: extracting the fundamental frequency of music from multidimensional music features: f_fundamental; harmonic sequence: f_harmonic_k=f_fundamental*k; where k represents the harmonic index, taking values ​​of 1, 2, 3, ..., 10; optimal harmonic matching with the target brainwave frequency: k_optimal=argmin|f_target-f_fundamental*k|; f_resonance=f_fundamental*k_optimal; where k_optimal represents... The harmonic index that minimizes |f_target - f_fundamental*k| is given, where f_target represents the target brainwave frequency, set by the specific induction task, and f_resonance represents the selected optimal harmonic resonance frequency; harmonic resonance intensity: R_harmonic = 1 / (1 + |f_target - f_resonance|); where R_harmonic represents the harmonic resonance intensity, with a value range of 0-1; the harmonic resonance mechanism generates the time-domain signal: S_harmonic(t) = sin(2π*f_resonance*t).

[0023] The parameters of the neural oscillation coupling mechanism in step S2 specifically include: neural oscillation coupling frequency: f_coupling=f_target+β*cos(2π*f_modulation*t); where β represents the coupling strength coefficient, used to control the modulation amplitude; f_target represents the target brainwave frequency, set by the specific induction task; f_modulation represents the modulation frequency set according to the specific induction task; defining the coupling phase relationship: φ_coupling=2π*f_rhythm*t+φ_offset; where φ_coupling represents the instantaneous phase of neural oscillation coupling; φ_offset represents the initial phase offset, set by the system according to the specific induction task; coupling coefficient: C_neural=tanh(|f_target-f_rhythm| / σ); where C_neural represents the neural oscillation coupling coefficient, with a value range of 0-1; σ is the coupling sensitivity parameter, used to determine the steepness of the output curve; the neural oscillation coupling mechanism generates a time-domain signal: S_coupling(t)=sin(2π*f_coupling*t).

[0024] The binaural beat frequency effect parameters in step S2 specifically include: calculating the fundamental carrier frequency: f_carrier = key_frequency_map(music_key); where key_frequency_map() represents a fundamental frequency lookup table indexed by music tonality, and music_key is the music tonality; binaural beat frequency difference: Δf_binaural = f_target * correction_factors; where correction_factors represents correction coefficients for individual differences and hardware system, and f_target represents the target brainwave frequency, determined by the specific induction task. Define the following: Left ear frequency: f_left = f_carrier; Right ear frequency: f_right = f_carrier + Δf_binaural; Binaural beat modulation depth: M_binaural = 0.3 + 0.4 * valence_factor; where M_binaural represents the binaural beat modulation depth; valence_factor represents the music emotional value factor, obtained through multi-dimensional music feature extraction in step S1; Binaural beat effect mechanism generates time-domain signal: S_binaural(t) = sin(2π * f_left * t) - sin(2π * f_right * t).

[0025] The weight allocation strategy in S3 specifically includes: dynamically allocating the weights of the four mechanisms based on the music features extracted in step S1 and the target state: W_rhythm = 0.2 + 0.3 * rhythm_complexity; W_harmonic = 0.15 + 0.25 * harmonic_richness; W_coupling = 0.25 + 0.2 * neural_compatibility; W_binaural = 0.4 + 0.1 * (W_rhythm + W_harmonic + W_coupling); where W_rhythm = 0.25 + 0.2 * neural_compatibility; W_binaural = 0.4 + 0.1 * (W_rhythm + W_harmonic + W_coupling); where W_rhythm = 0.25 + 0.2 * neural_compatibility; W_binaural = 0.4 + 0.1 * (W_rhythm + W_harmonic + W_coupling); hm represents the weight of the rhythm synchronization mechanism; rhythm_complexity represents the rhythm complexity, obtained through multi-dimensional music feature extraction in step S1; W_harmonic represents the weight of the harmonic resonance mechanism; harmonic_richness represents the harmonic complexity, obtained through multi-dimensional music feature extraction in step S1; W_coupling represents the weight of the neural oscillation coupling mechanism; neural_compatibility represents the neural compatibility, calculated by the system based on the user's historical brainwave feedback data; W_binaural represents the weight of the binaural beat frequency mechanism, used to ensure that the sum of the four weights is 1.

