Neural regulation and control method integrating median nerve electrical stimulation and acupoint acupuncture and moxibustion

Abstract

The invention relates to the technical field of nerve regulation and control, in particular to a nerve regulation and control method integrating median nerve electrical stimulation and acupoint acupuncture and moxibustion, which comprises the following steps: monitoring and collecting a multi-dimensional nerve response baseline of a target individual in a resting state based on a multi-source physiological signal; identifying a stimulation sensitivity type and an initial reaction delay feature through tentative short-time median nerve electrical stimulation, and outputting an individual nerve response feature vector; according to individual nerve response feature vectors, response delay differences are considered in a combined mode, a coupling stimulation synchronism model is constructed, and an individual nerve regulation scheme is generated; continuous neural feedback signals are collected in real time, feedback prediction parameters are compared to execute response lag recognition and self-stability evaluation, and if response deviation or stability decline is detected, a coupling stimulation synchronism model is called to intervene in self-updating of a body neural regulation scheme, and a dynamic closed-loop self-stability regulation sequence is formed. According to the invention, the problem of unstable curative effect caused by reaction drift, overstimulation or nerve fatigue in the traditional method is solved.

Description

Technical Field

[0001] This invention relates to the field of neuromodulation technology, and in particular to a neuromodulation method that integrates median nerve electrical stimulation and acupuncture. Background Technology

[0002] Neuromodulation technology, as a means of intervening in nervous system activity and regulating physiological states and functional disorders through electrical stimulation and other methods, has been widely researched and applied in recent years in various fields such as anxiety disorders, chronic pain, insomnia, and brain function rehabilitation. Common non-invasive neuromodulation methods mainly include median nerve electrical stimulation and acupuncture intervention. Median nerve electrical stimulation usually involves applying low-frequency electrical stimulation to the wrist or forearm to activate ascending central nervous pathways. It has advantages such as convenient operation and high wearability, and is widely used to enhance cortical excitability, improve cognitive state, or regulate mood. Acupuncture, on the other hand, is based on traditional Chinese medicine theory. By applying electrical stimulation to specific acupoints, it induces local and systemic physiological responses, and has certain effects on the regulation of the autonomic nervous system and the harmonization of visceral functions.

[0003] However, in existing technologies, median nerve electrical stimulation and acupuncture are usually applied separately as independent stimulation pathways, lacking a joint design mechanism based on individual neural response characteristics, making it difficult to achieve a unified central-peripheral synergistic stimulation effect. Furthermore, individuals exhibit significant differences in nervous system sensitivity, pathway delay, and response timing; using fixed stimulation parameters can easily lead to stimulation misalignment, response deviation, or regulatory failure. In addition, traditional methods are mostly based on feedforward intervention, lacking effective feedback evaluation mechanisms and dynamic parameter self-updating capabilities, making it difficult for the regulatory process to adapt to fluctuations in individual states. Summary of the Invention This invention provides a neuromodulation method that integrates median nerve electrical stimulation and acupuncture, a personalized neuromodulation method that integrates median nerve electrical stimulation and acupuncture. It can construct an individual characteristic model based on multi-source neural signals, establish a temporal-intensity synergistic mechanism between dual-pathway stimulation, and achieve dynamic self-stabilizing control by combining feedback signals, thereby improving the accuracy, stability and individual adaptability of neuromodulation.

[0004] A neuromodulation method integrating median nerve electrical stimulation and acupuncture includes the following steps: S1: Based on multi-source physiological signal monitoring, the target individual's multi-dimensional neural response baseline in the resting state is collected, and the stimulus sensitivity type and initial response delay characteristics of the target individual are identified by exploratory short-term median nerve electrical stimulation, and the individual's neural response feature vector is output. S2: Based on the individual neural response feature vector, and considering the difference in response delay between the median nerve pathway and the selected acupoint pathway, a coupled stimulation synchronization model is constructed to generate an individualized neural modulation scheme including a coupling strength factor, a dynamic temporal interval, and a set of feedback prediction parameters; the individualized neural modulation scheme is used to coordinate the synergistic consistency between median nerve electrical stimulation and acupoint acupuncture in terms of time and response. S3: Real-time acquisition of continuous neural feedback signals of individuals during the regulation process; performance of response lag identification and stability assessment based on the feedback prediction parameters in the individualized neural regulation scheme compared with the continuous neural feedback signals; if response deviation or stability decrease is detected, the coupled stimulus synchronization model is invoked to self-update the individualized neural regulation scheme, forming a dynamic closed-loop self-stabilizing regulation sequence.

