Inner ear brain electrical closed-loop neuroelectric regulation method and system targeting hypothalamus

Abstract

This invention relates to the field of closed-loop neuroelectromodulation technology, specifically disclosing a method and system for inner ear EEG closed-loop neuroelectromodulation targeting the hypothalamus. The method includes the following steps: acquiring multimodal physiological signals, extracting individual hypothalamic neural response fingerprints, and generating an initial personalized modulation model containing a hierarchical long short-term memory neural network; simultaneously acquiring inner ear EEG signals and the multimodal physiological signals, and extracting pure hypothalamic projection area EEG signals through a hardware-level stimulation artifact suppression circuit. In practical applications, the method provided by this invention, by extracting individual-specific neural response fingerprints and combining them with a hardware-level stimulation artifact suppression circuit, reduces the impact of individual physiological differences and physical interference from electrical stimulation on signal acquisition, thereby improving the objectivity of calculating the individual's relative hypothalamic functional comprehensive index.

Description

Technical Field

[0001] This invention relates to the field of closed-loop neuroelectric modulation technology, specifically to a method and system for inner ear EEG closed-loop neuroelectric modulation targeting the hypothalamus. Background Technology

[0002] Existing neuromodulation protocols typically assess a user's physiological state by collecting routine electroencephalogram (EEG) signals or physical characteristics, and then intervene and regulate the nervous system based on pre-defined algorithm models and electrical or acoustic stimulation parameters.

[0003] In practical long-term wearable applications, due to differences in the baseline thresholds of hypothalamic neural responses among different users, using a uniform preset standard for state assessment can easily lead to a mismatch between the control parameters and individual needs. Furthermore, during simultaneous application of electrical stimulation and acquisition of EEG signals, stimulation pulse artifacts generated by the hardware port can directly interfere with the EEG signal acquisition process, resulting in distortion of the EEG data input to the assessment model.

[0004] In addition, existing models often lack a continuous update mechanism for long-term physiological changes in individuals, making it difficult to adaptively adjust based on the neural response feedback of users at multiple time scales. This leads to a decrease in the accuracy of parameter matching and the reliability of equipment operation during long-term interventions. Summary of the Invention

[0005] This invention aims to at least partially address one of the technical problems in related technologies. Therefore, the objective of this invention is to propose a closed-loop neuroelectrophysiological modulation method and system targeting the hypothalamus to maintain the adaptability of neuromodulation operations in long-term wearable applications.

[0006] To achieve the above objectives, a first aspect of the present invention proposes a method for inner ear EEG closed-loop neuroelectrophysiological modulation targeting the hypothalamus, comprising:

[0007] Collect multimodal physiological signals, extract individual hypothalamic neural response fingerprints, and generate an initial personalized regulation model containing a hierarchical long short-term memory neural network;

[0008] The inner ear EEG signals and the aforementioned multimodal physiological signals were acquired simultaneously, and pure hypothalamic projection area EEG signals were extracted through a hardware-level stimulation artifact suppression circuit.

[0009] Based on the individual hypothalamic neural response fingerprint, the pure hypothalamic projection area EEG signal and the multimodal physiological signal are normalized to calculate the individual relative hypothalamic functional comprehensive index.

[0010] The individual's relative hypothalamic function index is input into the hierarchical long short-term memory neural network to predict the trend of state change and the probability of risk.

[0011] Based on the current individual relative hypothalamic functional comprehensive index, the state change trend, and the individual hypothalamic neural response fingerprint, a multimodal co-stimulation parameter matrix containing independent parameters of multiple auricular acupoints is generated.

[0012] Closed-loop targeted modulation is achieved by synchronously outputting electrical and acoustic stimulation according to the multimodal synergistic stimulation parameter matrix.

[0013] The neural response data collected after intervention are used to perform online self-calibration to update the individual hypothalamic neural response fingerprint, and the gradients of the model parameters are updated through a federated learning framework to update the hierarchical long short-term memory neural network.

[0014] To achieve the above objectives, a second aspect of the present invention provides an inner ear EEG closed-loop neuroelectrophysiological modulation system targeting the hypothalamus, comprising:

[0015] The initial model generation module is used to collect multimodal physiological signals, extract individual hypothalamic neural response fingerprints, and generate an initial personalized regulation model containing a hierarchical long short-term memory neural network.

[0016] The EEG extraction module is used to simultaneously acquire inner ear EEG signals and the multimodal physiological signals, and extract pure hypothalamic projection area EEG signals through a hardware-level stimulation artifact suppression circuit.

[0017] The index calculation module is used to normalize the pure hypothalamic projection area EEG signal and the multimodal physiological signal based on the individual's hypothalamic neural response fingerprint, and calculate the individual's relative hypothalamic functional comprehensive index.

[0018] The state prediction module is used to input the individual's relative hypothalamic functional comprehensive index into the hierarchical long short-term memory neural network to predict the state change trend and risk probability.

[0019] The parameter matrix generation module is used to generate a multimodal co-stimulation parameter matrix containing independent parameters of multiple auricular acupoints based on the current relative hypothalamic functional comprehensive index of the individual, the trend of state change, and the individual's hypothalamic neural response fingerprint.

[0020] The output control module is used to synchronously output electrical and acoustic stimulation according to the multimodal co-stimulation parameter matrix for closed-loop targeted control.

[0021] The fingerprint and model update module is used to collect neural response data after intervention, perform online self-calibration to update the individual hypothalamic neural response fingerprint, and aggregate model parameters to update gradients through a federated learning framework to update the hierarchical long short-term memory neural network.

[0022] To achieve the above objectives, a third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein when the computer program is executed by the processor, it implements the above-described method for inner ear EEG closed-loop neuroelectrophysiological modulation targeting the hypothalamus.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0024] The method provided by this invention, in practical operating scenarios, reduces the impact of individual physiological differences and physical interference from electrical stimulation on signal acquisition by extracting individual-specific neural response fingerprints and combining them with hardware-level stimulation artifact suppression circuits, thereby improving the objectivity of calculating the individual relative hypothalamic functional comprehensive index. Based on this data, a hierarchical long short-term memory neural network is used to predict state change trends and generate a multimodal co-stimulation parameter matrix, enabling the synchronously output electrical and acoustic stimulation parameters to match the user's immediate physiological changes. Simultaneously, by updating individual neural response fingerprints online through self-calibration and by aggregating model parameter update gradients using a federated learning framework, the system can continuously iterate the modulation model based on long-term actual intervention feedback, maintaining the adaptability of neural modulation in long-term wearable applications. Attached Figure Description

[0025] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein:

[0026] Figure 1 This is a flowchart illustrating the inner ear EEG closed-loop neuroelectro-modulation method targeting the hypothalamus provided by the present invention.

[0027] Figure 2 This is a nonlinear surface plot of hypothalamic neural response based on multi-frequency electrical stimulation in the inner ear EEG closed-loop neuroelectromodulation method targeting the hypothalamus provided by the present invention.

[0028] Figure 3 This is a time-series comparison of high-fidelity EEG signal extraction and artifact cancellation during the stimulation silence period in the inner ear EEG closed-loop neuroelectromodulation method targeting the hypothalamus provided by this invention.

[0029] Figure 4 This invention provides a method for closed-loop neuroelectrophysiological modulation of the inner ear targeting the hypothalamus, which includes the convergence curve and state prediction trajectory of the loss function of the hierarchical long short-term memory neural network.

[0030] Figure 5 This is a heatmap of the multidimensional similarity evolution matrix under long-term neural intervention in the inner ear EEG closed-loop neuroelectromodulation method targeting the hypothalamus provided by this invention.

[0031] Figure 6 This is a dynamic complex impedance separation and compensatory current multi-target Pareto front diagram in the inner ear EEG closed-loop neuroelectric modulation method targeting the hypothalamus provided by the present invention.

[0032] Figure 7 This is a time series diagram of biological rhythm phase angle polar coordinate mapping and parameter gating exponential modulation in the inner ear EEG closed-loop neuroelectromodulation method targeting the hypothalamus provided by the present invention;

[0033] Figure 8 This is a schematic diagram illustrating the implementation of the inner ear EEG closed-loop neuroelectromodulation system targeting the hypothalamus provided by the present invention.

[0034] Figure 9 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0035] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0036] The following describes, with reference to the accompanying drawings, a method, system, and electronic device for inner ear electroencephalographic closed-loop neuroelectroelectric modulation targeting the hypothalamus according to embodiments of the present invention.

[0037] Example 1:

[0038] This embodiment provides a complete and systematic closed-loop neuromodulation method for the inner ear EEG. This method primarily relies on a wearable neuromodulation device that integrates a flexible EEG sensing array, multimodal physiological sensors, an edge computing microprocessor, and a multi-channel digital-to-analog converter output unit.

[0039] In practical applications, such as for users who are in high-pressure environments for extended periods and suffer from circadian rhythm disorders or sleep disturbances, this method collects objective physiological feedback from users to construct a data loop, aiming to achieve precise and adaptive targeted intervention on the hypothalamic autonomic nervous system. This embodiment specifically includes the following:

[0040] Phase 1: Initialization and construction of individual neural response fingerprints.

[0041] Specifically, the method in this embodiment first performs the acquisition of multimodal physiological signals, extracts the individual hypothalamic neural response fingerprint, and generates an initial personalized regulation model containing a hierarchical long short-term memory neural network.

[0042] In practical applications, the human hypothalamus, as the highest regulatory center of the autonomic nervous and endocrine systems, exhibits highly individual-specific responses to external physical stimuli such as electric current and sound waves. Therefore, the system first needs to establish an individual hypothalamic neural response fingerprint (H-NRF), which is defined as a set of data features that uniquely identifies the physiological response patterns of a specific user's hypothalamic network to a specific parameter matrix input.

