Method and device for determining discharge parameters of neural electrode

Abstract

The invention provides a nerve electrode discharge parameter determination method and device, relates to the technical field of electric signal regulation and control, and solves the technical problems that in the existing nerve electrical stimulation technology, a traditional electrode is insufficient in targeting due to electric field distribution dispersion, target nerve fibers cannot be accurately activated, and excitation of a non-target area is easily caused. The method specifically comprises the following steps: acquiring a first position coordinate of a target nerve fiber, a second position coordinate of a non-target nerve fiber and a third position coordinate of a microelectrode; determining a first distance between the first position coordinate and the third position coordinate, and determining a second distance between the second position coordinate and the third position coordinate; based on the first spacing and the second spacing, the frequency and the phase of current emitted by the microelectrode when preset conditions are met are determined, and the preset conditions include that the target nerve fibers are successfully activated, and non-target nerve fibers are inhibited. The method is used in the process of determining the discharge parameters of the neural electrode.

Description

Technical Field

[0001] The present application relates to the technical field of electrical signal regulation, and in particular to a method and device for determining discharge parameters of a neural electrode. Background Art

[0002] As an important means of modern clinical treatment, neuroelectrical stimulation technology has demonstrated irreplaceable value in the fields of motor function rehabilitation, chronic pain management, and intervention of neurological diseases. Its core therapeutic logic is to release electrical pulses to the target nerve fibers through electrode carriers, accurately induce nerve impulses to rebuild the nerve signal conduction pathway. However, the traditional neuroelectrical stimulation technology currently used in clinical practice is based on traditional electrodes with metal wire or plate structures. When releasing current in the tissue, the electric field distribution has a natural diffusion characteristic, which makes it difficult to accurately control the range of electrical stimulation. This technical limitation causes the current to often activate the target nerve fibers and surrounding non-target tissues at the same time, which not only reduces the activation efficiency of specific nerve fibers, but also may cause unexpected physiological reactions such as involuntary muscle contraction and paresthesia. Therefore, the existing neuroelectrical stimulation technology has the technical problem that the traditional electrodes are not targeted enough due to the diffuse electric field distribution, and are unable to accurately activate the target nerve fibers and are prone to excitation of non-target areas. Summary of the Invention

[0003] The present application provides a method and device for determining the discharge parameters of a neural electrode, which solves the technical problems of existing neural electrical stimulation technology, that is, traditional electrodes have insufficient targeting due to diffuse electric field distribution, cannot accurately activate target nerve fibers, and are prone to causing excitement in non-target areas.

[0004] To achieve the above objectives, this application adopts the following technical solutions:

[0005] In a first aspect, a method for determining the discharge parameters of a neural electrode is provided, comprising: obtaining a first position coordinate of a target nerve fiber and a second position coordinate of a non-target nerve fiber, wherein the target nerve fiber is a nerve fiber to be activated, and the non-target nerve fiber is a nerve fiber located in an adjacent area of ​​the same nerve trunk as the target nerve fiber, which does not need to be activated and whose excitation needs to be suppressed.

[0006] A microelectrode for stimulating nerve fibers is deployed and the third position coordinates of the microelectrode are recorded.

[0007] A first distance between the first position coordinate and the third position coordinate is determined, and a second distance between the second position coordinate and the third position coordinate is determined.

[0008] The frequency and phase of the current emitted by the microelectrode when a preset condition is met are determined based on the first spacing and the second spacing, wherein the preset condition includes: successfully activating target nerve fibers and inhibiting non-target nerve fibers.

[0009] In conjunction with the first aspect above, in one possible implementation, determining the frequency and phase of the current emitted by the microelectrode when a preset condition is met based on the first spacing and the second spacing includes:

[0010] A first expression is determined based on the first spacing, the frequency coding factor, and the phase coding factor; the first expression is used to characterize the current value of the microelectrode current reaching the target nerve fiber at different frequencies and phases; the frequency coding factor is a nonlinear weight factor designed based on the optimal response frequency of the target nerve fiber, and the phase coding factor is a factor used to regulate the phase difference of each microelectrode signal at the target fiber.

[0011] A second expression is determined based on the second spacing, the frequency coding factor and the phase difference; the second expression is used to characterize the current value of the microelectrode current reaching the non-target nerve fiber at different frequencies and phases; the phase difference is the phase difference that suppresses the current intensity of the non-target nerve fiber due to destructive interference.