[0005] Optionally, S11, in a resting state, simultaneously collect the electroencephalogram (EEG) signal, heart rate variability signal, and surface electromyography (EMG) signal of the target individual, and extract the power spectral density of each signal in a preset frequency band as a multidimensional neural response baseline. S12, apply a set of trial short-term electrical stimulations with increasing parameters to the median nerve of the wrist of the target individual, and simultaneously record the changes of the multi-source physiological signals after each stimulation; calculate and output two key features based on the changes, namely the stimulation sensitivity type index and the initial response delay feature.

[0006] Optionally, the stimulus sensitivity type index is determined based on the ratio of the amplitude of the suppression of the power in the alpha band of the electroencephalogram to the slope of the amplitude increase of the surface electromyography evoked response; the initial response delay feature is defined as the time interval from the application of stimulus to the first appearance of a response in the γ band of the electroencephalogram or the high-frequency band of heart rate variability, i.e., the response amplitude exceeds the standard deviation of the resting baseline.

[0007] Optionally, S1 further includes standardizing and vectorizing the multidimensional neural response baseline, the stimulus sensitivity type index, and the initial response delay feature to form an individual neural response feature vector in the individual neural response model.

[0008] Optionally, S2 specifically includes: S21, from the individual neural response feature vector, the initial response delay features of the median nerve pathway and the initial response delay features of the selected acupoint pathway are parsed out, and the difference between the two is calculated as the basic pathway delay difference; with the basic pathway delay difference as input, combined with the preset stimulation frequency, the core regulation parameters, including dynamic temporal interval and coupling strength factor, are calculated and generated through the synchronization optimization function. S22, Based on the multi-dimensional neural response baseline, establish an expected collaborative response curve model for this modulation; extract key feature points from the expected collaborative response curve model to form a feedback prediction parameter set for this modulation; the key feature points include peak time and response start time; S23, the dynamic temporal interval, the coupling strength factor, and the feedback prediction parameter set are encapsulated to generate the individualized neural modulation scheme.

[0009] Optionally, the value of the dynamic timing interval is a function of the delay difference of the basic pathway, used to enable the slow pathway stimulation to be emitted in advance, ensuring that the central nervous system responses induced by the two stimuli are aligned in time; the value of the coupling strength factor is a function of the stimulus sensitivity type index in the individual neural response feature vector, used to dynamically adjust the ratio of the current intensity of acupuncture stimulation to median nerve electrical stimulation.

[0010] Optionally, in each stimulation cycle, the offset of the initiation time of acupuncture stimulation relative to median nerve electrical stimulation in the individualized neuromodulation scheme is determined by the dynamic temporal interval, and the intensity ratio of acupuncture stimulation relative to median nerve electrical stimulation is determined by the coupling strength factor.

[0011] Optionally, S3 specifically includes: S31, while executing the individualized neuromodulation scheme, the electroencephalogram (EEG) signal of the target individual is collected simultaneously; from the EEG signal, the actual collaborative response feature value and occurrence time corresponding to the key feature points in the feedback prediction parameter set are extracted in real time. S32, compare the actual collaborative response characteristic value and occurrence time with the expected amplitude and expected occurrence time in the feedback prediction parameter set, and calculate two evaluation indicators, namely the response lag index and the stability deviation; if either the response lag index or the stability deviation exceeds the corresponding preset threshold, it is determined that a response deviation or a decrease in stability has been detected. S33, when it is determined that adjustment is needed, the current actual collaborative response characteristic value, response lag index and stability deviation are used as inputs and fed back to the coupled stimulus synchronization model; the coupled stimulus synchronization model is called to recalculate and output the updated dynamic time interval and coupling strength factor, and a new generation of individualized neuromodulation schemes are generated accordingly.