[0026] The signal synthesis formula in S3 specifically includes: S_total(t) = W_rhythm*S_rhythm(t) + W_harmonic*S_harmonic(t) + W_coupling*S_coupling(t) + W_binaural*S_binaural(t); S_total(t) represents the final synthesized audio signal; S_rhythm(t) represents the time-domain signal generated by the rhythm synchronization mechanism; W_rhythm represents the weight of the rhythm synchronization mechanism; S_harmonic(t) represents the time-domain signal generated by the harmonic resonance mechanism; W_harmonic represents the weight of the harmonic resonance mechanism; S_coupling(t) represents the time-domain signal generated by the neural oscillation coupling mechanism; W_coupling represents the weight of the neural oscillation coupling mechanism; S_binaural(t) represents the time-domain signal generated by the binaural beat frequency effect mechanism; W_binaural represents the weight of the binaural beat frequency mechanism; and the final output signal is S_total(t).

[0027] It is worth noting that: “f_harm_n=f_rhythm*n (n=1–5)”, “S_rhythm=beat_stability*beat_intensity”, “f_harmonic_k=f_fundamental*k (k=1–10)”, “R_harmonic=1 / (1+|f_target−f_resonance|)”, “φ_coupling=2π*f_rhythm*t+φ_offset”, “C_neural=tanh(|f_target−f_rhythm| / σ)”, and “M_binaural=0.3+0.4*valence_factor” are not directly involved in the calculation of the final synthesized signal expression, but they all have corresponding roles.

[0028] The following section explains the exact location, calculation purpose, data flow, and usage of the seven parameters that are not directly involved in the calculation of the final signal expression in this invention.

[0029] 1. "f_harm_n=f_rhythm*n".

[0030] Its design purpose is to generate a list of 1st to 5th harmonics of the beat frequency for use as a pool of candidate synchronization frequencies. An interface is reserved for future multi-order beat synchronization expansion (such as 2nd and 3rd beat frequency doubling coupling).

[0031] 2. "S_rhythm=beat_stability*beat_intensity".

[0032] Its design purpose is to quantify the "beat salience" of the current segment. In weight allocation, it can be mapped as one of the inputs to "rhythm_complexity": "W_rhythm = 0.2 + 0.3 * rhythm_complexity". Here, "rhythm_complexity" can be obtained by normalizing "S_rhythm" (which can be jointly expressed through "rhythmic complexity"). It can be used to indirectly influence weights and visualize audio feature metrics.

[0033] 3. “f_harmonic_k=f_fundamental*k” (k=1–10).

[0034] Its design aims to generate 10 candidate harmonic frequencies for finding the best match.

[0035] In the data flow direction, it is immediately consumed by "k_optimal=argmin|f_target−f_harmonic_k|", selecting a single root "f_resonance"; after selection, it is discarded and no longer participates in the calculation. Its function is a one-time search space, which is discarded after use.

[0036] 4. "R_harmonic=1 / (1+|f_target−f_resonance|)" Its design purpose is to measure the frequency difference between the "selected harmonic" and the target brainwave. The data flow is as follows: during the weight allocation phase, it can be mapped to a component of "harmonic_richness": "W_harmonic = 0.15 + 0.25 * harmonic_richness", where "harmonic_richness" can be directly taken as "R_harmonic" or obtained by normalizing it. It can indirectly affect the weights and visualize audio feature indicators.

[0037] 5. "φ_coupling=2π*f_rhythm*t+φ_offset".

[0038] Its design purpose is to provide an instantaneous phase reference for the coupled oscillator. It is also used as a reserved phase interface and for debugging visualization.

[0039] 6. "C_neural=tanh(|f_target−f_rhythm| / σ)".

[0040] Its design aims to quantify the frequency offset reliability between the "target brainwave frequency" and the "beat frequency." It can be applied to subsequent adaptive learning backup metrics and evaluation logs.

[0041] 7. "M_binaural=0.3+0.4*valence_factor".