[0043] The individual hypothalamic neural response fingerprint consists of basic threshold features, dynamic response features, multimodal collaborative features, and long-term plasticity features, specifically including the following sub-components and performing corresponding data acquisition and extraction:

[0044] For example, the basic threshold features include the current sensing threshold, current discomfort threshold, and frequency response inflection point of the plurality of auricular acupoints. In this embodiment, the plurality of auricular acupoints are anatomically defined as the distribution areas of the vagus nerve branches, Shenmen acupoint, sympathetic acupoint, and endocrine acupoint. During the initialization phase, the system sequentially outputs tentative electrical stimulation pulses, starting from zero and increasing in preset steps, to the aforementioned target points through a built-in programmable constant current source, and simultaneously receives user button feedback or determines the boundary between perception and discomfort through skin conductance mutations. The frequency response inflection point reflects the nonlinear physiological gain amplification effect of electrical stimulation at a specific frequency at which a particular target point exhibits such a effect.

[0045] like Figure 2 This paper presents a nonlinear surface plot of the hypothalamic neural response based on multi-frequency electrical stimulation. The color gradient from dark blue to dark red and the undulating shape of the three-dimensional surface objectively reflect the combined variation of the comprehensive exponential response amplitude with the electrical stimulation frequency and current intensity. The horizontal axis represents the electrical stimulation frequency, the horizontal vertical axis represents the current intensity, and the vertical height represents the comprehensive exponential response amplitude.

[0046] Observing the data distribution of the spatial surface, it can be seen that when the current intensity increases in the range of 0 to 0.4 mA, the height of the response surface rises slowly and appears blue, which reflects the climbing stage of the perception threshold from no characterization to the generation of electrophysiological response in the target area.

[0047] When the current intensity increases to above 0.8 mA and the electrical stimulation frequency is concentrated in the 20 Hz to 25 Hz range, the surface exhibits a significant nonlinear bulge, and the surface color rapidly transitions to a deep red high-value region. The comprehensive exponential response amplitude corresponding to its apex reaches approximately 90. This bulge region is the frequency response inflection point described in the embodiment, indicating that a specific range of physical parameter combinations can stimulate the hypothalamic nerve projection area to produce a significant physiological gain amplification effect.

[0048] Furthermore, when the current intensity continues to increase beyond 1.2 mA, or when the electrical stimulation frequency deviates significantly from the 22 Hz central range, the surface height tends to flatten or even decrease, and the color correspondingly degrades to yellowish-green. This waveform transformation verifies the self-protective mechanism and tolerance characteristics of the human nervous system when approaching the current discomfort threshold. This nonlinear response surface plot provides a quantified physical boundary and an objective dose evaluation reference for the subsequent generation of multimodal co-stimulation parameter matrices in the closed-loop system.

[0049] Furthermore, the dynamic response characteristics include the rate of change of the relative hypothalamic functional index of the multiple auricular acupoints under different stimulation parameters, peak response time, effect duration, and recovery time constant. Extraction of this characteristic is used to establish a kinetic mapping relationship between stimulation dose and physiological effect. For example, after applying a stimulus, the system records the time required from the start of stimulation to the maximum offset of each physiological indicator (i.e., peak response time), and the decay characteristics of the physiological indicators returning to baseline after stimulation is withdrawn (i.e., recovery time constant).

[0050] Optionally, the multimodal synergistic features include a preset synergistic enhancement coefficient, synchronization phase difference, and volume intensity matching curve between acoustic stimulation patterns and electrical stimulation. In multimodal modulation, the auditory pathway has a complex neural projection network with the hypothalamus through the medial geniculate body. The system plays specific binaural beats or isochronous tones through bone conduction or air conduction modules and tests the superposition physiological effects of these beats with auricular electrical pulses at different phase differences, extracting the synergistic matching parameters that produce the maximum positive gain.

[0051] It is also important to note that the long-term plasticity characteristics include the rate of change of the baseline threshold, the rate of change of dynamic response sensitivity, and the synergistic effect decay coefficient. Synaptic weights in the nervous system undergo remodeling with long-term regulatory interventions (i.e., neural plasticity), causing parameters that are effective initially to become less effective after several months. Extracting this feature aims to provide a basis for compensating for long-term baseline drift in the system.

[0052] After acquiring the aforementioned fingerprint data, the system uses it as part of the pre-training weights to generate an initial personalized modulation model incorporating a Hierarchical Long Short-Term Memory (LSTM) neural network. The LSTM neural network employs a two-layer architecture, specifically including: a general base layer containing a pre-trained LSTM network layer trained on globally anonymized data in the cloud, used to extract global state change patterns; and an individual-specific layer containing a post-trained LSTM network layer trained on the user's local data, used to learn individual neural response patterns. The input feature matrix of this two-layer architecture includes multimodal physiological data from a preset historical period, the dynamic response features, the long-term plasticity features, and the matching degree features between the current stimulus parameters and the optimal parameters. This two-layer architecture ensures that the model possesses universal human understanding of neural modulation physiology while deeply embedding the user's unique neural response dynamics preferences.

[0053] Phase Two: High-fidelity acquisition of real-time data and elimination of physical artifacts.

[0054] Specifically, after the regulation model is initialized, the system enters a closed-loop regulation execution process. The system simultaneously acquires inner ear EEG signals and the aforementioned multimodal physiological signals, and extracts pure hypothalamic projection area EEG signals through a hardware-level stimulation artifact suppression circuit.

[0055] In practical applications of neuromodulation, a significant physical barrier is encountered: closed-loop systems require simultaneously injecting milliampere-level stimulation currents into ear tissues and acquiring microvolt-level EEG signals in a region extremely close to the stimulation target. The voltage gradient and polarization electric field generated by the stimulation current within human tissue have amplitudes thousands of times greater than those of the EEG signals. If purely software filtering is relied upon, the front-end amplifier will instantly saturate and fail due to this massive common-mode or differential-mode interference. Therefore, it is necessary to introduce the mechanism described above for extracting pure hypothalamic projection area EEG signals through a hardware-level stimulation artifact suppression circuit, which specifically includes the following underlying hardware actions:

[0056] First, while outputting each preset intensity level of electrical stimulation pulse, the microcontroller core sends a synchronization trigger signal to the hardware-level stimulation artifact suppression circuit. This trigger signal has nanosecond-level timing control precision, aiming to strictly and rigidly bind the clock domain of the stimulation output module to the EEG acquisition module.

[0057] Subsequently, the silence period of the electrical stimulation pulse is determined based on the synchronous trigger signal. A high-speed differential sampling channel is activated for continuous sampling after the falling edge of the electrical stimulation pulse and closed before the rising edge. Since electrical stimulation often uses square wave pulse sequences, there are gaps in tissue discharge between the pulses, i.e., silence periods. After detecting the falling edge of the stimulation pulse and waiting for the tissue parasitic capacitance discharge to decay to a safe threshold, the system rapidly closes the sampling channel via a field-effect transistor analog switch, capturing the weak EEG fragments not submerged by the strong electric field between the two stimulation pulse segments. The channel is then rapidly disconnected before the rising edge of the next stimulation pulse, thus achieving time-division multiplexing and physical avoidance in the time domain.

[0058] like Figure 3 This diagram presents a time-series comparison of high-fidelity EEG signal extraction and artifact cancellation during the stimulus-silent period. The diagram corresponds to the signal separation process of a hardware-level stimulus artifact suppression circuit at a microscopic timescale. It includes three time-series curves arranged from top to bottom, sharing a horizontal time axis with the horizontal axis unit being microseconds.

[0059] The red curve at the top represents the change of the original signal voltage over time, with the vertical axis in millivolts. The data shows that during the electrical stimulation pulse output from 0 to 50 microseconds and after 100 microseconds, the acquisition port recorded voltage spikes as high as 5 millivolts, and a significant discharge decay tail was observed after the falling edge at 50 microseconds. This high voltage interference would severely mask the true physiological potential.

[0060] The black broken line in the middle represents the sampling trigger level, and the vertical axis is in volts. Based on the 50-microsecond silence period calculated by the system, the curve accurately rises to a high level at 55 microseconds and falls back to a low level at 95 microseconds, thus defining a hardware sampling window of 40 microseconds. This window effectively avoids the early electrode polarization discharge and the later rising edge interference by utilizing the time difference.

[0061] The blue curve at the bottom represents the pure EEG voltage output after isolation and extraction. The vertical axis is in microvolts, and its waveform data remains stable within ±20 microvolts throughout the entire 150-microsecond observation period.

[0062] The longitudinal timing comparison of the red distorted waveform, the black gated waveform, and the blue smooth waveform objectively demonstrates the system's execution effect in using the physical layer's time-sharing avoidance and synchronous latching mechanisms to separate microvolt-level effective neurophysiological signals from artifacts generated by milliampere-level strong physical interventions.

[0063] For example, based on the frequency and intensity of the current electrical stimulation pulse, the microprocessor adaptively adjusts the cutoff frequency of the dynamic hardware low-pass filter network to a preset cutoff frequency range. This step aims to dynamically suppress high-frequency harmonic crosstalk generated by the high-frequency pulse edge according to the spectral energy distribution of the interference source, ensuring the smoothness of the analog signal before entering the analog-to-digital converter.

[0064] Furthermore, a polarization artifact isolation unit combining capacitive coupling and differential amplification isolates the DC offset voltage generated by electrode polarization, outputting the pure EEG signal from the hypothalamic projection area. At the tissue interface, the injected current from the metal electrodes generates a DC polarization potential (DC Offset) with an extremely long duration. This isolation unit uses a high-pass coupling network to intercept the DC component, while simultaneously using an instrumentation amplifier with high common-mode rejection ratio (CMRR) to amplify the differential useful signal, ultimately delivering a pure EEG sequence with a high signal-to-noise ratio to the digital signal processor.

[0065] The third stage: mathematical modeling and trend prediction of individual physiological states.

[0066] Specifically, the system normalizes the pure hypothalamic projection area EEG signals and the multimodal physiological signals based on the individual's hypothalamic neural response fingerprint, and calculates the individual's relative hypothalamic functional comprehensive index.