[0012] Optionally, the response lag index is calculated by normalization based on the difference between the actual occurrence time and the expected occurrence time, combined with the stimulus cycle; the stability deviation is calculated based on the ratio of the fluctuation variance of the actual coordinated response characteristic value to the expected amplitude.

[0013] Optionally, S3 further includes repeating steps S31 to S33 to form a dynamic closed-loop self-stabilizing regulation sequence of continuous perception, evaluation, decision-making and adjustment during a single treatment, so that the synergistic stimulation effect is maintained within the preset target range.

[0014] The beneficial effects of this invention are: 1. This invention introduces a response delay difference modeling mechanism based on the median nerve pathway and acupoint pathway. By analyzing the individual's multidimensional neural response baseline and the initial response delay characteristics of the dual pathways, a coupled stimulus synchronicity model is constructed, dynamically generating key parameters such as temporal intervals and intensity ratios, enabling the two types of stimuli to achieve synergistic consistency at both temporal and functional levels. This mechanism addresses the problems of isolated implementation of dual-stimulation modes and inconsistent regulatory targets in existing technologies, improving the targetedness and physiological adaptability of the intervention.

[0015] 2. This invention proposes a neural synergistic response modeling function. Before modulation, it predicts the target neural response trajectory based on the individual's neural baseline state and modulation parameters, and evaluates the real-time feedback signal cycle by cycle using two indicators: response hysteresis index and stability deviation. When the response deviates or fluctuates beyond a threshold, the system can automatically update the modulation parameters based on the feedback data, forming a dynamic closed-loop self-stabilizing modulation sequence. This invention solves the problem of unstable therapeutic effects caused by response drift, overstimulation, or neural fatigue in traditional methods.

[0016] 3. This invention constructs an individual neural response feature vector by fusing multi-source neural signals such as EEG, heart rate variability, and electromyography. This vector serves as the basis for the entire process of parameter generation and predictive modeling, enabling accurate identification and classification adaptation for individuals with different sensitivity types. The proposed coupling strength factor and dynamic temporal interval parameters not only reflect individual physiological differences but also serve as adjustment handles for regulatory schemes, enabling the deployment of highly generalized regulatory strategies across populations and disease conditions. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the logic framework of an embodiment of the present invention. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. For some well-known technologies, those skilled in the art may also use other alternative methods to implement the invention. Moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0020] like Figures 1-2 As shown, a neuromodulation method integrating median nerve electrical stimulation and acupuncture includes the following steps: S1: Based on multi-source physiological signal monitoring, the target individual's multi-dimensional neural response baseline in the resting state is collected, and the stimulus sensitivity type and initial response delay characteristics of the target individual are identified by exploratory short-term median nerve electrical stimulation, and the individual's neural response feature vector is output. S1 specifically includes: S11: Under the resting state of the target individual, the following three types of physiological signals are collected simultaneously: Electroencephalogram (EEG) signals: Acquire scalp electrode signals and extract their power spectral density in the alpha band, i.e., 8-13 Hz; Heart rate variability (HRV): The RR interval sequence is obtained through chest leads or a finger clip ECG device, and its power in the high-frequency range, i.e., HF, 0.15-0.4Hz, is extracted. Surface electromyography (sEMG): Electromyographic signals from the forearm flexor muscles were acquired and their average power spectral density was calculated.

[0021] Power spectrum analysis was performed on the above signals, and the baseline representation of the multidimensional neural response was obtained as follows: ; in, Represents the α-band power spectral density. HRV indicates high-frequency band power. This represents the average power spectral density of surface electromyography.

[0022] S12: Trial Stimulation and Feature Extraction. A series of progressively increasing stimuli are applied to the median nerve of the wrist in the target individual. Let the stimulus intensity sequence be: ; After each stimulus, the changes in physiological signals before and after the stimulus were recorded. : ; ; in, Indicates the first The stimulation intensity parameters of the initial exploratory electrical stimulation, such as voltage, current, or pulse width, This represents the time-domain amplitude response of the electromyographic signal. Indicates the first The average amplitude of the electromyographic signal before stimulation. Indicates the first After stimulation, the average amplitude of the electromyographic signal, Indicates the first Before stimulation, the power spectral density of the EEG signal in the alpha band, i.e., within 8–13 Hz, This represents the power spectral density within the alpha band of the electroencephalogram (EEG) signal.