[0042] Its design purpose is to determine the modulation depth (amplitude difference between the left and right ears) of the binaural beat frequency signal. It can be used as a design reserve depth parameter and for visualization / logging.

[0043] Therefore, in the current publicly available scheme, these 7 parameters are all auxiliary quantitative indicators that "do not drive signals but support weights, logs, or future expansions".

[0044] This invention also provides a personalized brainwave-induced audio generation system for the method, comprising: a music feature analysis module for executing step S1; a four-mechanism parameter calculation module for executing step S2; a multi-mechanism fusion algorithm module for executing step S3; and a signal synthesis and output module for outputting the final brainwave-induced audio. The music feature analysis module includes: a time-domain feature extraction submodule, a frequency-domain feature extraction submodule, a pitch feature extraction submodule, an emotion feature extraction submodule, and a complex feature extraction submodule.

[0045] Using "sleep aid" as the induction task, the target brainwave frequency f_target was set to the typical value of 6Hz in the theta band. The input audio was a 5-minute piano solo in C major, 4 / 4 time, with a tempo of 80 BPM.

[0046] The system first executes step S1: extracting the beat speed (BPM=80), beat intensity (beat_intensity=0.72), and beat stability (beat_stability=0.85) using a conventional beat tracking algorithm; obtaining the fundamental frequency (f_fundamental=261.6Hz) using the autocorrelation method, and taking the first 10 harmonics; confirming music_key=“C” using the tonality estimation module; obtaining valence_factor=0.45 using the emotion classification model; obtaining rhythm_complexity=0.38 and harmonic_richness=0.52 using the rhythm complexity algorithm; and calculating neural_compatibility=0.67 based on the average EEG similarity value of the user's feedback from the last 10 uses.

[0047] Then proceed to step S2.

[0048] Rhythm synchronization mechanism: Based on f_rhythm=BPM / 60=1.33Hz, take n=1~5th beat harmonics, synchronization depth coefficient α=0.2, generate f_sync=6*(1+0.2sin(2π*1.33t)), and then obtain S_rhythm(t)=sin(2πf_synct).

[0049] Harmonic resonance mechanism: Among the 1st to 10th harmonics of the fundamental frequency 261.6Hz, the difference between the 23rd harmonic 6016.8Hz and the target 6Hz is the smallest. Therefore, k_optimal=23, f_resonance=6016.8Hz, resonance intensity R_harmonic=0.98, and S_harmonic(t)=sin(2π*6016.8t) is generated.

[0050] Neural oscillation coupling mechanism: With modulation frequency f_modulation=0.1Hz and coupling strength coefficient β=0.15, we get f_coupling=6+0.15cos(2π*0.1t), coupling coefficient C_neural=tanh(|6-1.33| / 2)=0.94, generating S_coupling(t)=sin(2πf_couplingt).

[0051] Binaural beat frequency mechanism: Check the key_frequency_map, "C" corresponds to f_carrier=256Hz, the correction coefficient correction_factors is calibrated to 1.0, so Δf_binaural=6Hz, f_left=256Hz, f_right=262Hz, the modulation depth M_binaural=0.3+0.4*0.45=0.48, generating S_binaural(t)=sin(2π*256t)-sin(2π*262t).

[0052] Proceed to step S3.

[0053] Weighting: W_rhythm=0.2+0.3*0.38=0.314.

[0054] W_harmonic=0.15+0.25*0.52=0.280.

[0055] W_coupling=0.25+0.2*0.67=0.384.

[0056] W_binaural=0.4-0.1*(0.314+0.280+0.384)=0.302.

[0057] The sum of the four weights is 1.000, which satisfies the normalization requirement.

[0058] Calculate S_total(t) according to the synthesis formula.

[0059] S_total(t)=0.314*S_rhythm(t)+0.280*S_harmonic(t)+0.384*S_coupling(t)+0.302S_binaural(t).