[0012] The joint objective function of the first and second expressions is determined. The frequency and phase of the current emitted by each microelectrode in the joint objective function are iteratively adjusted using a gradient descent method, so that the current value calculated based on the first expression reaches the activation threshold and the current value calculated based on the second expression is below the safety threshold. The activation threshold is the current value that the target nerve fiber must reach to successfully activate; the safety threshold is the current value that the non-target nerve fiber must fall below to inhibit its excitation.

[0013] In conjunction with the first aspect above, in one possible implementation, initializing the frequency and phase of the current emitted by the microelectrode includes:

[0014] The frequency of the current emitted by each microelectrode is ω i =ω0+Δω·log2(i+1) increasing, where ω i is the frequency of the current emitted by each microelectrode, ω0 is the set basic frequency, Δω is the frequency step, i=1,…,Q is the index of the microelectrode, and Q is the number of microelectrodes.

[0015] The phase of the current emitted by each microelectrode is Calculate, where is the phase of the current emitted by each microelectrode, d i is the distance from each microelectrode to the nerve fiber, λ i is the wavelength.

[0016] In combination with the first aspect above, in a possible implementation, the frequency coding factor satisfies the following formula:

[0017]

[0018] Among them, ω optis the optimal response frequency of the target nerve fiber, k i is the frequency coding factor.

[0019] In combination with the first aspect above, in one possible implementation, the phase coding factor satisfies the following formula:

[0020]

[0021] in, is the desired phase of the target nerve fiber, p i is the phase encoding factor.

[0022] In conjunction with the first aspect above, in one possible implementation, determining the first expression and the second expression includes:

[0023] The first expression satisfies the following formula:

[0024]

[0025] Where T represents the first position coordinate of the target nerve fiber, t represents the time variable, and I i0 is the initial current amplitude of the i-th microelectrode, r i is the first distance between each microelectrode and the target nerve fiber.

[0026] The second expression satisfies the following formula:

[0027]

[0028] Where S represents the second position coordinate of the non-target nerve fiber, is the phase difference between the microelectrode to the first position coordinate T and to the second position coordinate S, is the second distance between each microelectrode and the non-target nerve fiber.

[0029] In combination with the first aspect above, in one possible implementation, the joint objective function F satisfies the following formula:

[0030]

[0031] Where α represents the first weighted coefficient for optimizing the target nerve fiber, β represents the second weighted coefficient for optimizing the non-target nerve fiber, and I th is the activation threshold, is the safety threshold.

[0032] In combination with the first aspect above, in one possible implementation, iteratively adjusting the current frequency and phase of each microelectrode based on a gradient descent method includes:

[0033] The gradient of the joint objective function with respect to the frequency and phase of the current emitted by each microelectrode is calculated.

[0034] Update the frequency and phase of the current emitted by each microelectrode.

[0035] The difference between the joint objective function of the current iteration and the previous iteration and the set threshold are judged. If the difference is less than the set threshold, the convergence condition is met and the iteration is stopped. If the difference is greater than the set threshold, the gradient is continued to be calculated and the frequency and phase of the current emitted by each microelectrode are updated.

[0036] In conjunction with the first aspect above, in one possible implementation, updating the frequency and phase of the current emitted by each microelectrode includes:

[0037] The frequency update process of the current emitted by each microelectrode satisfies the following formula:

[0038]

[0039] Where m represents the iteration index of the frequency update of the current emitted by each microelectrode, η ω The step size for updating the frequency of the current emitted by each microelectrode.

[0040] The phase update process of the current emitted by each microelectrode satisfies the following formula:

[0041]

[0042] Where l represents the iteration index of the current phase update of each microelectrode, The step size for updating the current phase emitted for each microelectrode.

[0043] In a second aspect, a device for determining neural electrode discharge parameters is provided, comprising: a communication unit and a processing unit; the communication unit is configured to obtain a first position coordinate of a target neural fiber and a second position coordinate of a non-target neural fiber, wherein the target neural fiber is a neural fiber to be activated, and the non-target neural fiber is a neural fiber located in an adjacent area of ​​the same nerve trunk as the target neural fiber, which does not need to be activated and whose excitation needs to be suppressed; the processing unit is configured to deploy a microelectrode for stimulating the neural fiber and record the third position coordinate of the microelectrode. A first spacing between the first position coordinate and the third position coordinate is determined, and a second spacing between the second position coordinate and the third position coordinate is determined. Based on the first spacing and the second spacing, the frequency and phase of the current emitted by the microelectrode when a preset condition is met are determined, wherein the preset condition includes: successful activation of the target neural fiber and inhibition of the non-target neural fiber.