[0067] The absolute values ​​of baseline heart rate, baseline skin conductance, and energy distribution across different frequency bands of EEG vary significantly among different users. If absolute physical quantities are used directly, the algorithm model will be unable to correctly determine the state. Therefore, it is necessary to utilize baseline data from a fingerprint database to perform individual normalization on real-time signals, converting absolute deviations into relative volatility.

[0068] The individual's relative hypothalamic function index is calculated using the following formula:

[0069] ;

[0070] in: Defined as an individual's relative hypothalamic function comprehensive index, its value falls within a preset normalized range. This index is a quantitative representation of the user's current hypothalamic functional homeostasis.

[0071] , , , The relative scores are, in order, relative scores for the emotion dimension, stress dimension, sleep dimension, and cognitive dimension, and these relative scores are normalized based on the physiological baseline in the individual's hypothalamic neural response fingerprint. For example, the relative score for the stress dimension is obtained by analyzing the mapping between the high-frequency / low-frequency ratio of heart rate variability (HRV) and the slope of changes in skin conductance; the relative score for the emotion dimension is obtained by analyzing the absolute power asymmetry index of alpha waves in the frontotemporal lobe region of the pure EEG signal.

[0072] , , , These are defined as adaptive weights for the four dimensions mentioned above, and their values ​​are controlled by the individual's hypothalamic neural response fingerprint. For example, if the fingerprint data shows that a user's physiological response to stress is more sensitive and representative, The weight value will be automatically increased.

[0073] After acquiring the current real-time quantitative index, the system inputs the individual's relative hypothalamic functional comprehensive index into the hierarchical long short-term memory neural network to predict the trend of state changes and the probability of risk. The memory units (cell states) of the neural network can retain time-series patterns from multiple past sampling periods. Combined with the non-linear activation operations of the current input gate and forget gate, it can deduce the evolution of physiological states within a preset time window at its output. For example, it can predict the slope of the decrease in alertness that a user may experience due to accumulated fatigue within the next thirty minutes.

[0074] like Figure 4 This diagram illustrates the convergence curve of the loss function and the trajectory of state prediction in the model update and state evaluation process of a hierarchical long short-term memory neural network. The diagram is divided into two sub-regions: the horizontal axis of the upper region represents the number of training iterations (in seconds), and the vertical axis represents the loss function value (dimensionless).

[0075] The blue solid line in the figure represents the training loss curve. This curve exhibits an exponential smooth decrease in waveform characteristics as the number of iterations increases. When the number of iterations reaches 500, the loss function value gradually and steadily converges from the initial value of around 0.79 to around 0.04. This waveform transformation objectively reflects that the hierarchical long short-term memory neural network has a stable learning convergence ability after aggregating the gradients of the model parameters.

[0076] The horizontal axis in the lower half of the region is named time, in seconds, and the vertical axis is named the comprehensive index score, in points. The black solid curve in the lower half of the region represents the historical actual score calculated based on pure EEG signals and multimodal physiological signals over the past 30 seconds, with the value fluctuating between 75 and 85 points; the red dashed curve represents the trend of the individual's relative hypothalamic functional comprehensive index over the next 45 seconds as predicted by the network model, and the waveform shows that the predicted score shows a clear downward trend.

[0077] Furthermore, the orange-red dashed line fixed at a height of 40 minutes on the vertical axis represents the risk warning baseline. By observing the time point at which the red dashed line penetrates this risk warning baseline, the system can identify and quantify the probability of future psychological safety risks in advance. This provides reliable data guidance for the generation and intervention of multimodal synergistic stimulation parameter matrices, enhancing the foresight of closed-loop neuroelectric modulation schemes.

[0078] Phase 4: Generation and execution of the multimodal control parameter matrix.

[0079] Specifically, based on the individual's relative hypothalamic functional comprehensive index, the trend of state change, and the individual's hypothalamic neural response fingerprint, the system generates a multimodal co-stimulation parameter matrix containing independent parameters of multiple auricular acupoints.

[0080] To ensure that the generated parameter matrix achieves the intervention target in terms of medical mechanism while effectively guaranteeing safety of use, this method employs a three-level parameter generation mechanism to generate a multimodal co-stimulation parameter matrix containing independent parameters of multiple auricular acupoints, including:

[0081] First-level parameter generation: Match the historically optimal parameter combination corresponding to the individual's relative hypothalamic functional comprehensive index from the fingerprint database to generate an initial parameter matrix. This step is equivalent to experience retrieval, where the system retrieves a set of target stimulation parameters (including current amplitude, pulse frequency, duty cycle, etc. of each target point) that produced the greatest positive improvement benefit under the same physiological state in the user's history.

[0082] Secondary parameter adjustment: Based on the aforementioned basic threshold features and long-term plasticity features, the initial parameter matrix undergoes a safety check, and is then multiplied by a product containing EEG stability coefficients, physiological tolerance coefficients, and behavioral stability coefficients for constraint adjustment. This step is the core safety constraint mechanism. For example, if the current amplitude in the retrieved historical parameters exceeds the current discomfort threshold in the recently updated basic threshold features, the system will forcibly clip it. Subsequently, based on the current EEG frequency band energy stability, heart rate, blood oxygenation, and other physiological tolerance limits, corresponding constraint coefficients (usually floating-point numbers no greater than 1) are generated, and the initial parameters are weighted with reduced quotients.

[0083] Level 3 fine-tuning of parameters: Based on high-frequency feedback indicators, the constrained matrix is ​​finely adjusted at a preset step size, and the multimodal co-stimulation parameter matrix is ​​output. During this stage, the system utilizes electromyography or subtle bodily sign feedback with sampling rates above 100Hz to perform microscale closed-loop compensation of the stimulation parameters within a preset safe step size range.

[0084] Optionally, the hardware driving unit synchronously outputs electrical and acoustic stimulation according to the multimodal co-stimulation parameter matrix for closed-loop targeted modulation. The electrical stimulation module independently drives flexible microelectrode arrays attached to different auricular acupoints using a high-precision numerically controlled constant current source; the acoustic stimulation module synchronously triggers specific modulated audio signals. The current and sound waves converge in the human nervous system, achieving efficient intervention on the hypothalamus target area.

[0085] Phase 5: Long-term self-evolution of the system and security blocking mechanisms.

[0086] It is also important to note that the system of a long-term wearable device cannot be a rigid, static program. Shifts in individual physiological characteristics and changes in the environment can cause the original algorithm to fail. Therefore, the method in this embodiment further includes: collecting post-intervention neural response data, performing online self-calibration to update the individual hypothalamic neural response fingerprint, and aggregating model parameters and updating gradients through a federated learning framework to update the hierarchical long short-term memory neural network.

[0087] In this process, the online self-calibration is based on a three-level calibration mechanism, including:

[0088] Routine calibration: Triggered when the calculated similarity between the current neural response and the fingerprint baseline falls within a first preset similarity range, parameter adjustments with a preset step size are inserted to update the dynamic response features. This process typically involves incorporating extremely small probe parameters into the routine modulation sequence without the user's noticeable awareness, in order to determine the neural conduction efficiency for the day.

[0089] Periodic deep calibration: Triggered when the similarity falls within a second preset similarity interval or the intervention effect score falls below a preset value, a standardized stimulus sequence is executed to update the multimodal collaborative features and the dynamic response features; the value of the first preset similarity interval is greater than the value of the second preset similarity interval. This means that when the system detects a significant deviation between the actual control effect and the model's expectations (a decrease in similarity), it will rerun the complete calibration process when the user is resting or in a safe state.

[0090] Long-term plasticity calibration: Based on the set long-term statistical neural response data, the long-term plasticity characteristics are updated and the baseline of stimulation parameters for future cycles is adaptively adjusted.

[0091] The core evaluation metric upon which the three-level calibration mechanism and subsequent anomaly handling rely is the similarity, which is calculated using the formula:

[0092] ;

[0093] in: This represents the similarity between the current neural response and the fingerprint baseline. The closer the value is to 1, the better the current neural response pattern matches historical fingerprint data; the lower the value, the more significant the variation in physiological state or environmental factors.

[0094] The total number of features of the individual's hypothalamic neural response fingerprint is a positive integer greater than 1 (which includes tens of dimensions of parameters in the full feature set extraction of this system). For the newly collected Item neural response characteristic value. For the fingerprint database The historical mean of each feature. For the fingerprint database The standard deviation of a feature is used to measure the historical fluctuation range of that feature.

[0095] This is a positive singularity prevention constant for extremely small systems. When the historical fluctuation of a certain physiological characteristic is so small that the standard deviation approaches zero, this constant is used to prevent the underlying algorithm from executing a division-by-zero overflow exception, ensuring the engineering robustness of the closed-loop feedback logic.

[0096] For the first The weight coefficients of the features indicate the differences in importance levels of different features in the overall similarity assessment.

[0097] Based on the derived calculations of the aforementioned similarity index, to prevent the device from generating harmful outputs under abnormal physiological states, the method further includes a three-level abnormal safety handling closed loop derived from similarity, including:

[0098] The system calculates the abnormal deviation of neural responses in real time, defined as follows: The abnormal deviation of the neural response is divided into three intervals in ascending order of value: the first abnormal deviation interval, the second abnormal deviation interval, and the third abnormal deviation interval.

[0099] like Figure 5This diagram presents a heatmap of the multidimensional similarity evolution matrix under long-term neural intervention. The horizontal axis represents the intervention period in days, recording the evolution of neural modulation data over a 30-day period. The vertical axis represents the feature dimension number, in dimensionless units, encompassing 10 key individual hypothalamic neural response feature dimensions. The color bar on the right serves as the legend for the entire diagram, illustrating the numerical mapping relationship of neural response similarity. The color is dimensionless, gradually changing from dark blue to cyan and then to dark red, corresponding to the gradual increase in similarity values ​​from 0 to 1.