[0060] By stacking samples one by one, a final audio file with 48kHz sampling and 16-bit resolution is obtained. The user plays this audio using ordinary stereo headphones. This specific implementation only provides the detailed calculation process for each parameter in a sleep-aid scenario and the results of one experiment. For other induced tasks, such as focus, relaxation, or analgesia, simply replace f_target, modulation frequency, correction coefficient, and music input with the corresponding values ​​and repeat the above steps; no additional hardware or algorithm modifications are required.

[0061] Preferably, based on the present invention, those skilled in the art can select one or more parameters of a neuroacoustic induction mechanism to participate in the calculation as needed. The specific method is as follows: in the signal synthesis in step S3, the weight corresponding to the parameter of a certain neuroacoustic induction mechanism can be set to 0, and the weights corresponding to the parameters of other neuroacoustic induction mechanisms can be adaptively adjusted so that the parameter of the neuroacoustic induction mechanism does not participate in the calculation of the final audio signal synthesis. Thus, those skilled in the art can select one or more parameters of a neuroacoustic induction mechanism to participate in the final audio signal synthesis calculation of brainwave induced audio according to specific needs.

[0062] Example 1: Induction of a state of focus (classical music scene).

[0063] Musical characteristics input: Track: Bach's Prelude in C major from The Well-Tempered Clavier, Tempo: 72 BPM, Tempo Stability: 0.95, Tonality: C major, Fundamental Frequency: 261.63 Hz, Harmonic Complexity: 0.8, Rhythmic Complexity: 0.3, Energy Value: 0.6, Emotional Value: 0.7.

[0064] Calculation of parameters for the quadruple mechanism: 1. Rhythm synchronization mechanism.

[0065] Main beat frequency: f_rhythm=72 / 60=1.2Hz.

[0066] Beat harmonics: 1.2Hz, 2.4Hz, 3.6Hz, 4.8Hz, 6.0Hz.

[0067] Synchronization strength: S_rhythm=0.95*0.6=0.57.

[0068] Target beta wave frequency selection: 20Hz.

[0069] Beat synchronization modulation: f_sync=20*(1+0.2*sin(2π*1.2*t)).

[0070] 2. Harmonic resonance mechanism.

[0071] Music fundamental frequency: 261.63Hz.

[0072] Find the optimal harmonic match: 20Hz≈261.63Hz / 13.08.

[0073] Choose the 13th harmonic: f_resonance=261.63 / 13=20.13Hz.

[0074] Resonance intensity: R_harmonic=1 / (1+|2020.13|)=0.885.

[0075] 3. Neural oscillation coupling mechanism.

[0076] Modulation frequency: f_modulation=1.2Hz (synchronized with the beat).

[0077] Coupling frequency: f_coupling=20+1.5*cos(2π*1.2*t).

[0078] Coupling coefficient: C_neural=tanh(|201.2| / 5)=0.967.

[0079] 4. Binaural beat frequency effect.

[0080] Carrier frequency: f_carrier = 261.63 Hz.

[0081] Beat frequency difference: Δf_binaural = 20Hz.

[0082] Left ear: 261.63Hz, right ear: 281.63Hz.

[0083] Modulation depth: M_binaural=0.3+0.4*0.7=0.58.

[0084] 5. Weight allocation.

[0085] W_rhythm=0.2+0.3*0.3=0.29 W_harmonic=0.15+0.25*0.8=0.35 W_coupling=0.25+0.2*0.8=0.41 W_binaural=0.4 0.1*(0.29+0.35+0.41)=0.05→0.05(Minimum value limit).

[0086] 6. Final output signal.

[0087] S_focus(t)=0.29*sin(2π*f_sync*t)+0.35*sin(2π*f_resonance*t)+0.41*sin(2π*f_coupling*t)+0.05*[sin(2π*261.63*t)sin(2π*281.63*t)].

[0088] Example 2: Deep relaxation state induction (ambient music scene).