[0044] In a third aspect, the present application provides a device for determining neural electrode discharge parameters, comprising: a processor and a storage medium; the storage medium comprising instructions, the processor configured to execute the instructions to implement the method described in the first aspect and any possible implementation of the first aspect. The device for determining neural electrode discharge parameters may be an electronic device or a chip within an electronic device.

[0045] In a fourth aspect, the present application provides a neural electrode discharge parameter determination system, comprising: a microelectrode, and an electronic device; the microelectrode is used to obtain a first position coordinate of a target nerve fiber and a second position coordinate of a non-target nerve fiber, the target nerve fiber being the nerve fiber to be activated, and the non-target nerve fiber being the nerve fiber located in an adjacent area of ​​the same nerve trunk as the target nerve fiber, which does not need to be activated and whose excitation needs to be suppressed, and the electronic device is used to deploy a microelectrode for stimulating the nerve fiber and record the third position coordinate of the microelectrode; determine a first distance between the first position coordinate and the third position coordinate, and determine a second distance between the second position coordinate and the third position coordinate; determine the frequency and phase of the current emitted by the microelectrode when a preset condition is met based on the first distance and the second distance, wherein the preset condition includes: successfully activating the target nerve fiber and inhibiting the non-target nerve fiber.

[0046] In a fifth aspect, the present application provides a computer-readable storage medium, which stores instructions. When the instructions are run on a neural electrode discharge parameter determination device, the neural electrode discharge parameter determination device performs the method described in the first aspect and any possible implementation of the first aspect.

[0047] In a sixth aspect, the present application provides a computer program product comprising instructions, which, when run on a neural electrode discharge parameter determination device, enables the neural electrode discharge parameter determination device to perform the method described in the first aspect and any possible implementation of the first aspect.

[0048] The present application provides a method and device for determining the discharge parameters of neural electrodes, which designs a nonlinear frequency coding factor based on the optimal response frequency of the target nerve fiber, and controls the spatial phase difference in combination with the phase coding factor. The current is superimposed to the activation threshold in the target area, and the phase difference is used to achieve destructive interference in the non-target area to suppress the current intensity. The frequency and phase parameters of the current emitted by each electrode are iteratively optimized by the gradient descent method, which can dynamically balance the current scaling degree of the target point and the current suppression degree of the non-target point, thereby improving the activation efficiency of the target nerve fiber while effectively avoiding the generation of nerve impulses in non-target areas. Therefore, through the joint frequency and phase coding and gradient optimization mechanism, the targeting accuracy and safety of neural electrical signal regulation can be improved, solving the technical problems of existing neural electrical stimulation technology, such as the lack of targeting of traditional electrodes due to the diffuse electric field distribution, the inability to accurately activate the target nerve fibers, and the easy induction of excitement in non-target areas.

[0049] It should be understood that the description of technical features, technical solutions, beneficial effects or similar language in this application does not imply that all features and advantages can be realized in any single embodiment. On the contrary, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution or beneficial effect is included in at least one embodiment. Therefore, the description of a technical feature, technical solution or beneficial effect in this specification does not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions and beneficial effects described in the present embodiment can also be combined in any appropriate manner. Those skilled in the art will understand that the embodiment can be implemented without one or more specific technical features, technical solutions or beneficial effects of a specific embodiment. In other embodiments, additional technical features and beneficial effects can also be identified in specific embodiments that do not embody all embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A system architecture diagram of a neural electrode discharge parameter determination system provided in an embodiment of the present application;

[0051] Figure 2 A flowchart of a method for determining neural electrode discharge parameters provided in an embodiment of the present application;

[0052] Figure 3 A flowchart of another method for determining neural electrode discharge parameters provided in an embodiment of the present application;

[0053] Figure 4 A flowchart of another method for determining neural electrode discharge parameters provided in an embodiment of the present application;

[0054] Figure 5 A schematic structural diagram of a device for determining neural electrode discharge parameters provided in an embodiment of the present application;

[0055] Figure 6 A schematic diagram of the hardware structure of a device for determining neural electrode discharge parameters provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] In the description of this application, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "at least one" means one or more, and "a plurality" means two or more. Words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not limit them to be necessarily different.