[0100] Observing the color distribution and data evolution trajectory within the heatmap reveals that during most of the conventional intervention period, the matrix exhibits large areas of deep red to orange-red, indicating that the neural response similarity remains stable between 0.85 and 1, reflecting a good matching degree in the individual-specific fingerprint feature database. Around day 8, the color of some feature dimensions clearly transitions to yellow-green, and the similarity drops to around 0.65. This data fluctuation corresponds to the first abnormal deviation range described in the embodiment. At this point, the system will respond to this state by automatically initiating the daily calibration process and reducing the electrical stimulation intensity according to a preset ratio.

[0101] On day 18, a bright cyan patch appeared in the middle of the matrix, indicating that the similarity had further dropped to around 0.45. This waveform change triggered the judgment condition for the second abnormal deviation interval. The system then paused the current electrical stimulation action and forcibly started periodic depth calibration to recalibrate the feature baseline.

[0102] On day 27, the color of the entire vertical feature dimension instantly plummeted to dark blue, indicating a sharp drop in overall similarity to around 0.2. This extremely low value objectively represents a high-risk state in the third abnormal deviation interval. At this moment, the system immediately blocked the output commands of all multimodal co-stimulation parameter matrices and triggered external alarm linkage. This heatmap intuitively reflects the closed-loop neural electromodulation system's ability to keenly capture fluctuations in multidimensional physiological characteristics and the execution logic of its graded abnormality safety handling mechanism in long-term applications.

[0103] If the abnormal deviation of the neural response falls within the first abnormal deviation range, indicating a mild deviation from the individual's state or environmental noise interference, the system does not need to interrupt its operation. Instead, it automatically initiates the routine calibration and reduces the electrical stimulation intensity by a preset ratio to prevent overstimulation using a conservative intervention strategy.

[0104] If the abnormal deviation of the neural response falls within the second abnormal deviation range, indicating a moderate disconnect between the algorithm model and actual physiological feedback, the current parameters may trigger unpredictable side effects. The system will immediately pause the current electrical stimulation and forcibly initiate the periodic depth calibration, resuming operation only after the fingerprint features have been recalibrated.

[0105] If the abnormal deviation of the neural response falls within the third abnormal deviation range, which constitutes an extremely high-risk event (such as electrode detachment leading to impedance abrupt change, sudden severe cardiovascular and cerebrovascular physiological abnormalities in the user, or a strong external electromagnetic attack on the system), the underlying hardware control logic will directly intervene, immediately blocking the output commands of all multimodal co-stimulation parameter matrices and cutting off the constant current source power supply in the physical circuit. Simultaneously, it will trigger external alarm linkage (such as sending a distress and error message to the user's mobile phone or emergency contacts via Bluetooth).

[0106] Furthermore, regarding the long-term iteration of the hierarchical long short-term memory neural network, the system employs federated learning technology to ensure the sharing of collective wisdom while protecting data privacy. The process of aggregating model parameter update gradients through the federated learning framework to update the hierarchical long short-term memory neural network includes the following specific steps: Incremental learning is performed on the individual-specific layer locally to generate model parameter update gradients with differential privacy encryption properties. The device locally calculates the corrected gradients of the network weights using the backpropagation algorithm by comparing the error between the actual intervention results and the predicted results. To prevent attackers from using the gradients to infer the user's original physiological characteristics (such as retrieving the user's EEG waveform), the system injects Laplace noise or Gaussian noise into the gradient vector, giving it differential privacy properties.

[0107] Subsequently, the encrypted model parameter update gradients are uploaded separately to the cloud. The cloud then uses a federated averaging algorithm (FedAvg) to aggregate gradients from multiple devices to update the general base layer. The cloud server does not access any user's raw EEG or heart rate data; it simply performs weighted summation and averaging of the encrypted gradients uploaded from thousands of devices to discover new universal patterns in the neural response of the entire human population.

[0108] Finally, the local device receives the updated general base layer from the cloud and merges it with the individual-specific layer to complete the adjustment model update. This closed loop ensures that each individual device retains its absolute privacy and habits while sharing the optimized results of the group model accumulated over the network.

[0109] Based on the above process details, a comparative analysis of the technical solution of this embodiment with the prior art reveals that there are two major technical bottlenecks in existing conventional neuromodulation devices: First, electromagnetic artifacts caused by the stimulator hardware output often paralyze the feedback acquisition link, forcing the adoption of blind tuning or open-loop mode; second, the setting of control parameters is rigid and cannot adapt to the changes in synaptic plasticity and complex dynamic internal environment fluctuations of different individuals over a long period of time.

[0110] The solution in this embodiment effectively solves the problem of extracting weak EEG signals under strong stimulation through a low-level hardware-level stimulation artifact suppression circuit, providing a real and reliable feedback source for closed-loop control. On this basis, multimodal physiological normalization processing and construction of individual hypothalamic neural response fingerprints are used to unify complex and diverse biological signals into a standardized mathematical dimension that can be understood by computer deep learning networks. Furthermore, relying on the edge-cloud combined federated learning framework and a three-level self-calibration and anomaly handling mechanism, the entire system avoids the limitations of fixed static program execution and is upgraded into a biological control system that can accompany users in their long-term lives, has a high degree of self-reflection and risk avoidance capabilities, and can evolve throughout life while protecting data privacy.

[0111] Example 2:

[0112] This embodiment, as a further extension of Embodiment 1, addresses the issue of physical execution deviations encountered in the complex microenvironment of the human ear canal after neural modulation commands are sent to the hardware port. It provides a high-precision, low-level closed-loop compensation mechanism. Specifically, this embodiment includes the following:

[0113] Phase 1: High-frequency carrier monitoring of the dynamic impedance of the ear canal microenvironment.

[0114] Specifically, in the method provided in this embodiment, the step of synchronously outputting electrical stimulation according to the multimodal synergistic stimulation parameter matrix further includes a dynamic current targeted compensation step based on the impedance monitoring of the ear canal microenvironment: within the period of the output electrical stimulation pulse, a high-frequency subthreshold monitoring carrier is synchronously injected to collect dynamic complex impedance data of the interface between the electrode and the ear canal skin in real time.

[0115] In actual neuroelectric modulation operations, the human external auditory canal is a highly dynamic physical microenvironment. The skin of the ear canal is richly supplied with sebaceous and ceruminous glands. When users wear the device during daily activities, such as brisk walking or in warm environments, the amount of sweat and sebum secreted on the surface changes in real time. Furthermore, when users perform actions such as chewing, swallowing, or speaking, the movement of the temporomandibular joint pulls on the ear canal cartilage and soft tissue, causing micrometer-level mechanical slippage of the flexible electrodes attached to the inner wall of the ear canal. This leads to abrupt changes in the contact area and pressure between the electrodes and the skin. The combination of these physiological and physical factors results in highly unstable interfacial contact impedance.

[0116] To measure this change in real time without interfering with normal neural regulation, the system employs a high-frequency subthreshold monitoring carrier technique. Here, "high-frequency" refers to an AC signal with a frequency set within a specific range of biological tissue dielectric dispersion characteristics (typically within a specific frequency band), allowing it to easily penetrate the highly capacitive stratum corneum of the skin. "Subthreshold" means that the amplitude of the monitoring carrier is strictly limited below the activation threshold of the neural action potential, ensuring that after injection into the human body, it is not only imperceptible to the user's auditory or somatosensory nerves but also does not produce any unintended physiological stimulation to the hypothalamic nerve projection area.

[0117] In terms of specific hardware implementation, the system uses a precision analog adder circuit to linearly superimpose the low-frequency square wave electrical stimulation pulse carrying the therapeutic effect with the aforementioned high-frequency subthreshold monitoring carrier wave, and then outputs it through a digitally controlled constant current source drive circuit. During this output cycle, the high-speed current sampling module and the voltage differential amplification module inside the hardware system operate synchronously, acquiring the total current injected into the circuit and the voltage difference across the electrode plates. Subsequently, the digital signal processor uses orthogonal demodulation algorithms (such as lock-in amplification technology) to extract the in-phase and quadrature components of the high-frequency carrier wave after reflection through the ear canal tissue, and then calculates dynamic complex impedance data containing amplitude and phase information. This data reflects the electrical transmission path status between the electrode and the subcutaneous tissue in real time and objectively at the current microsecond level.

[0118] Phase 2: Multidimensional impedance separation calculation under the equivalent circuit model.

[0119] For example, based on the dynamic complex impedance data, the bypass shunt impedance of the ear canal surface and the deep penetration impedance of the target nerve are calculated separately.

[0120] After obtaining the overall dynamic impedance data, it cannot be directly used as a general value for compensation calculations because the physical mechanisms leading to impedance changes are quite different. From the perspective of equivalent circuit models in biomedical engineering, the impedance network between the electrode and the target nerve is not a single series impedance, but a complex network composed of multiple paths. Among them, the bypass shunt impedance on the surface of the ear canal mainly characterizes the electrical impedance formed by the surface conductive film composed of sweat, sebum, and residual moisture on the skin surface. When the ear canal sweats, this conductive film forms a short-circuit channel in parallel with the deep tissue, causing a large amount of current to flow away along the skin surface and fail to penetrate into the tissue.

[0121] The deep penetration impedance of the target nerve mainly characterizes the series impedance that the current must overcome to pass through the dermis and subcutaneous tissue from beneath the epidermis to finally reach the terminal branches of the vagus nerve or auriculotemporal nerve and their myelin sheath structures. This impedance is relatively stable and reflects the inherent conductivity and anatomical structure of the tissue.

[0122] To achieve precise decoupling of these two distinctly different physical path impedances, the system incorporates an impedance vector-based separation algorithm within the microprocessor. This algorithm calculates the required magnitude of the surface impedance to be separated using the following formula:

[0123] ;

[0124] In the formula: The total amplitude modulus of the dynamic complex impedance data extracted through orthogonal demodulation represents the overall impedance of the current measurement circuit.