[0089] Musical Input Characteristics: Track: Brian Eno Ambient Music; Tempo: 60 BPM; Tempo Stability: 0.7; Tonality: Am (A minor); Fundamental Frequency: 220 Hz; Harmonic Complexity: 0.3; Rhythmic Complexity: 0.1; Energy Value: 0.3; Emotional Value: 0.4 Calculation of parameters for the quadruple mechanism: 1. Rhythm synchronization mechanism: Main beat frequency: f_rhythm = 60 / 60 = 1.0Hz Target alpha wave frequency: 10Hz Beat synchronization modulation: f_sync = 10 * (1 + 0.15 * sin(2π * 1.0 * t)) 2. Harmonic resonance mechanism: Music fundamental frequency: 220Hz Optimal harmonic matching: 10Hz≈220Hz / 22 Choose the 22nd harmonic: f_resonance = 220 / 22 = 10Hz Perfect resonance: R_harmonic=1.0 3. Neural oscillation coupling mechanism: Modulation frequency: 0.5Hz (slow modulation, to enhance relaxation effect) Coupling frequency: f_coupling=10+0.8*cos(2π*0.5*t) Coupling coefficient: C_neural = tanh(|101.0| / 3) = 0.996 4. Binaural beat frequency effect: Carrier frequency: f_carrier = 220Hz Beat frequency difference: Δf_binaural = 10Hz Modulation depth: M_binaural = 0.3 + 0.4 * 0.4 = 0.46 5. Weighting: W_rhythm=0.2+0.3*0.1=0.23W_harmonic=0.15+0.25*0.3=0.225W_coupling=0.25+0.2*0.9=0.43W_binaural=0.40.1*0.885=0.315.

[0090] Example 3: Creativity-stimulating state induction (jazz music scene).

[0091] Musical feature input: Track: Miles Davis "Kind of Blue" Tempo: 120 BPM, Tempo Stability: 0.8 Tonality: D-Dorian mode, Fundamental frequency: 293.66 Hz Harmonic complexity: 0.9, Rhythmic complexity: 0.7 Energy value: 0.8, Emotional value: 0.75.

[0092] Key points of innovative technologies.

[0093] Composite rhythm synchronization: main beat: 2Hz, syncopated beat: 1.33Hz (triplets).

[0094] Multi-layer rhythm synchronization: f_sync_composite=f_target*[1+α1*sin(2π*2*t)+α2*sin(2π*1.33*t)]. Where, α1=0.2, α2=0.15.

[0095] Dynamic harmonic tracking: Real-time tracking of chord changes and dynamic adjustment of resonant frequencies. f_resonance(t)=chord_fundamental(t) / harmonic_order.

[0096] Cross-frequency coupling: θα cross-coupling (optimal state of creativity).

[0097] Target frequencies: 6Hz (θ wave) + 10Hz (α wave).

[0098] Coupling function: f_cross_coupling = 6*cos(φ1) + 10*cos(φ2). Where φ2 = φ1 + π / 4 (phase difference 45°).

[0099] Example 4: Sleep induction state (natural sound scene).

[0100] Music feature input: Track: Wave sound + gentle piano Tempo: 40 BPM (very slow), Tempo stability: 0.9 Main frequency component: 80 Hz (low-frequency wave sound) Harmonic complexity: 0.1, Rhythmic complexity: 0.05 Energy value: 0.2, Emotional value: 0.3.

[0101] Deep Sleep Optimization Technology: Ultra-low Frequency Synchronization: Target delta wave: 1.5Hz. Respiratory Rhythm Synchronization: 0.25Hz (15 breaths / minute). Composite Synchronization Signal: f_sleep_sync = 1.5 + 0.3 * cos(2π * 0.25 * t). Pink Noise Harmonics: Based on the 1 / f spectral characteristics of natural sound. Harmonic Attenuation: A_n = A O / √n. Sleep optimization spectrum: S_pink(f) = A0 / f^0.5*window_function(f).

[0102] Gradual frequency reduction: Gradually decreasing from 8Hz to 1Hz over 15 minutes.

[0103] The exponential decay function is: f_progressive(t) = 8*exp(t / τ) + 1, where τ = 900s (a 15-minute time constant).

[0104] Example 5: Multi-stage dynamic transition (complete experience process).

[0105] Scenario: A complete 60-minute meditation experience.