[0057] It should be noted that, in this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described in this application as "exemplary" or "for example" should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0058] The method for determining the discharge parameters of a neural electrode provided in the embodiment of the present application can be applied to Figure 1 In the neural electrode discharge parameter determination system shown, the system includes: a microelectrode 101 and an electronic device 102.

[0059] Among them, the microelectrode 101 is used to obtain the first position coordinates of the target nerve fiber and the second position coordinates of the non-target nerve fiber. The target nerve fiber is the nerve fiber to be activated, and the non-target nerve fiber is the nerve fiber located in the adjacent area of ​​the same nerve trunk as the target nerve fiber, which does not need to be activated and whose excitation needs to be suppressed. The electronic device 102 is used to deploy the microelectrode for stimulating the nerve fiber and record the third position coordinates of the microelectrode. Determine the first spacing between the first position coordinate and the third position coordinate, and determine the second spacing between the second position coordinate and the third position coordinate. Based on the first spacing and the second spacing, determine the frequency and phase of the current emitted by the microelectrode when the preset conditions are met, wherein the preset conditions include: successfully activating the target nerve fiber and suppressing the non-target nerve fiber.

[0060] In order to solve the technical problem in existing neural electrical stimulation technology that traditional electrodes are insufficiently targeted due to the diffuse distribution of electric field, cannot accurately activate target nerve fibers, and easily induce excitation of non-target areas, an embodiment of the present application provides a method for determining the discharge parameters of neural electrodes, the method comprising: obtaining the first position coordinates of the target nerve fiber and the second position coordinates of the non-target nerve fiber, the target nerve fiber being the nerve fiber to be activated, and the non-target nerve fiber being the nerve fiber located in the adjacent area of ​​the same nerve trunk as the target nerve fiber, which does not need to be activated and whose excitation needs to be suppressed. Deploy a microelectrode for stimulating the nerve fiber and record the third position coordinates of the microelectrode. Determine the first spacing between the first position coordinate and the third position coordinate, and determine the second spacing between the second position coordinate and the third position coordinate. Based on the first spacing and the second spacing, determine the frequency and phase of the current emitted by the microelectrode when the preset conditions are met, wherein the preset conditions include: successfully activating the target nerve fiber and suppressing the non-target nerve fiber. Based on this, through the frequency, phase joint encoding and gradient optimization mechanism, the targeting accuracy and safety of neural electrical signal regulation can be improved, solving the technical problems of existing neural electrical stimulation technology that traditional electrodes have insufficient targeting due to the diffuse electric field distribution, cannot accurately activate target nerve fibers, and are prone to causing excitement in non-target areas.

[0061] like Figure 2 As shown, the method for determining the discharge parameters of a neural electrode provided in the embodiment of the present application includes:

[0062] Step 201: The nerve electrode discharge parameter determination device obtains the first position coordinates of the target nerve fiber and the second position coordinates of the non-target nerve fiber.

[0063] Among them, the target nerve fibers are specific nerve fibers to be activated, and the non-target nerve fibers are nerve fibers in the adjacent area of ​​the same nerve trunk that do not need to be activated and whose excitement needs to be suppressed. The position coordinates use a three-dimensional spatial coordinate system to represent the spatial positioning of nerve fibers in human tissue.

[0064] In an embodiment of the present application, the device for determining the discharge parameters of a neural electrode may scan the nerve trunk area through medical imaging technology and extract the coordinates through image reconstruction, and the present application does not make any specific limitation on this.

[0065] It should be pointed out that the position coordinates of nerve fibers are the spatial positioning parameters of the nerve fiber cross section, that is, the center point of the nerve fiber cross section is used as the coordinate reference point. The coordinate acquisition accuracy must meet the clinical micron requirements. Non-target nerve fibers are usually selected from adjacent fibers within 1-10 microns from the target nerve fibers to evaluate non-target effects. The nerve electrode discharge parameter determination device also supports the simultaneous acquisition of multi-target nerve fiber coordinates.

[0066] As an example, the neural electrode discharge parameter determination device first performs an MRI scan on the target nerve fiber, identifies the target nerve fiber coordinates T(x0, y0, z0) through an image segmentation algorithm, selects adjacent nerve fibers within a 5 micron range as non-target nerve fibers and marks the coordinates S(x s ,y s ,z s ).

[0067] Step 202: The neural electrode discharge parameter determination device deploys microelectrodes for stimulating nerve fibers and records the third position coordinates of the microelectrodes.