[0023] a) The Stimulus Sensitivity Type Index (STI) is expressed as: ; This expression represents a type of sensitivity to median nerve stimulation used to assess an individual's sensitivity. It compares the central inhibitory response in EEG signals with the peripheral excitatory response in EMG signals to form a comprehensive sensitivity discrimination index. This index reflects stimulus response characteristics in two aspects: first, the degree of EEG inhibition. When electrical stimulation is applied, the alpha wave in EEG, which is usually associated with relaxation and resting states, is inhibited. The greater the inhibition amplitude, the more sensitive the brain is to the stimulus, indicating active central pathways. Second, the rate of change in EMG response. Increased muscle activity is manifested as an increase in the amplitude of EMG signals; the faster the change, the more sensitive the peripheral nerves and muscle pathways are. This index calculates the ratio between the amplitude of EEG inhibition and the slope of the EMG response increase. This ratio can be used to distinguish the following types: a large ratio indicates a significant EEG inhibitory response and a relatively gentle muscle excitatory response, belonging to the centrally sensitive type; a small ratio indicates a rapid increase in EMG response and a less obvious EEG inhibition, belonging to the peripherally sensitive type; a moderate ratio indicates a mixed sensitive type or a low-sensitivity type. The slope of the change in electromyographic response represents the derivative of the amplitude with respect to stimulus intensity, i.e., the trend of change; the molecule reflects the electroencephalogram (EEG). The degree of inhibition, with the denominator reflecting the slope of the electromyographic response to changes in stimulus intensity.

[0024] b) The initial response delay characteristic (IRL) is expressed as: ; This expression defines the time delay at which an individual first develops a neural response after receiving median nerve stimulation; this is called the initial response delay characteristic. The individual's neural response is continuously monitored from the moment the electrical stimulation is applied. Once the amplitude of a certain characteristic indicator in the EEG of interest, such as the gamma band or heart rate variability (HRV) signal, exceeds its normal range of fluctuation at rest (i.e., more than twice the standard deviation), a valid response is considered to have occurred at that moment. The initial response delay is the time interval between the occurrence of this valid response and the moment of stimulation. Among multiple monitoring channels, the earliest time point showing a significant response is selected as the representative delay value for that individual. The smaller this value, the faster the individual's nervous system responds to the stimulus; the larger the value, the more likely there are problems such as slow response, delayed pathway conduction, or insufficient excitability. For the time point after stimulation, The duration of stimulus application, This indicates the signal increment in the EEG gamma band (30-50Hz) or the HRV high-frequency band. This represents the standard deviation of the signal at rest.

[0025] S13: Concatenate the above three types of indicators into an individual neural response feature vector: ; Where, normalize represents the normalization function. For vector concatenation operations, this normalization function employs linear normalization and min-max normalization. Each feature value is scaled according to its minimum and maximum values ​​within the historical range of the samples, mapping it to the 0,1 interval. For each feature dimension in the vector, such as EEG power, HRV power, EMG power, STI, IRL, etc., the maximum and minimum values ​​of that dimension are obtained from historical samples. The minimum value is subtracted from the current individual's original value, and then divided by the difference between the maximum and minimum values. This results in a standardized vector where all features are within the 0-1 range, ensuring comparability and equal weighting across different indicators, and preventing any single dimension from dominating the overall result.

[0026] S2: Based on the individual neural response feature vector, and considering the difference in response delay between the median nerve pathway and the selected acupoint pathway, a coupled stimulation synchronization model is constructed to generate an individualized neural modulation scheme including a coupling strength factor, a dynamic temporal interval, and a set of feedback prediction parameters; the individualized neural modulation scheme is used to coordinate the synergistic consistency between median nerve electrical stimulation and acupoint acupuncture in terms of time and response. S2 specifically includes: S21, from the individual neural response feature vector Extract: Initial response delay characteristics of the median nerve pathway: denoted as ; Initial response delay characteristics of the selected acupoint pathway: denoted as .