[0106] Phase 1 (010 minutes): Preparation for entering a meditative state. Goal: Transition from beta waves (25Hz) to alpha waves (12Hz). Music: Soft New Age music. Main mechanism: Binaural beat frequency + rhythm synchronization. Transition function: f(t) = 2513 * (t / 600).

[0107] Phase 2 (1025 minutes): Shallow meditation Goal: Stabilize alpha waves (10Hz) Music: Tibetan singing bowl music Main mechanism: Harmonic resonance + neural oscillation coupling Resonance frequency: 440Hz / 44=10Hz (perfect match).

[0108] Phase 3 (2545 minutes): Deep Meditation Goal: Theta wave (6Hz) Music: Natural ambient sound Main mechanism: Full mechanism fusion Special technique: Theta-γ coupling (40Hz modulated 6Hz).

[0109] Phase 4 (4555 minutes): Consciousness return goal: from theta wave back to alpha wave. Music: light instrumental music with a gradual recovery: f(t) = 6 + 4*sin²(π*(t2700) / 600).

[0110] Phase 5 (5560 minutes): Awake Integration Goal: Relaxed beta waves (15Hz) Music: Uplifting music Final Stability: 15Hz ± 1Hz

[0111] The above description is merely a specific embodiment of the invention, but the scope of protection of the invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the invention, based on the technical solution and concept of the invention, should be covered within the scope of protection of the invention.

Claims

1. A method for generating personalized brainwave-induced audio, characterized in that, include: Step S1: Extract multidimensional features of the input audio signal. The multidimensional features include: time domain features, frequency domain features, pitch features, emotional features, and complex features. The complex features include at least: rhythmic complexity and harmonic complexity. Step S2: Based on the multidimensional features extracted in step S1, the parameters of four neuroacoustic induction mechanisms are calculated in parallel. The parameters of the neuroacoustic induction mechanisms include: rhythm synchronization mechanism parameters, harmonic resonance mechanism parameters, neural oscillation coupling mechanism parameters, and binaural beat frequency effect parameters. Step S3: Combine the features obtained in step S1 with the parameters of the four neuroacoustic induction mechanisms in step S2, and fuse the corresponding audio signals using a weighting strategy and signal synthesis formula to generate the final brainwave-induced audio. The weight allocation strategies include: The weights of the rhythm synchronization mechanism (W_rhythm) are calculated based on the rhythm complexity (rhythm_complexity), the harmonic resonance mechanism (W_harmonic) is calculated based on the harmonic richness (harmonic_richness), the neural oscillation coupling mechanism (W_coupling) is calculated based on the neural compatibility (neural_compatibility) calculated by the system based on the user's historical brainwave feedback data, and the binaural beat frequency mechanism (W_binaural) is calculated based on the rhythm synchronization mechanism weight (W_rhythm), the harmonic resonance mechanism weight (W_harmonic), and the neural oscillation coupling mechanism weight (W_coupling).

2. The personalized brainwave-induced audio generation method as described in claim 1, characterized in that, The time-domain features include at least: beat speed, beat intensity, and beat stability; The frequency domain features include at least: fundamental frequency and harmonic structure; The pitch characteristics include at least: musical tonality; The emotional characteristics include at least: emotional value; Step S2 further includes: Step S2.1 Calculate the rhythm synchronization mechanism parameters: Calculate the main beat frequency f_rhythm based on the beat speed, calculate the beat synchronization target frequency f_sync based on the set target brainwave frequency f_target and the main beat frequency f_rhythm, and calculate the time domain signal S_rhythm(t) generated by the rhythm synchronization mechanism based on the synchronization target frequency f_sync. Step S2.2 Calculate the harmonic resonance mechanism parameters: Calculate the optimal harmonic match f_resonance with the target brainwave frequency based on the music fundamental frequency f_fundamental and the set target brainwave frequency f_target; calculate the harmonic resonance mechanism to generate the time-domain signal S_harmonic(t) based on the optimal harmonic match f_resonance. Step S2.3 Calculate the parameters of the neural oscillation coupling mechanism: Calculate the neural oscillation coupling frequency f_coupling based on the set target brainwave frequency f_target and the modulation frequency f_modulation set according to the specific induction task, and calculate the time domain signal S_coupling(t) generated by the neural oscillation coupling mechanism based on the neural oscillation coupling frequency f_coupling. Step S2.4 Calculate the binaural beat frequency effect parameters: Calculate the fundamental carrier frequency f_carrier based on the music key and the fundamental frequency lookup table key_frequency_map; calculate the binaural beat frequency difference Δf_binaural based on the set target brainwave frequency f_target and correction coefficients correction_factors; calculate the left ear frequency f_left based on the fundamental carrier frequency f_carrier; calculate the right ear frequency f_right based on the fundamental carrier frequency f_carrier and the binaural beat frequency difference Δf_binaural; and calculate the time-domain signal S_binaural(t) based on the left ear frequency f_left and the right ear frequency f_right. Finally, the time-domain signal S_total(t) is obtained by jointly calculating the time-domain signal generated by the rhythm synchronization mechanism, the harmonic resonance mechanism, the neural oscillation coupling mechanism, and the binaural beat effect mechanism.