[0068] Among them, the microelectrode refers to an electrode array with a micron-level diameter, and its structure can be a microneedle-like protrusion or a thin cylindrical sheet style, which can be attached to the surface of the nerve trunk. This application does not make specific restrictions on this; the third position coordinate is the positioning parameter of the microelectrode in three-dimensional space, which is used to characterize the spatial position relationship of the microelectrode relative to the nerve fiber.

[0069] In an embodiment of the present application, the neural electrode discharge parameter determination device can deploy microelectrodes around the nerve trunk by implantation, for example, by bringing the microelectrode within 10 microns of the target nerve fiber under image guidance, and at the same time using an optical positioning system or imaging equipment to record the third position coordinates of the microelectrode.

[0070] It should be pointed out that the positioning accuracy of microelectrode deployment must reach the micron level to ensure targeted stimulation, and single electrodes or multi-electrode arrays can be deployed according to needs. When deploying multiple electrodes, the electrode spacing must be considered to avoid electric field coupling effects.

[0071] Step 203: The neural electrode discharge parameter determination device determines a first distance between the first position coordinate and the third position coordinate, and determines a second distance between the second position coordinate and the third position coordinate.

[0072] The spacing is calculated based on the Euclidean distance formula between two points in a three-dimensional space coordinate system and is used to characterize the spatial position relationship between the microelectrode and the nerve fiber.

[0073] In an embodiment of the present application, the apparatus for determining the discharge parameters of neural electrodes obtains coordinates using medical imaging equipment and then calculates the three-dimensional Euclidean distance using a built-in algorithm. This application does not make any specific limitations on this.

[0074] Step 204: The neural electrode discharge parameter determination device determines the frequency and phase of the current emitted by the microelectrode when the preset conditions are met based on the first spacing and the second spacing.

[0075] Among them, the preset conditions include: successful activation of the target nerve fibers and inhibition of non-target nerve fibers; the current frequency refers to the number of oscillations of the electrical signal per unit time (unit Hz), and the phase is the initial offset of the electrical signal waveform (unit rad). The preset conditions refer to the regulation of electrical signals so that the current at the target nerve fibers reaches the activation threshold and the current at the adjacent non-target nerve fibers is lower than the safety threshold.

[0076] In an embodiment of the present application, a device for determining the discharge parameters of neural electrodes constructs a mathematical model of the spatial electric field distribution, combines the first spacing and the second spacing with the electrophysiological characteristics of nerve fibers (such as the optimal response frequency and excitation threshold of the target fiber), and uses a gradient descent optimization algorithm to iteratively calculate the frequency and phase combination of the current emitted by each electrode that meets the preset conditions, so that the calculated current parameters can exceed the activation threshold at the target fiber through superposition, and can suppress the current intensity at non-target points through phase difference.

[0077] It should be pointed out that the coordinated regulation of frequency and phase needs to take into account the attenuation characteristics of the electrical signals emitted by each microelectrode reaching the target nerve fibers and non-target nerve fibers.

[0078] As an example, the current at the target nerve fiber is superimposed to 110 μA (exceeding the 100 μA activation threshold), and the current at the non-target nerve fiber is suppressed to 28 μA (below the 30 μA safety threshold) through phase difference.

[0079] Based on the above technical solution, in a method for determining the discharge parameters of a neural electrode provided by the present application, the first position coordinates of the target nerve fiber and the second position coordinates of the non-target nerve fiber are obtained. A microelectrode for stimulating the nerve fiber is deployed and the third position coordinates of the microelectrode are recorded. The first spacing between the first position coordinate and the third position coordinate is determined, and the second spacing between the second position coordinate and the third position coordinate is determined. Based on the first spacing and the second spacing, the frequency and phase of the current emitted by the microelectrode when the preset conditions are met are determined, wherein the preset conditions include: successful activation of the target nerve fiber and inhibition of the non-target nerve fiber. Based on this, through the frequency and phase joint encoding and gradient optimization mechanism, the targeting accuracy and safety of neural electrical signal regulation can be improved, and the technical problem of the existing neural electrical stimulation technology that the traditional electrodes are insufficiently targeted due to the diffuse electric field distribution, cannot accurately activate the target nerve fibers, and are prone to cause excitement in non-target areas is solved.