[0125] The phase angle of the dynamic complex impedance data is given. Since the cell membrane structure of deep tissues has significant capacitive characteristics, while the surface sweat shunt channels exhibit purely resistive characteristics, the purely resistive surface shunt component can be effectively separated from the comprehensive impedance including capacitive reactance by projecting the cosine value of the phase angle.

[0126] This is the anatomical calibration factor. This calibration factor is used to correct the geometric distortion of the surface electric field distribution caused by different ear canal curvatures and electrode contact patterns of different users. Its initial value is established based on a standard ear canal model when the system is assembled at the factory, and is then slightly fitted based on historical data during actual use.

[0127] Through the calculation of the above formula, the system successfully extracted parameters characterizing the degree of sweat diversion from the mixed physical signals. At the same time, combined with Kirchhoff's laws of circuits, it derived another dimension of deep penetration impedance, providing an accurate data source for subsequent compensation calculations.

[0128] The third stage: the compensatory reconstruction logic of the dynamic current compensation model.

[0129] It is important to note that after impedance separation, the system enters the core compensatory control phase to ensure the accurate delivery of the neuromodulation scheme. Based on the separated impedance data, the target stimulus intensity in the multimodal co-stimulation parameter matrix is ​​reconstructed compensatorily using a dynamic current compensation model.

[0130] Compensatory reconstruction refers to the phenomenon where, due to the aforementioned bypass shunting effect, if the hardware port rigidly outputs current according to the target stimulus intensity issued by the algorithm matrix, the current actually flowing through the target nerve will be significantly attenuated. Therefore, the underlying hardware control must perform over-output or convergent output to compensate for the losses or abrupt changes caused by the physical environment.

[0131] The dynamic current compensation model calculates the current value that the hardware execution terminal needs to output using the following formula:

[0132] ;

[0133] In the formula: This is the real-time current output from the hardware execution terminal. This is the total physical current amplitude actually generated by the constant current source drive circuit of the final instruction of the digital-to-analog converter (DAC) inside the system.

[0134] The actual current, designed to penetrate the stratum corneum of the skin and act on the hypothalamic nerve projection area, is configured to be equal to the target stimulus intensity in the multimodal co-stimulation parameter matrix. This means that regardless of how harsh the external environment changes, the system must ensure that this value is stably transmitted to the nerve tissue. It is the prescription dosage at the medical algorithm level, and It is the dosage applied at the electronic engineering level.

[0135] Independently defined as the deep penetration impedance of the target nerve, it is calculated from the separation in the previous stage.

[0136] The bypass shunt impedance of the ear canal surface is defined independently and is also derived from the separation calculation in the previous stage.

[0137] The ear canal morphological impedance coupling coefficient is defined as an individual-specific parameter, extracted based on fundamental features from the individual's hypothalamic neural response fingerprint. The cross-sectional shape of the human ear canal, skin texture roughness, and epidermal thickness vary from person to person. These anatomical features determine the uniformity of surface sweat distribution and the degree of divergence of deep current paths. When acquiring neural response fingerprints, the system extracts this user-specific coupling parameter using an established multi-frequency bioimpedance scanning baseline, which is used to correct the current shunting ratio in the actual three-dimensional tissue.

[0138] For example, when a user is at rest and the ear canal is dry, the surface of the ear canal is extremely dry, and a sweat shunt channel has not formed. At this time, the bypass shunt impedance... The value of is extremely large. In the formula calculation, the proportional term... It approaches zero. Substituting into the formula, we can see that... Basically equivalent to This indicates that at this point, the hardware port only needs to output the target current to ensure that most of the current flows into the deep nerve, without the need for additional compensation.

[0139] Conversely, when a user is engaged in strenuous exercise or in a high-temperature environment that causes excessive sweating in the ear canal, the conductive sweat reduces the bypass shunt impedance. A rapid decrease. A large amount of current tends to leak out along the low-resistance channels on the skin's surface. At this point, the proportional term... Significantly increased. The real-time current calculated by the formula... The current will be proportionally greater than the actual current. Upon receiving the reconstructed high instruction value, the system hardware automatically increases the total output of the constant current source. This ensures that even if a significant portion of the current is wasted along with sweat, the remaining current, capable of penetrating high-resistance tissue to reach the target nerve, can still be precisely maintained at the target stimulus intensity required by the algorithm model. This enables precise dose delivery in complex interference environments.

[0140] like Figure 6 This diagram displays a dynamic complex impedance separation and a multi-target Pareto front plot of compensating current. The horizontal axis of the plot is named the surface bypass shunt impedance drop, in kiloohms, reflecting the severity of impedance decrease caused by physical changes in the ear canal microenvironment such as sweating. The vertical axis is named the deep-target current fidelity, in percentage, reflecting the closeness of the actual current penetrating the stratum corneum and actually acting on the hypothalamic nerve projection area to the intensity of the target stimulus.

[0141] The blue scatter dots in the figure represent the current fidelity distribution collected under different impedance drop conditions when using a traditional output strategy without environmental compensation. The overall graph shows a discrete state that diverges significantly downwards and to the right as the horizontal axis value increases, reflecting the dose loss phenomenon of the traditional strategy when facing sweat diversion.

[0142] The red solid line curve located at the upper edge of the scatter plot represents the optimal compensation front achieved by this scheme after adopting the dynamic current compensation model. Observing along this red curve, when the surface bypass shunt impedance drop value is in the range of 0 to 30 kΩ, the curve shape is flat, and the deep-target current fidelity is stably maintained in the high range of 95% to 100%. This reflects the resistance of the impedance separation and compensation reconstruction logic to physical interference from bypass shunt interference.

[0143] As the horizontal axis value continues to increase to the right to the extreme sweating drop state of 50 kiloohms, the red curve shows a gentle downward bend, and the fidelity value gradually drops back to around 80%.

[0144] This waveform transformation objectively characterizes the physical protection intervention process of the system's underlying circuitry when the hard-wired current threshold clamping mechanism is triggered, actively limiting the actual output peak value to prevent damage to human tissue and overload of electronic components. The shape of this leading-edge curve, compared with the blue scatter dots, demonstrates the system's ability to achieve a comprehensive coordination effect that balances targeted execution accuracy and underlying hardware security when dealing with complex dynamic microenvironments.

[0145] Phase 4: Hardware security limits.

[0146] After the dynamic current compensation model generates real-time reconstructed compensation current values, to prevent overload of electronic components or tissue damage caused by extreme changes in the physical environment, the underlying circuit also integrates hard-wired compliant voltage limiting protection and current threshold clamping mechanism. When the compensated command value exceeds the preset absolute safety envelope, the system will prioritize the protection of human tissue as the highest criterion, limit the actual output peak value, and upload the abnormal flag to the upper-level control logic.

[0147] The effectiveness of the overall technical solution in this embodiment is summarized and explained based on existing technologies: In traditional transcutaneous electrical nerve stimulation (TENS) or transcutaneous auricular vagus nerve stimulation (taVNS) devices, the hardware output strategy generally adopts a single constant voltage control or a constant current control without environmental compensation. When using such traditional devices, once the user sweats, causing a decrease in skin surface impedance, the constant voltage device will cause the total current flowing into the body to surge, resulting in a strong stinging sensation or even burns; while in ordinary constant current devices, although the total current remains unchanged, the current actually reaching the deep nerves will be sharply reduced due to the diversion effect of sweat, leading to the failure of the treatment intervention. This uncertainty in the execution of the underlying physical hardware renders all advanced AI algorithms and predictive models built on it meaningless in terms of practical control.

[0148] The technical solution described in this embodiment achieves real-time monitoring of the microscopic dynamic impedance of the ear canal through high-frequency threshold waveguide technology. Combined with a rigorous equivalent circuit separation algorithm and compensatory reconstruction model, it establishes an independent hardware-level closed-loop compensation mechanism. This mechanism acts as a physical isolation mechanism for the entire neuroelectric modulation system, eliminating interference from dynamically changing skin interface impedance. Regardless of whether the external ear canal environment is dry, humid, or experiences changes in fit due to movement, the system ensures that the target stimulus intensity sent by the algorithm layer is accurately delivered to the subcutaneous nerve tissue.

[0149] This not only greatly improves the comfort and safety of long-term wear and control, but also ensures the execution fidelity of the multimodal synergistic stimulation parameter matrix in Example 1, making the collected individual hypothalamic neural response fingerprints more pure and objective, effectively closing the precise calculation and precise execution link of neural electrical modulation, and has strong practical significance for industrial transformation and clinical application.

[0150] Example 3:

[0151] This embodiment, as an important component of the overall closed-loop neural regulation scheme of the present invention, aims to solve the problem of conflict between regulation instructions and physiological rhythms that may occur during the issuance of neural regulation instructions due to ignoring the circadian rhythm controlled by the human body's underlying biological clock, namely the suprachiasmatic nucleus of the hypothalamus.

[0152] This embodiment introduces a biological rhythm adaptive mechanism to ensure that the regulatory process is coordinated with the body's basal metabolic level. Specifically, this embodiment includes the following:

[0153] Phase 1: The necessity and definition of adaptive parameter gating modulation of biological rhythms.

[0154] Specifically, this embodiment provides a parameter-gated modulation step based on hypothalamic biological rhythm adaptation, which is deployed after generating a multimodal co-stimulation parameter matrix and before synchronously outputting electrical and acoustic stimuli. The physical significance of this step is that it acts as a rhythmic safety gating mechanism in a closed-loop system.

[0155] It is important to note that the biorhythm adaptive parameter gating modulation defined here refers to the system's requirement to assess whether a neuromodulation command targeting mood, stress, or cognitive enhancement violates the user's current circadian rhythm phase before issuing such a command. The hypothalamus, as the body's highest pacemaker, exhibits significant periodic fluctuations in neuronal firing frequency, hormone secretion, and receptor sensitivity over a 24-hour period. If the system forcibly issues high-frequency electrical stimulation aimed at increasing wakefulness when the user's circadian rhythm is in a low-metabolic / preparation-for-sleep phase, not only will the modulation efficiency be low, but it may also trigger autonomic nervous system rebound resistance effects such as abnormal heart rate fluctuations and endocrine disorders. Therefore, this embodiment extracts metabolically relevant underlying signals to achieve dynamic gating restrictions on the modulation parameters, allowing neural intervention to proceed in accordance with the natural rhythm.