3. The personalized brainwave-induced audio generation method as described in claim 1, characterized in that, The specific parameters of the rhythm synchronization mechanism in step S2 include: Main beat frequency: f_rhythm=BPM / 60; BPM represents the tempo of the music, measured in beats per minute, and is obtained through multi-dimensional music feature extraction in step S1. Beat harmonic sequence: f_harm_n = f_rhythm * n; Where n represents the harmonic order of the beat, and takes values ​​of 1, 2, 3, 4, and 5; Calculate beat synchronization strength: S_rhythm=beat_stability*beat_intensity; Where beat_stability represents beat stability and is used to quantify the uniformity of beats, and beat_intensity represents beat intensity and is used to quantify the strength of beats, both of which are obtained through multi-dimensional music feature extraction in step S1; Target frequency for beat synchronization: f_sync=f_target*(1+α*sin(2π*f_rhythm*t)); Where f_target represents the target brainwave frequency, which is set by the specific induction task; α represents the synchronization depth coefficient, α∈[0.1,0.3], and t represents the time variable; The rhythm synchronization mechanism generates time-domain signals: S_rhythm(t)=sin(2π*f_sync*t).

4. The personalized brainwave-induced audio generation method as described in claim 1, characterized in that, The harmonic resonance mechanism parameters in step S2 specifically include: Extracting the fundamental frequency of music from multidimensional music features: f_fundamental; Harmonic sequences: f_harmonic_k=f_fundamental*k; Where k represents the harmonic index, and its value is 1, 2, 3, ..., 10; Optimal harmonic matching with the target brainwave frequency: k_optimal=argmin|f_target-f_fundamental*k|; f_resonance=f_fundamental*k_optimal; Where, k_optimal represents the harmonic index that minimizes |f_target-f_fundamental*k|, f_target represents the target brainwave frequency, which is set by the specific induction task, and f_resonance represents the selected optimal harmonic resonance frequency; Harmonic resonance intensity: R_harmonic=1 / (1+|f_target-f_resonance|); Where R_harmonic represents the harmonic resonance intensity, with a value range of 0-1; Harmonic resonance mechanism generates time-domain signals: S_harmonic(t)=sin(2π*f_resonance*t).

5. The personalized brainwave-induced audio generation method as described in claim 1, characterized in that, The parameters of the neural oscillation coupling mechanism in step S2 specifically include: Neural oscillation coupling frequency: f_coupling=f_target+β*cos(2π*f_modulation*t); Where β represents the coupling strength coefficient, used to control the modulation amplitude; f_target represents the target brainwave frequency, set by the specific induction task; and f_modulation represents the modulation frequency set according to the specific induction task. Define the coupling phase relationship: φ_coupling=2π*f_rhythm*t+φ_offset; Where φ_coupling represents the instantaneous phase of neural oscillation coupling; φ_offset represents the initial phase offset, which is set by the system according to the specific induction task. Coupling coefficient: C_neural=tanh(|f_target-f_rhythm| / σ); Where C_neural represents the neural oscillation coupling coefficient, with a value range of 0-1; σ is the coupling sensitivity parameter, used to determine the steepness of the output curve; Neural oscillatory coupling mechanism generates time-domain signals: S_coupling(t)=sin(2π*f_coupling*t).