[0080] In a possible implementation, combining the above Figure 2 ,like Figure 3 As shown, the process of determining the frequency and phase of the current emitted by the microelectrode when the preset condition is met based on the first spacing and the second spacing in the above step 204 can be specifically implemented by the following steps 301 to 305:

[0081] Step 301: The neural electrode discharge parameter determination device initializes the frequency and phase of the current emitted by each microelectrode.

[0082] In the embodiment of the present application, the frequency of the current emitted by each microelectrode is ω i =ω0+Δω·log2(i+1) increasing, where ω i is the frequency of the current emitted by each microelectrode, ω0 is the set basic frequency, Δω is the frequency step, i=1,…,Q is the index of the microelectrode, and Q is the number of microelectrodes; the phase of the current emitted by each microelectrode is Calculate, where is the phase of the current emitted by each microelectrode, d i is the distance from each microelectrode to the nerve fiber, λ i is the wavelength, λ i =c / ω i , c is the propagation speed of electrical signals in tissues.

[0083] It should be pointed out that the initialization strategy needs to take into account both computational efficiency and optimization accuracy. The logarithmically increasing frequency sequence can achieve high-density coverage within a limited frequency band, while preventing infinite frequency growth when the number of electrodes is large. Phase initialization introduces linear encoding based on the spatial position of the electrodes.

[0084] As an example, the neural electrode discharge parameter determination device initializes the 8-channel microelectrode array: the basic frequency ω0 is set to 35 Hz, the frequency step Δω is set to 5 Hz, and the frequency sequence of each microelectrode channel is calculated to be [35 Hz, 40 Hz, 43.3 Hz, 45.8 Hz, 48 Hz, 49.8 Hz, 51.5 Hz, 53 Hz]; according to the distance d from each electrode to the nerve fiber, i , the propagation speed of the electrical signal in the tissue is 100m / s, and the wavelength λ is calculated i =c / ω i , and then by Calculate the initial phase of each electrode so that the initial phase difference conforms to the law of spatial electric field distribution.

[0085] Based on the above steps, this initialization method provides a parameter starting point for the subsequent iterative optimization of frequency and phase that conforms to the electrophysiological characteristics of the nerves and the laws of spatial electric field distribution. It can effectively improve the convergence speed of parameter optimization and the targeted control accuracy of neural electrical stimulation.

[0086] Step 302: The neural electrode discharge parameter determination device determines a frequency coding factor.

[0087] The frequency coding factor is a nonlinear weight factor designed based on the optimal response frequency of the target nerve fiber.

[0088] As an example, the frequency coding factor satisfies the following formula:

[0089]

[0090] Among them, ω opt is the optimal response frequency of the target nerve fiber, k i is the frequency coding factor.

[0091] It should be pointed out that the nonlinear design of the frequency coding factor is intended to enhance the selectivity of the target frequency. When the microelectrode frequency is close to the optimal response frequency, the coding factor k i The value of approaches 1, at this time the contribution weight of the microelectrode current at the target nerve fiber is significantly improved, making the current superposition at the target nerve fiber more efficient; and when ω i With ω opt When the deviation is large, k i The value of decays rapidly, thereby weakening the influence of non-sensitive frequencies on target nerve fibers.

[0092] Based on the above steps, nonlinear characteristics can focus on the sensitive frequency range of target nerve fibers more accurately than linear weights, effectively improving the targeting of the frequency dimension, providing a frequency selective basis for the efficient superposition of currents at the target fibers, and reducing energy dispersion caused by broadband stimulation.

[0093] Step 303: The neural electrode discharge parameter determination device determines a phase encoding factor.

[0094] The phase encoding factor is a factor used to adjust the phase difference of each microelectrode signal at the target fiber.

[0095] Optionally, the phase coding factor satisfies the following formula:

[0096]

[0097] in, is the desired phase of the target nerve fiber, p i is the phase encoding factor.

[0098] It should be pointed out that the phase encoding factor associates the actual phase of each microelectrode with the expected phase of the target nerve fiber through the cosine function, which essentially converts the phase difference into a linear weight coefficient.

[0099] As an example, when the microelectrode phase With expected phase When consistent, When the value is 1, the contribution weight of the electrode current at the target fiber is the largest; when the phase difference increases (such as and When the phase difference is π / 2, the weight coefficient decreases, thereby dynamically regulating the phase coherence of multi-electrode signals at the target nerve fiber.

[0100] Based on the above steps, this design not only retains the flexibility of phase control, allowing each microelectrode to have a reasonable phase offset based on the difference in spatial position, but also establishes a unified benchmark through the expected phase to ensure that the multi-electrode current forms a constructive superposition at the target nerve fiber.