[0156] Phase 2: Real-time extraction and quantification of underlying metabolic signals.

[0157] Specifically, the system extracts real-time body temperature change gradient and skin conductance fluctuation rate based on synchronously acquired multimodal physiological signals.

[0158] In practical applications, the temperature inside the ear canal, through tympanic membrane thermal radiation, can effectively reflect the dynamics of the body's core temperature. The system utilizes an infrared thermopile sensor or a high-precision negative temperature coefficient thermistor (NTC) integrated into the ear-hook main structure to acquire real-time temperature sequences at a preset high sampling rate. The real-time body temperature change gradient is defined as the slope of body temperature change over time within a preset time window. This indicator provides direct evidence of the rise or fall of metabolic water levels regulated by the hypothalamus.

[0159] For example, the system uses a skin conductance sensor module to collect electrical conductivity data of the skin around the ear. The skin conductance volatility is defined as the rate of variation of the slow-moving component reflecting the tension of the basal sympathetic nervous system, extracted after time-domain decomposition of the collected skin conductance levels. Since skin conductance activity is driven by sweat gland secretion controlled by the hypothalamus, its volatility has significant baseline differences in different phases of the biological clock.

[0160] Phase 3: Mathematical modeling and conflict quantification of biological rhythm phase angles.

[0161] Specifically, based on the real-time body temperature change gradient and the skin conductance fluctuation rate, the system calculates the phase angle of the biological rhythm under the current metabolic state.

[0162] The biological rhythm phase angle is a mathematical parameter that quantitatively describes the position of the human body within a 24-hour circadian rhythm loop. To accurately calculate this parameter, this embodiment constructs a first algorithm formula to map discrete metabolic features into a rhythm vector space:

[0163] ;

[0164] In the formula: The phase angle of the biological rhythm, which ranges from negative to positive pi, is used to determine whether the current metabolic water level is in the rising, peak, falling, or trough phase of the rhythm loop. The real-time body temperature change gradient is given. It is independently defined as the standard normalization constant of individual body temperature changes extracted from the fingerprint database. The variability of the skin conductance is denoted as . Independently defined as the standard normalized constant of individual skin conductance fluctuations extracted from the fingerprint database.

[0165] It is important to note that this formula allows the system to deduce the current phase of the human body's biological clock from the underlying metabolic dimension.

[0166] Specifically, the system compares the state change trend with the phase angle of the biorhythm to quantify the biorhythm phase conflict degree. The state change trend is the direction of the regulatory target predicted by the hierarchical long short-term memory neural network based on higher-order signals such as EEG, while the biorhythm phase angle represents the reference direction of the underlying biorhythm. When the two point in opposite directions, for example, when the biological clock indicates that it is currently in a deep repair phase, while the regulatory algorithm attempts to induce high-intensity alertness due to the detection of instantaneous stress, the vector difference between the two constitutes the biorhythm phase conflict degree.

[0167] Phase 4: Parameter-gated reconstruction logic based on exponential functions.

[0168] Specifically, in response to the biological rhythm phase conflict degree exceeding a preset rhythm tolerance threshold, the multimodal co-stimulation parameter matrix is ​​reconstructed through a rhythm modulation function.

[0169] Reconstruction here refers to the reconstruction of the multimodal costimulation parameter matrix. The system nonlinearly compresses or attenuates key dimensions such as current intensity, pulse frequency, and acoustic volume. This reconstruction is not a simple cutoff, but rather utilizes a smooth exponential curve to achieve a smooth transition of commands, avoiding secondary interference caused by abrupt changes in control parameters.

[0170] This embodiment uses the second algorithm formula, namely the rhythm modulation function, to calculate the final parameters to be sent:

[0171] ;

[0172] In the formula: This refers to the rhythm-adaptive stimulus parameter matrix that is actually sent to the hardware execution end for execution after being controlled by rhythm gating. The multimodal co-stimulation parameter matrix is ​​generated by a high-order AI model. The individual rhythm sensitivity attenuation constant, derived from the individual's hypothalamic neural response fingerprint, reflects the sensitivity of the user's physiological system to rhythm conflicts. This represents the quantified phase conflict degree of the biological rhythm. The preset rhythm tolerance threshold is specific to each individual.

[0173] For example, by This functional logic ensures that the attenuation mechanism is activated only when the level of conflict truly threatens physiological homeostasis (i.e., exceeds the tolerance threshold). This design guarantees the system's regulatory flexibility within the normal range.

[0174] For example, to better understand the application of this step in actual operations, consider the following scenario: Suppose a user is traveling across time zones. Their underlying metabolic signals (decreased body temperature, reduced skin conductance fluctuations) indicate that they are in the late-night sleep phase of their home location, i.e. At a trough. At this moment, because the user is attending an important meeting and experiencing anxiety, the neural network model detects an exponential drop and attempts to generate a matrix of high-intensity arousal stimuli. To maintain their cognitive level. At this time, It will reach extremely high values.

[0175] Through the calculations in this embodiment, the exponential decay term will take effect quickly, and the final output will be... The intensity of the suppression was kept within a safe range. This alleviated the user's immediate anxiety while avoiding excessive interference with the hypothalamic rhythm, thus preventing potentially severe jet lag reactions later on.

[0176] like Figure 7 This diagram illustrates the polar coordinate mapping of biological rhythm phase angles and the time series plot of parameter-gated exponential modulation. The plot consists of two sub-regions, left and right.

[0177] The left side shows a polar coordinate mapping diagram. The blue solid line vector in the diagram points to the lowest trough position in the negative 90-degree direction. This objectively represents how the system extracts underlying metabolic signals such as body temperature change gradient and skin conductance fluctuation rate to calculate the current user's deep sleep rhythm phase in scenarios such as cross-time zone flight.

[0178] The right sub-region is a time series plot of parameter-gated exponential modulation. Its horizontal axis is named time in minutes, and its vertical axis is named stimulus parameter intensity in milliamperes.

[0179] In the right-hand figure, the red dashed curve represents the initial high-intensity arousal stimulus matrix generated by the AI ​​prediction model upon detecting user anxiety or fatigue. Its waveform fluctuates within a high range of approximately 2.5 mA throughout the 60-minute regulation cycle. Because this high-intensity intervention command severely conflicts with the deep sleep rhythm phase indicated by the left-hand polar coordinates, the system quantifies that this conflict exceeds the individual's specific rhythm tolerance threshold, thereby activating the parameter gating reconstruction mechanism.

[0180] In the right figure, the blue solid curve represents the actual stimulus parameter matrix after smooth reconstruction by the rhythm modulation function. The waveform transformation of the curve shows that when the intervention reaches the 10th minute, the blue solid line begins to deviate from the red dashed line and exhibits a significant exponential downward decay transformation because the rhythm phase conflict officially exceeds the threshold.

[0181] As time progressed to the 60th minute, the intensity of the actual stimulation parameters was smoothly suppressed to a safe range of approximately 0.2 mA.

[0182] By comparing the waveform trends of the red dashed line and the blue solid line, it can be concluded that the system can actively weaken the stimulation instructions that oppose the body's current basal metabolic rate when a conflict occurs. This effectively avoids the autonomic nervous system rebound resistance effect caused by forcibly applying high-dose electrical stimulation at an inappropriate biological clock phase, ensuring the safety and physiological compliance of all-weather long-cycle closed-loop regulation.

[0183] The solution provided in this embodiment is logically highly compatible with and complementary to the multimodal fingerprint extraction in Embodiment 1 and the impedance compensation mechanism in Embodiment 2. Impedance compensation solves the problem of accuracy in physical execution, while this rhythm gating solves the problem of rationality in biological logic. Together, they constitute a complete and secure strategy for hypothalamic targeted regulation.

[0184] To illustrate the effectiveness of this embodiment in light of existing technology: Traditional neuromodulation devices typically focus only on the instantaneous correspondence between signal input and target response, representing a forced-drive mode. This mode ignores the fact that the human body, as an organism with highly periodic fluctuations, experiences dynamic changes in receptor openness and metabolic tolerance across different rhythmic phases. When faced with rhythmic conflicts, existing technologies often attempt to suppress the body's rhythmic signals by simply increasing the intensity of the stimulation current. This not only leads to a surge in device power consumption but also easily triggers severe physiological stress responses.

[0185] This embodiment constructs a flexible gating modulation mechanism based on exponential decay by introducing two metabolic anchors: real-time body temperature gradient and skin conductance fluctuation rate. The practical significance of this approach lies in enabling AI-based modulation algorithms to adaptively assess physiological states. By aligning high-order neural state predictions with underlying biological rhythm phases in real time, the system can automatically identify and avoid commands that might cause autonomic nervous system resistance. This rhythm-adaptive matching strategy significantly improves the safety and human compliance of neuromodulation in all-weather operation, achieving a technological leap from forceful intervention to intelligent guidance, and providing robust rhythmic support for long-term, highly reliable closed-loop neuromodulation of the inner ear.

[0186] Example 4:

[0187] like Figure 8 As shown, this embodiment provides a physical execution device architecture that matches the method of the above embodiments, specifically an inner ear EEG closed-loop neuroelectric modulation system targeting the hypothalamus.

[0188] In actual operation and hardware implementation, the system adopts a highly integrated wearable medical electronic device form, mainly including an ear-hook main body structure. The weight of each ear-hook main body structure is configured to be less than a preset mass threshold, supporting independent or collaborative operation of both sides, and the shell has a high-level waterproof function.

[0189] To achieve precise closed-loop data control, the system's internal hardware circuitry comprises multiple dedicated functional sensors, an edge AI computing chip, a hardware-level stimulation artifact suppression circuit, a multi-channel stimulation driver, and wireless communication and power management modules. The following section provides an in-depth explanation of the system's internal structure and operating logic, focusing on specific functional modules and hardware execution entities.