[0027] The difference between the two is defined as the basic path delay difference: ; in, Indicates the difference in basic path delay. This indicates the time delay in the first appearance of a neural response after median nerve electrical stimulation. This indicates the time delay in the first appearance of a nerve response after acupuncture stimulation of acupoints.

[0028] Using the aforementioned basic pathway delay difference as input, and combining it with a preset stimulation frequency, core regulation parameters are calculated and generated through a synchronicity optimization function, including: S211, Dynamic Timing Interval: Based on the basic path delay difference Combined with stimulation frequency Optimize function through synchronization The calculation yielded: ; in, Dynamic timing intervals represent the amount of time that the stimulation of slower pathways should be applied earlier in each stimulation cycle. This indicates the base frequency parameter used for modulating stimulation, ranging from 1Hz to 20Hz. The low-frequency range (1–5) is mostly used to stimulate chronic regulatory responses and is suitable for individuals aiming for analgesia, relaxation, or parasympathetic activation. The mid-frequency range (6–10) is used to enhance neural excitability and response synchronicity. Frequencies above 10 can be used for short-term enhanced stimulation effects, but should be avoided to prevent neural fatigue or over-excitation. This represents the synchronization optimization function, which outputs the optimal offset value for time alignment. The synchronization optimization function calculates an optimal temporal offset based on the response delay difference between two neural pathways, such as the median nerve pathway and the acupoint pathway, as well as the stimulation frequency used. This ensures that the central responses induced by the two stimulation methods overlap as much as possible in time, thereby enhancing the synergistic regulatory effect. The synchronization optimization function operates according to the following strategy: If the difference in response delay between the two pathways is small, the time interval of the function output can be set to 0 or a minimum value, allowing the two stimuli to be emitted simultaneously. If the delay difference is moderate, the function will advance the stimulus from the slower-delayed pathway by a certain amount of time, the advance amount being proportional to... If the delay difference is close to or exceeds the effective window width of the stimulus period, the function needs to perform intra-cycle folding on the lead amount to avoid stimulus overlap and confusion. The function also incorporates stimulus frequency limitations: for high-frequency stimuli such as... However, the advance time should not exceed a certain proportion of the cycle length, such as half, to prevent conflicts in the next cycle.

[0029] S212, Coupling Strength Factor: Stimulus sensitivity type index from individual neural response feature vector It is decided that the coupling strength factor is defined as follows: ; in, The coupling strength factor represents the ratio between the intensity of the acupuncture stimulation current at the acupoint and the intensity of the median nerve electrical stimulation current. Indicators representing the type of stimulus sensitivity. This represents the coupling mapping function, which adjusts the stimulation weights of acupoints based on individual sensitivity types. The coupling mapping function is a function used to dynamically allocate the proportion of stimulation intensity, and it outputs a coupling strength factor based on the individual's Stimulus Sensitivity Type Index (STI). This factor determines the ratio of the intensity of acupuncture stimulation at acupoints to the intensity of median nerve electrical stimulation during neural regulation, thereby achieving functional synergy between the two stimulation methods. STI is a discriminant index for the direction of an individual's stimulus response, indicating whether they are more sensitive to central stimulation (brain electrical inhibition) or peripheral stimulation (myomyographic excitation). The intensity of acupuncture intervention at acupoints will be determined based on the numerical range of this index. Essentially, Ψ(STI) constructs a personalized coupling strategy mapping relationship, addressing the discrepancy between individuals who are insensitive to acupuncture but respond well to central stimulation, or vice versa. When the STI is large, indicating that the individual exhibits central sensitivity, it suggests that the acupoint pathway has a weaker influence on that individual, and the coupling strength factor output by the function... A lower STI indicates that acupuncture stimulation at the acupoints can be appropriately reduced; a lower STI indicates that the individual exhibits peripheral sensitivity, suggesting that the acupoint pathway is the primary response path, and the function output... A higher STI indicates that the acupuncture stimulation current should be increased. When the STI is in the middle, indicating moderate sensitivity or a mixed dual-pathway response, the function output... Maintain a moderate value to keep the intensity of the two stimuli balanced.