6. The personalized brainwave-induced audio generation method as described in claim 1, characterized in that, The binaural beat frequency effect parameters in step S2 specifically include: Calculate the fundamental carrier frequency: f_carrier=key_frequency_map(music_key); Here, key_frequency_map() represents a fundamental frequency lookup table indexed by musical key, where music_key is the musical key; Binaural beat frequency difference: Δf_binaural=f_target*correction_factors; Wherein, correction_factors represents the correction coefficients for individual differences and hardware system, and f_target represents the target brainwave frequency, which is set by the specific induction task; Left ear frequency: f_left = f_carrier; Right ear frequency: f_right=f_carrier+Δf_binaural; Binaural beat modulation depth: M_binaural=0.3+0.4*valence_factor; Where M_binaural represents the binaural beat modulation depth; valence_factor represents the music emotional value factor, which is obtained through multi-dimensional music feature extraction in step S1; Binaural beat frequency effect mechanism generates time-domain signals: S_binaural(t)=sin(2π*f_left*t)-sin(2π*f_right*t).

7. The personalized brainwave-induced audio generation method as described in claim 1, characterized in that, The weight allocation strategy in S3 specifically includes: The weights of the four mechanisms are dynamically assigned based on the music features extracted in step S1 and the target state: W_rhythm=0.2+0.3*rhythm_complexity; W_harmonic=0.15+0.25*harmonic_richness; W_coupling=0.25+0.2*neural_compatibility; W_binaural=0.4-0.1*(W_rhythm+W_harmonic+W_coupling); Wherein, W_rhythm represents the weight of the rhythm synchronization mechanism; rhythm_complexity represents the rhythm complexity, which is obtained through multi-dimensional music feature extraction in step S1; W_harmonic represents the harmonic resonance mechanism weight; harmonic_richness represents the harmonic complexity, which is obtained through multi-dimensional music feature extraction in step S1. W_coupling represents the weight of the neural oscillation coupling mechanism; neural_compatibility represents neural compatibility, which is calculated by the system based on the user's historical brainwave feedback data. W_binaural represents the binaural beat frequency mechanism weights, used to ensure that the sum of the four weights is 1.

8. The personalized brainwave-induced audio generation method as described in claim 1, characterized in that, The signal synthesis formula in S3 specifically includes: S_total(t)=W_rhythm*S_rhythm(t)+W_harmonic*S_harmonic(t)+W_coupling*S_coupling(t)+W_binaural*S_binaural(t); S_total(t) represents the final synthesized audio signal; S_rhythm(t) represents the time-domain signal generated by the rhythm synchronization mechanism; W_rhythm represents the weight of the rhythm synchronization mechanism; S_harmonic(t) represents the time-domain signal generated by the harmonic resonance mechanism; W_harmonic represents the weight of the harmonic resonance mechanism; S_coupling(t) represents the time-domain signal generated by the neural oscillatory coupling mechanism; W_coupling represents the weights of the neural oscillatory coupling mechanism. S_binaural(t) represents the time-domain signal generated by the binaural beat frequency effect mechanism; W_binaural represents the weight of the binaural beat frequency mechanism; The final output signal is S_total(t).

9. A personalized brainwave-induced audio generation system for implementing the method of any one of claims 1-8, characterized in that, include: The music feature analysis module is used to execute step S1; The quadruple mechanism parameter calculation module is used to execute step S2; The multi-mechanism fusion algorithm module is used to execute step S3; The signal synthesis and output module is used to output the final brainwave-induced audio.

10. A personalized brainwave-induced audio generation system according to claim 9, characterized in that, The music feature analysis module includes: a time-domain feature extraction submodule, a frequency-domain feature extraction submodule, a pitch feature extraction submodule, an emotion feature extraction submodule, and a complex feature extraction submodule.

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