[0101] In addition, the expected phase It can be adaptively adjusted according to the electrophysiological characteristics of the target nerve fibers. For example, in motor nerve regulation, Set the phase value to be synchronized with the EMG signal cycle to further improve activation efficiency.

[0102] Step 304: The neural electrode discharge parameter determination device determines a first expression and a second expression.

[0103] Among them, the first expression is used to represent the current value of the microelectrode current reaching the target nerve fiber at different frequencies and phases; the second expression is used to represent the current value of the microelectrode current reaching the non-target nerve fiber at different frequencies and phases.

[0104] As an example, the first expression satisfies the following formula:

[0105]

[0106] Wherein, T represents the first position coordinate of the target nerve fiber, t represents the time variable, and I i0 is the initial current amplitude of the i-th microelectrode, r i is a first distance between each microelectrode and the target nerve fiber;

[0107] As an example, the second expression satisfies the following formula:

[0108]

[0109] Where S represents the second position coordinate of the non-target nerve fiber, is the phase difference between the microelectrode to the first position coordinate T and the microelectrode to the second position coordinate S. The phase difference is the phase difference that suppresses the current intensity of the non-target nerve fiber due to destructive interference. is the second distance between each microelectrode and the non-target nerve fiber.

[0110] In the embodiment of the present application, the determination of the first expression and the second expression are both based on the spatial superposition and temporal modulation characteristics of the currents of multiple microelectrodes. Both integrate the current contribution of each microelectrode in the form of summation to reflect the array control effect. The first expression introduces a frequency coding factor and a phase coding factor, and strengthens the response of the target nerve fiber to a specific frequency and phase through nonlinear weights. At the same time, it combines the term inversely proportional to the square of the first spacing to reflect the spatial attenuation of the current in the tissue; the second expression retains the frequency coding factor and introduces a phase difference, and uses phase shift to make the current in the non-target area form destructive interference. At the same time, it reflects the influence of distance on the current of the non-target point through the term inversely proportional to the square of the second spacing.

[0111] It should be noted that the core difference between the first and second expressions lies in the targeted nature of the control logic. The first expression uses a phase encoding factor to ensure phase coordination of currents at the target point to achieve superimposed activation, while the second expression actively destroys the phase coordination of non-target points through phase elimination to achieve inhibition. The two form a complementary control mechanism. If it is necessary to activate multiple target nerve fibers or inhibit multiple non-target nerve fibers, it is only necessary to add the corresponding summation term for each target nerve fiber in the first expression and the corresponding summation term for each non-target nerve fiber in the second expression, without changing the core structure and control mechanism of the expression.

[0112] Based on the above steps, accurate modeling of neural electrical stimulation is achieved through the targeted integration of spatial attenuation characteristics, frequency and phase control factors, and multi-electrode synergistic effects: the first expression strengthens the response weight of the target nerve fibers to specific frequencies and phases at the mathematical level through the synergistic effect of frequency coding factors and phase coding factors, providing a quantitative basis for the efficient activation of target nerve fibers; the second expression introduces phase elimination to actively intervene in the phase relationship of non-target areas, accurately representing the inhibitory mechanism of current in non-target nerve fibers.

[0113] Step 305: The neural electrode discharge parameter determination device determines a joint objective function and uses a gradient descent method to iteratively adjust the frequency and phase of the current emitted by each microelectrode in the joint objective function.

[0114] As an example, the joint objective function F satisfies the following formula:

[0115]

[0116] Where α represents the first weighted coefficient for optimizing the target nerve fiber, β represents the second weighted coefficient for optimizing the non-target nerve fiber, and I th is the activation threshold, is the safety threshold.

[0117] In the embodiment of the present application, the determination of the joint objective function takes the precise activation of the target and the strong inhibition of the non-target as the core optimization goal, and constructs a comprehensive evaluation index by weighted summation of two items: the first item is for the target nerve fiber, and the max function is used to ensure that the activation is only performed when the current I(T, t) is lower than the activation threshold I th A penalty term is generated when the optimization process is optimized, pushing the optimization process iteratively toward increasing the current in the target fiber. The second term, for non-target nerve fibers, quantifies the degree to which the current at non-target points exceeds the safety threshold through the squared current ratio term, guiding the optimization process to suppress the current in non-target areas. The gradient descent method determines the adjustment direction for the frequency and phase of the current emitted by each electrode by calculating the partial derivatives of the objective function with respect to the frequency and phase of each microelectrode.