[0190] Specifically, the inner ear EEG closed-loop neuroelectrophysiological modulation system targeting the hypothalamus described in this embodiment includes:

[0191] The initial model generation module is used to collect multimodal physiological signals, extract individual hypothalamic neural response fingerprints, and generate an initial personalized regulation model containing a hierarchical long short-term memory neural network.

[0192] In practical operation, the initial model generation module is physically connected to the control core of the edge AI computing chip through a multimodal physiological sensing module. The multimodal physiological sensing module integrates a skin conductance sensor, a body temperature sensor, and a triaxial accelerometer, and is attached to the skin surface around the user's ear or ear canal. The initial model generation module controls a constant current source to apply progressively increasing, step-by-step microcurrent stimulation to the user's auricular vagus nerve, Shenmen acupoint, sympathetic acupoint, and endocrine acupoint. Simultaneously, it collects EEG and skin conductance signals before and after the stimulation pulses, thereby measuring and recording the user's unique current perception threshold, current discomfort threshold, and frequency response inflection point. The transfer learning algorithm unit inside the edge AI computing chip converts these features into a standard-format data matrix, writes it into a read-only or erasable memory as an individual hypothalamic neural response fingerprint, and uses this fingerprint to fine-tune the parameters of a general pre-trained basic neural model, ultimately reconstructing and generating an initial personalized control model containing a hierarchical long short-term memory neural network.

[0193] The EEG extraction module is used to simultaneously acquire inner ear EEG signals and the aforementioned multimodal physiological signals, and extract pure hypothalamic projection area EEG signals through a hardware-level stimulation artifact suppression circuit.

[0194] At the hardware delivery level, the EEG extraction module includes a flexible multi-channel electrode array for attaching to the user's ear canal and mastoid region, a high-impedance preamplifier, an analog switch array, a high-speed analog-to-digital converter, and the aforementioned hardware-level stimulation artifact suppression circuit. When the electrical stimulation controller outputs therapeutic stimulation pulses, its internal clock synchronization unit sends a synchronization trigger signal to the hardware-level stimulation artifact suppression circuit.

[0195] The hardware-level stimulation artifact suppression circuit is designed based on the principles of hardware synchronous latching and adaptive cancellation. It uses a high-speed analog switch to activate a high-speed differential sampling channel during the instantaneous discharge silence period of the stimulation pulse, continuously sampling to capture weak voltage potentials. Simultaneously, its internal polarization artifact isolation unit utilizes a high-pass capacitive coupling network to block the DC offset voltage generated by electrode polarization. Finally, a dynamic hardware low-pass filter network adaptively adjusts the cutoff frequency to a preset range based on the current output frequency of the electrical stimulation, outputting a clean hypothalamic projection area EEG signal with a signal-to-noise ratio conforming to preset specifications, which is then provided to the edge AI computing chip for decoding.

[0196] The index calculation module is used to normalize the pure hypothalamic projection area EEG signal and the multimodal physiological signal based on the individual's hypothalamic neural response fingerprint, and calculate the individual's relative hypothalamic functional comprehensive index.

[0197] Specifically, the index calculation module is deployed in the arithmetic logic unit of the edge AI computing chip. During actual operation, the module periodically reads the physiological baseline data from the user's unique individual hypothalamic neural response fingerprint from the flash memory. Then, it performs feature normalization processing on the real-time input pure hypothalamic projection area EEG signals and multimodal physiological signals to eliminate dimensional differences in absolute physiological values ​​among different users, generating relative scores for emotion, stress, sleep, and cognition dimensions. Finally, the module retrieves adaptive weights controlled by the individual fingerprint, performs real-time calculations using an algebraic weighted summation formula, and outputs a scalar-form individual relative hypothalamic functional comprehensive index.

[0198] The state prediction module is used to input the individual's relative hypothalamic functional comprehensive index into the hierarchical long short-term memory neural network to predict the state change trend and risk probability.

[0199] In actual hard inference, the state prediction module relies on the neural network inference accelerator built into the edge AI computing chip. The hierarchical long short-term memory neural network employs a hierarchical hardware and software storage layout: a general basic layer is stored in the chip's read-only memory, responsible for processing general neural state evolution patterns; the individual-specific layer is stored in the chip's erasable non-volatile memory, responsible for responding to the individual's unique patterns. The state prediction module inputs a multi-dimensional feature matrix composed of multimodal physiological data from past preset periods, the current individual's relative hypothalamic functional composite index, fingerprint database features, and stimulus matching features into the network. Utilizing the network's temporal memory gating chain, it predicts the trend of the individual's relative hypothalamic functional composite index and the probability of psychological safety risks within a future preset window period. The chip's edge inference latency is configured to be relatively low, ensuring the immediacy of the prediction response.

[0200] The parameter matrix generation module is used to generate a multimodal co-stimulation parameter matrix containing independent parameters of multiple auricular acupoints based on the current relative hypothalamic functional comprehensive index of the individual, the trend of state change, and the individual's hypothalamic neural response fingerprint.

[0201] Specifically, this parameter matrix generation module physically corresponds to the multimodal collaborative control engine within the edge AI computing chip. This engine has a built-in three-level parameter generation unit. The initial parameter generation unit, as the first level, is responsible for retrieving historically optimal control parameters from the fingerprint database that closely match the current comprehensive index. The safety constraint adjustment unit, as the second level, is responsible for retrieving perception and discomfort thresholds from the fingerprint database, performing boundary trimming on the initial parameters, and multiplying them by a product of EEG stability coefficient, physiological tolerance coefficient, and behavioral stability coefficient for triple safety constraint limitation. The real-time fine-tuning unit, as the third level, is responsible for periodically reading the latest physiological feedback indicators and performing micro-fine adjustments to the parameters of electrical and acoustic stimulation according to preset step sizes. Finally, it generates and outputs the multimodal collaborative stimulation parameter matrix in the register, which includes independent waveforms, frequencies, pulse widths, and intensities of the auricular vagus nerve, Shenmen acupoint, sympathetic acupoint, and endocrine acupoint, as well as the acoustic control type, volume, and rhythm.

[0202] The output control module is used to synchronously output electrical and acoustic stimulation according to the multimodal synergistic stimulation parameter matrix for closed-loop targeted control.

[0203] The output control module consists of a multi-channel independent electrical stimulation controller and a dual-mode acoustic module. The electrical stimulation controller is connected to the auricular nerve stimulation module and internally includes a digital-to-analog converter chip and a high-precision numerically controlled constant current source circuit. It can receive electrical characteristic commands from the parameter matrix and output microcurrent physical pulses of preset intensity levels to multiple preset auricular acupoints, such as the vagus nerve. Simultaneously, the dual-mode acoustic module employs a hybrid bone conduction and air conduction technology to drive a built-in miniature bone conduction speaker and air conduction earplugs, synchronously outputting acoustic control signals. The physical current and acoustic waveforms generate spatiotemporal synergy in the ear and the vagus nerve pathway of the inner ear, ultimately achieving closed-loop targeted control of the hypothalamic autonomic nerve center.

[0204] It is also important to note that when the hardware executes electrical stimulation output, a dynamic current targeting compensation unit is integrated into the output control module to compensate for impedance fluctuations caused by dynamic microenvironments such as sweating and micro-movements in the external ear canal. This unit synchronously injects a high-frequency subthreshold monitoring carrier during the electrical control cycle to measure complex impedance and uses an equivalent circuit algorithm to separate and calculate the bypass shunt impedance on the ear canal surface and the deep penetration impedance of the target nerve. Finally, the output command is reconstructed through a dynamic current compensation model, enabling the real-time current emitted by the electrical stimulation controller to adaptively adjust according to the impedance ratio. This ensures that even in highly humid or frictionally slippery working environments, the actual current acting on the hypothalamic nerve projection area remains stably configured equal to the target stimulation intensity issued in the multimodal co-stimulation parameter matrix.

[0205] The fingerprint and model update module is used to collect neural response data after intervention, perform online self-calibration to update the individual hypothalamic neural response fingerprint, and aggregate model parameters to update gradients through a federated learning framework to update the hierarchical long short-term memory neural network.

[0206] At the hardware self-evolution level, this module corresponds to the online self-calibration unit and federated learning control unit within the edge AI computing chip. The online self-calibration unit operates automatically when the user is in a stable state and there is no need for active intervention, periodically calculating the similarity $S$ between the current neural response and the fingerprint baseline. If the similarity deviates slightly or moderately, it automatically triggers daily calibration or periodic deep calibration stimulus sequences to update the dynamic response features and multimodal collaborative features in the fingerprint database; long-term plasticity calibration updates long-term plasticity features based on long-term statistical historical data.

[0207] Meanwhile, the federated learning control unit includes a local incremental learning unit, a gradient encryption unit, and a cloud communication component. When the device is charging or idle, the local incremental learning unit trains the individual-specific layers of the hierarchical long short-term memory neural network to optimize local weights and generate model parameter update gradients. Subsequently, the gradient encryption unit performs differential privacy encryption on the gradient vector and uploads it individually to the cloud server via a wireless communication network. The cloud aggregation unit uses a federated averaging algorithm to aggregate the encrypted gradients uploaded by all terminals, updates the general base layer model, and periodically pushes and distributes it back to the local terminal, where it is automatically loaded and reconstructed and merged with the individual-specific layers. The entire update process does not upload any user's original private EEG or physiological waveform data, enabling continuous iterative updates of the personalized control model.