[0030] S22, Feedback Prediction Parameter Modeling: S221, based on multidimensional neural response baseline Construct a synergistic stimulus-response model: ; in, This represents the predicted neural response curve. This represents a neural collaborative response modeling function. The inputs include: Multidimensional Neural Response Baseline : Reflects an individual's current resting state of brain activity, autonomic nervous activity, and electromyographic state, and is a basic representation of an individual's reaction ability; Dynamic timing interval : Indicates the time difference between median nerve stimulation and acupuncture at acupoints; it is a temporal parameter for the synergistic regulation of dual pathways. Coupling strength factor This represents the intensity ratio of acupuncture stimulation at acupoints relative to electrical stimulation of the median nerve, and is a weighted average of the synergistic effects between pathways. The value is... Acupuncture stimulation below 0.3 may not reach the effective threshold and fail to elicit a response, while stimulation intensity above 2.0 may exceed the safe current range, especially in low-impedance acupoint areas, posing a risk of burns or overactivation. This is typically used as the default initial value when the sensitive type is unclear or during the first adjustment; in actual systems, It can be dynamically generated through the function Ψ(STI) and adjusted in combination with individual stimulus sensitivity; The function output is In other words, after simulating the stimulation process, the expected response curves of various neural signals, such as EEG, HRV, and sEMG, within a certain time range can include the following predictive dimensions: the degree and time trend of gamma wave power enhancement in the EEG, the trend of HRV high-frequency band changes, the short-term activation trend of surface electromyography, and key feature points in the expected co-response curve model, namely peak time and response start time, which will be extracted as the feedback prediction parameter set. Components of.

[0031] S222, from Extracting the feedback prediction parameter set: ; in, Represents the set of feedback prediction parameters. Indicates the time when the predicted response peak or feature point occurs. This represents the predicted response magnitude at the corresponding time point. This indicates the number of feedback feature points.

[0032] S23 integrates three key control components: ; in, This indicates a personalized neuromodulation protocol. This indicates the offset of the initiation time of acupuncture stimulation at acupoints relative to the initiation time of median nerve electrical stimulation. This indicates the ratio of the intensities of two stimuli within the same period. This indicates that it provides feedback and prediction for subsequent closed-loop control. This control scheme will be used in each round of neural stimulation to precisely control the timing and intensity of dual-pathway stimulation, ensuring coordinated response and providing an expected reference benchmark for closed-loop updates.

[0033] S3: Real-time acquisition of continuous neural feedback signals of individuals during the regulation process; performance of response lag identification and stability assessment based on the feedback prediction parameters in the individualized neural regulation scheme compared with the continuous neural feedback signals; if response deviation or stability decrease is detected, the coupled stimulus synchronization model is invoked to self-update the individualized neural regulation scheme, forming a dynamic closed-loop self-stabilizing regulation sequence.

[0034] S3 specifically includes: S31, executing the current round of individualized neuromodulation protocol. During the process, the electroencephalogram (EEG) signals of the target individual are collected in real time. And for each key feature point in the feedback prediction parameter set Extract the following two types of actual response information: Actual response time: Actual response characteristic amplitude: The actual response feature set is constituted as follows: ; S32, Predicting feature points With actual feature points Compare and calculate the following two types of evaluation indicators: S321, Response Lag Index: ; This formula indicates that in rhythmic neural modulation, the consistency of response timing is a key factor in determining the stability of the modulation effect. By normalizing the response time difference, it is possible to effectively compare the response lag under different individuals, different periods, and different parameters, and then use this to drive the adaptive adjustment of modulation parameters to achieve closed-loop optimization. The response hysteresis exponent is represented by the k-th feature point. This indicates the length of the current regulatory stimulus cycle. Indicates the actual response time. This indicates the predicted response time. (Molecular part) This represents the time difference between the actual and expected occurrence times of a specific characteristic point, such as the peak time of a gamma wave in an EEG. It reflects whether the response is delayed or advanced. The denominator is... The stimulation period is the fixed time interval between two stimuli. The ratio represents the proportion of this time difference in the complete stimulation period. The larger the value, the more delayed or deviating the response is from the prediction.

[0035] S322, Stability Deviation: ; in, This represents the stability deviation of the k-th feature point. This represents the short-term variance of the actual response within a time window, reflecting whether it exhibits high-frequency instability or fluctuations in response intensity. This represents the predicted response amplitude, indicating the ideal response strength that the system is expected to achieve based on the current control parameters.

[0036] If there exists any , making The entire ratio represents the degree of deviation of the actual fluctuation intensity from the expected amplitude. The larger the value, the more unstable the response or the more serious the deviation from the target.

[0037] This is then determined to be a response deviation or a decrease in stability, triggering the self-update mechanism. Among these, This represents the empirically set value for the response lag threshold, indicating the allowable response time deviation to not exceed 10% to 30% of one stimulus cycle. 0.2 is a commonly used empirical value, applicable to most neural modulation tasks that require central-peripheral coordination. This indicates a stability deviation threshold, which is recommended to be set in the range of 0.1 to 0.25, meaning the actual response variance should not exceed 10 to 25% of the predicted amplitude. 0.15 is a commonly used setting that balances response sensitivity and fault tolerance, making it suitable for assessing the stability of fluctuations in neural states.

[0038] S33, if the update condition is triggered, the following content will be fed back as input to the coupled stimulus synchronicity model: ; Call the model to regenerate a new set of updated parameters: ; Based on this, a new personalized neuromodulation protocol can be developed: ; in, This is the set of feedback prediction parameters reconstructed based on the updated parameters.

[0039] S34 iterates through S31 to S33 at a fixed period, resulting in the following sequence: ; This sequence is a dynamic closed-loop self-stabilizing regulation sequence that continuously senses the individual's neural state throughout the treatment period, dynamically assesses the regulation effect, and optimizes stimulation parameters in real time, so that the dual-pathway synergistic regulation effect is maintained within the preset target range.

[0040] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0041] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A neuromodulation method integrating median nerve electrical stimulation and acupuncture, characterized in that, Includes the following steps: S1: Based on multi-source physiological signal monitoring, the target individual's multi-dimensional neural response baseline in the resting state is collected, and the stimulus sensitivity type and initial response delay characteristics of the target individual are identified by exploratory short-term median nerve electrical stimulation, and the individual's neural response feature vector is output. S2: Based on the individual neural response feature vector, and considering the difference in response delay between the median nerve pathway and the selected acupoint pathway, a coupled stimulation synchronization model is constructed to generate an individualized neural modulation scheme including a coupling strength factor, a dynamic temporal interval, and a set of feedback prediction parameters; the individualized neural modulation scheme is used to coordinate the synergistic consistency between median nerve electrical stimulation and acupoint acupuncture in terms of time and response. S3: Real-time acquisition of continuous neural feedback signals of individuals during the regulation process; performance of response lag identification and stability assessment based on the feedback prediction parameters in the individualized neural regulation scheme compared with the continuous neural feedback signals; if response deviation or stability decrease is detected, the coupled stimulus synchronization model is invoked to self-update the individualized neural regulation scheme, forming a dynamic closed-loop self-stabilizing regulation sequence.

2. The neuromodulation method integrating median nerve electrical stimulation and acupuncture as described in claim 1, characterized in that, S2 specifically includes: S11, under resting state, simultaneously collect EEG signals, heart rate variability signals and surface electromyography signals of the target individual, and extract the power spectral density of each signal in the preset frequency band as the baseline of multidimensional neural response. S12, apply a set of trial short-term electrical stimulations with increasing parameters to the median nerve of the wrist of the target individual, and simultaneously record the changes of the multi-source physiological signals after each stimulation; calculate and output two key features based on the changes, namely the stimulation sensitivity type index and the initial response delay feature.

3. The neuromodulation method integrating median nerve electrical stimulation and acupuncture as described in claim 2, characterized in that, The stimulus sensitivity type index is determined based on the ratio of the amplitude of the suppression of the alpha band power in the EEG to the slope of the amplitude increase of the surface electromyography evoked response; the initial response delay feature is defined as the time interval from the application of stimulus to the first appearance of a response in the γ band of the EEG or the high-frequency band of heart rate variability, i.e., the response amplitude exceeds the standard deviation of the resting baseline.