[0208] The inner ear EEG closed-loop neuroelectrophysiological modulation system targeting the hypothalamus provided in this embodiment comprehensively implements digital signal processing algorithms, deep learning prediction networks, and adaptive feedback regulation mechanisms into specific physical execution entities such as flexible electrodes, hardware artifact suppression circuits, high-precision constant current sources, and edge AI chips. The system achieves precise calculation of high-order control commands through an exponential calculation module and a parameter matrix generation module, and achieves precise delivery of physical doses through an output regulation module with a dynamic current compensation unit. Finally, driven by the closed-loop fingerprint and model update modules, this hardware system can balance complex ear canal environmental interference and deviation risks in practical long-term wearable applications, maintaining regulation fidelity, long-term environmental adaptability, and operational safety while ensuring user data privacy, demonstrating significant industrial application and product commercialization potential.

[0209] Example 5:

[0210] Corresponding to the above embodiments, the present invention also proposes an electronic device.

[0211] like Figure 9 The diagram shows a structural schematic of an electronic device according to the present invention. The electronic device 100 includes a processor 101 and a memory 103. The processor 101 and the memory 103 are connected, for example, via a bus 102. Optionally, the electronic device 100 may further include a transceiver 104. It should be noted that in practical applications, the transceiver 104 is not limited to one unit, and the structure of this electronic device 100 does not constitute a limitation on the embodiments of the present invention.

[0212] Processor 101 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. Processor 101 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0213] Bus 102 may include a pathway for transmitting information between the aforementioned components. Bus 102 may be a PCI bus or an EISA bus, etc. Bus 102 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0214] The memory 103 stores a computer program corresponding to the inner ear EEG closed-loop neuroelectrophysiological modulation method targeting the hypothalamus in the above embodiments of the present invention. This computer program is executed under the control of the processor 101. The processor 101 executes the computer program stored in the memory 103 to implement the content shown in the aforementioned method embodiments.

[0215] Among them, electronic devices 100 include, but are not limited to: mobile terminals such as laptops and PADs (tablet computers) and fixed terminals such as desktop computers. Figure 9 The electronic device 100 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0216] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. An inner ear electroencephalic closed-loop neuroelectric modulation method targeting the hypothalamus, characterized in that, include: Collect multimodal physiological signals, extract individual hypothalamic neural response fingerprints, and generate an initial personalized regulation model containing a hierarchical long short-term memory neural network; The inner ear EEG signals and the aforementioned multimodal physiological signals were acquired simultaneously, and pure hypothalamic projection area EEG signals were extracted through a hardware-level stimulation artifact suppression circuit. Based on the individual hypothalamic neural response fingerprint, the pure hypothalamic projection area EEG signal and the multimodal physiological signal are normalized to calculate the individual relative hypothalamic functional comprehensive index. The individual's relative hypothalamic function index is input into the hierarchical long short-term memory neural network to predict the trend of state change and the probability of risk. Based on the current individual relative hypothalamic functional comprehensive index, the state change trend, and the individual hypothalamic neural response fingerprint, a multimodal co-stimulation parameter matrix containing independent parameters of multiple auricular acupoints is generated. Closed-loop targeted modulation is achieved by synchronously outputting electrical and acoustic stimulation according to the multimodal synergistic stimulation parameter matrix. The neural response data collected after intervention are used to perform online self-calibration to update the individual hypothalamic neural response fingerprint, and the gradients of the model parameters are updated through a federated learning framework to update the hierarchical long short-term memory neural network.

2. The method according to claim 1, characterized in that, The individual hypothalamic neural response fingerprint consists of basic threshold features, dynamic response features, multimodal synergistic features, and long-term plasticity features, specifically including: The basic threshold features include the current sensing threshold, current discomfort threshold, and frequency response inflection point of the multiple auricular acupoints; The dynamic response characteristics include the rate of change of the relative hypothalamic functional index of the multiple auricular acupoints under different stimulation parameters, peak response time, effect duration, and recovery time constant. The multimodal synergistic features include the synergistic enhancement coefficient of the preset acoustic stimulation mode and electrical stimulation, the synchronization phase difference, and the volume intensity matching curve. The long-term plasticity characteristics include the rate of change of the basic threshold, the rate of change of dynamic response sensitivity, and the synergistic effect decay coefficient.

3. The method according to claim 1, characterized in that, The extraction of pure hypothalamic projection area EEG signals via a hardware-level stimulation artifact suppression circuit includes: While outputting each preset intensity level of electrical stimulation pulse, a synchronous trigger signal is sent to the hardware-level stimulation artifact suppression circuit. The silence period of the electrical stimulation pulse is determined according to the synchronous trigger signal. The high-speed differential sampling channel is activated for continuous sampling after the falling edge of the electrical stimulation pulse and the channel is closed before the rising edge. Based on the frequency and intensity of the current electrical stimulation pulse, the cutoff frequency of the dynamic hardware low-pass filter network is adaptively adjusted to the preset cutoff frequency range. The polarization artifact isolation unit, which combines capacitive coupling and differential amplification, isolates the DC offset voltage generated by electrode polarization and outputs the pure electroencephalogram signal from the hypothalamic projection area.

4. The method according to claim 1, characterized in that, The individual's relative hypothalamic function index is calculated using the following formula: ； in: Defined as the individual's relative hypothalamic function comprehensive index, whose value is within a preset normalized value range; , , , The relative scores are, in order, the relative scores of the emotion dimension, the stress dimension, the sleep dimension, and the cognition dimension, and the relative scores are normalized based on the physiological baseline in the individual's hypothalamic neural response fingerprint. , , , These are defined as adaptive weights for the four dimensions mentioned above, and their values ​​are controlled by the individual's hypothalamic neural response fingerprint.

5. The method according to claim 2, characterized in that, The hierarchical long short-term memory neural network adopts a two-layer architecture, specifically including: General base layer: contains a front-end long short-term memory network layer trained on global anonymous data in the cloud, used to extract global state change patterns; Individual-specific layer: contains a post-long short-term memory network layer trained on the user's local data, used to learn the individual's neural response patterns; The input feature matrix of the two-layer architecture includes multimodal physiological data from a preset historical period, the dynamic response features, the long-term plasticity features, and the matching degree features between the current stimulus parameters and the optimal parameters.

6. The method according to claim 2, characterized in that, The generation of a multimodal co-stimulation parameter matrix containing independent parameters of multiple auricular acupoints is performed using a three-level parameter generation mechanism, including: First-level parameter generation: Match the historical best parameter combination corresponding to the relative hypothalamic functional comprehensive index of the current individual from the fingerprint database to generate the initial parameter matrix; Secondary parameter adjustment: Based on the basic threshold features and the long-term plasticity features, the initial parameter matrix is ​​subjected to security verification, and then multiplied by a product containing the EEG stability coefficient, physiological tolerance coefficient and behavioral stability coefficient for constraint adjustment; Level 3 parameter fine-tuning: Based on the high-frequency feedback index, the constrained adjusted matrix is ​​finely adjusted by a preset step size, and the multimodal co-stimulation parameter matrix is ​​output.

7. The method according to claim 2, characterized in that, The online self-calibration is based on a three-level calibration mechanism, including: Routine calibration: Triggered when the similarity between the calculated current neural response and the fingerprint baseline is within a first preset similarity range, parameter adjustments with a preset step size are inserted to update the dynamic response features; Periodic deep calibration: triggered when the similarity is within a second preset similarity interval or the intervention effect score is lower than a preset value, a standardized stimulus sequence is executed to update the multimodal collaborative features and the dynamic response features; the value of the first preset similarity interval is greater than the value of the second preset similarity interval; Long-term plasticity calibration: Based on the set long-term statistical neural response data, the long-term plasticity characteristics are updated and the baseline of stimulation parameters for future cycles is adaptively adjusted.

8. The method according to claim 7, characterized in that, The similarity required to trigger the three-level calibration mechanism is calculated using the following formula: ； in: The similarity between the current neural response and the fingerprint baseline; The total number of features in the individual's hypothalamic neural response fingerprint is a positive integer greater than 1. For the newly collected Neural response eigenvalues; For the fingerprint database Historical mean of the feature; For the fingerprint database The standard deviation of a feature is used to measure the historical fluctuation range of that feature. For extremely small system positive anti-singularity constants; For the first The weight coefficients of the features.

9. The method according to claim 5, characterized in that, The step of updating the gradient by aggregating model parameters through a federated learning framework to update the hierarchical long short-term memory neural network includes: Incremental learning is performed on the individual-specific layer locally to generate model parameter update gradients with differential privacy encryption properties; The encrypted model parameter update gradient is uploaded separately to the cloud, and the cloud uses a federated averaging algorithm to perform multi-terminal gradient aggregation to update the general base layer. The local device receives the updated general base layer from the cloud and merges it with the individual-specific layer to complete the control model update.

10. A closed-loop neuroelectrophysiological modulation system for the inner ear targeting the hypothalamus, characterized in that, include: The initial model generation module is used to collect multimodal physiological signals, extract individual hypothalamic neural response fingerprints, and generate an initial personalized regulation model containing a hierarchical long short-term memory neural network. The EEG extraction module is used to simultaneously acquire inner ear EEG signals and the multimodal physiological signals, and extract pure hypothalamic projection area EEG signals through a hardware-level stimulation artifact suppression circuit. The index calculation module is used to normalize the pure hypothalamic projection area EEG signal and the multimodal physiological signal based on the individual's hypothalamic neural response fingerprint, and calculate the individual's relative hypothalamic functional comprehensive index. The state prediction module is used to input the individual's relative hypothalamic functional comprehensive index into the hierarchical long short-term memory neural network to predict the state change trend and risk probability. The parameter matrix generation module is used to generate a multimodal co-stimulation parameter matrix containing independent parameters of multiple auricular acupoints based on the current relative hypothalamic functional comprehensive index of the individual, the trend of state change, and the individual's hypothalamic neural response fingerprint. The output control module is used to synchronously output electrical and acoustic stimulation according to the multimodal co-stimulation parameter matrix for closed-loop targeted control. The fingerprint and model update module is used to collect neural response data after intervention, perform online self-calibration to update the individual hypothalamic neural response fingerprint, and aggregate model parameters to update gradients through a federated learning framework to update the hierarchical long short-term memory neural network.

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