4. The neuromodulation method integrating median nerve electrical stimulation and acupuncture as described in claim 2, characterized in that, S1 further includes standardizing and vectorizing the multidimensional neural response baseline, the stimulus sensitivity type index, and the initial response delay feature to form an individual neural response feature vector in the individual neural response model.

5. A neuromodulation method integrating median nerve electrical stimulation and acupuncture as described in claim 2, characterized in that, S2 specifically includes: S21, from the individual neural response feature vector, the initial response delay features of the median nerve pathway and the initial response delay features of the selected acupoint pathway are parsed out, and the difference between the two is calculated as the basic pathway delay difference; with the basic pathway delay difference as input, combined with the preset stimulation frequency, the core regulation parameters, including dynamic temporal interval and coupling strength factor, are calculated and generated through the synchronization optimization function. S22, Based on the multi-dimensional neural response baseline, establish an expected collaborative response curve model for this modulation; extract key feature points from the expected collaborative response curve model to form a feedback prediction parameter set for this modulation; the key feature points include peak time and response start time; S23, the dynamic temporal interval, the coupling strength factor, and the feedback prediction parameter set are encapsulated to generate the individualized neural modulation scheme.

6. A neuromodulation method integrating median nerve electrical stimulation and acupuncture as described in claim 5, characterized in that, The value of the dynamic timing interval is a function of the delay difference of the basic pathway, which is used to advance the stimulation of the slow pathway and ensure that the central nervous system responses induced by the two stimuli are aligned in time; the value of the coupling strength factor is a function of the stimulation sensitivity type index in the individual neural response feature vector, which is used to dynamically adjust the ratio of the current intensity of acupuncture stimulation to median nerve electrical stimulation.

7. A neuromodulation method integrating median nerve electrical stimulation and acupuncture as described in claim 5, characterized in that, In each stimulation cycle, the offset of the initiation time of acupuncture stimulation relative to median nerve electrical stimulation in the individualized neuromodulation scheme is determined by the dynamic temporal interval, and the intensity ratio of acupuncture stimulation relative to median nerve electrical stimulation is determined by the coupling strength factor.

8. A neuromodulation method integrating median nerve electrical stimulation and acupuncture as described in claim 5, characterized in that, S3 specifically includes: S31, while executing the individualized neuromodulation scheme, the electroencephalogram (EEG) signal of the target individual is collected simultaneously; from the EEG signal, the actual collaborative response feature value and occurrence time corresponding to the key feature points in the feedback prediction parameter set are extracted in real time. S32, compare the actual collaborative response characteristic value and occurrence time with the expected amplitude and expected occurrence time in the feedback prediction parameter set, and calculate two evaluation indicators, namely the response lag index and the stability deviation; if either the response lag index or the stability deviation exceeds the corresponding preset threshold, it is determined that a response deviation or a decrease in stability has been detected. S33, when it is determined that adjustment is needed, the current actual collaborative response characteristic value, response lag index and stability deviation are used as inputs and fed back to the coupled stimulus synchronization model; the coupled stimulus synchronization model is called to recalculate and output the updated dynamic time interval and coupling strength factor, and a new generation of individualized neuromodulation schemes are generated accordingly.

9. A neuromodulation method integrating median nerve electrical stimulation and acupuncture as described in claim 8, characterized in that, The response lag index is calculated by normalizing the difference between the actual occurrence time and the expected occurrence time, combined with the stimulus cycle; the stability deviation is calculated by the ratio of the fluctuation variance of the actual coordinated response characteristic value to the expected amplitude.

10. A neuromodulation method integrating median nerve electrical stimulation and acupuncture as described in claim 8, characterized in that, S3 further includes repeating steps S31 to S33 to form a dynamic closed-loop self-stabilizing regulation sequence of continuous perception, evaluation, decision-making and adjustment during a single treatment, so that the synergistic stimulation effect is maintained within the preset target range.

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