[0118] It should be pointed out that the non-negativity of the joint objective function ensures the clarity of the optimization direction, that is, the smaller the objective function value, the closer it is to the ideal state of target activation and non-target inhibition.

[0119] Based on the above technical solution, an initialization strategy is used to provide a starting point for frequency and phase parameters that conforms to the electrophysiological characteristics of neurons and the laws of spatial electric fields. A nonlinear frequency encoding factor and phase encoding factor are combined to achieve frequency sensitivity enhancement and phase synergistic regulation of target nerve fibers. The first and second expressions are then used to accurately model the current superposition in the target area and the destructive interference in the non-target area, respectively. Finally, a joint objective function and gradient descent method are used to dynamically balance the optimization goals of target activation and non-target inhibition, forming a complete technical chain of parameter initialization, encoding regulation, model construction, and iterative optimization. This technical chain not only utilizes the joint encoding mechanism of frequency and phase to improve the target selectivity of neural electrical stimulation, but also strengthens the inhibitory effect in non-target areas through spatial attenuation characteristics and phase elimination design. At the same time, iterative adjustment of the gradient descent method achieves dynamic parameter optimization, effectively solving the problem of insufficient targeting caused by electric field diffusion in traditional technologies, significantly improving the accuracy and safety of neural electrical stimulation, and synergistically optimizing the activation efficiency of target nerve fibers and the inhibitory effect in non-target areas.

[0120] In one possible implementation, combining Figure 3 ,like Figure 4 As shown, the above step 305 employing the gradient descent method to iteratively adjust the frequency and phase of the current emitted by each microelectrode in the joint objective function can be specifically implemented by the following steps 401 to 403, which are described in detail below:

[0121] Step 401: The neural electrode discharge parameter determination device calculates the gradient of the frequency and phase of the current emitted by each microelectrode by the joint objective function.

[0122] The gradient refers to the vector formed by taking the partial derivative of the joint objective function with respect to the frequency and phase of each microelectrode. It is used to quantify the influence of small changes in frequency and phase on the objective function value, and its direction indicates the direction in which the objective function value decreases fastest.

[0123] In the embodiment of the present application, an analytical method is used to calculate the gradient. Based on the mathematical expression of the joint objective function, combined with the functional relationship between current, frequency and phase in the first expression and the second expression, the chain rule is used to derive the analytical expression of the partial derivative of the objective function with respect to the frequency and phase of each microelectrode, and the current parameter value is directly substituted to obtain the gradient.

[0124] Based on the above steps, gradient calculation provides a quantitative parameter adjustment basis for the iterative optimization of the joint objective function. The quantitative control mechanism avoids the blindness of parameter adjustment, enabling the gradient descent method to iterate efficiently along the direction where the objective function value decreases fastest, which can significantly improve the convergence speed and final accuracy of parameter optimization.

[0125] Step 402: The neural electrode discharge parameter determination device updates the frequency and phase of the current emitted by each microelectrode.

[0126] As an example, the frequency update process of the current emitted by each microelectrode satisfies the following formula:

[0127]

[0128] Where m represents the iteration index of the frequency update of the current emitted by each microelectrode, η ω The step size for updating the frequency of the current emitted by each microelectrode.

[0129] As an example, the phase update process of the current emitted by each microelectrode satisfies the following formula:

[0130]

[0131] Where l represents the iteration index of the current phase update of each microelectrode, The step size for updating the current phase emitted for each microelectrode.

[0132] In the embodiment of the present application, the frequency and phase of each microelectrode can be updated using a variety of dynamic adjustment strategies: the step size η of the frequency update ω It can be flexibly set according to the current absolute value of the gradient. When the absolute value of the gradient is large, a larger step size is used to speed up convergence. When the absolute value of the gradient is small, a smaller step size is used to ensure accuracy. The step size of the phase update It can also be adjusted dynamically. For example, a larger value (such as π / 4) is taken at the beginning of the iteration to quickly approach the ideal phase, and a smaller value (such as π / 16) is taken at the end of the iteration for fine calibration.

[0133] It should be pointed out that the updates of frequency and phase are both independent and synergistic: independence is manifested in that the two can adopt different iteration step sizes to adapt to their respective sensitivities to the objective function; synergy is reflected in the need to verify the overall effect through the joint objective function value after the update to avoid over-optimization of a single parameter leading to target activation or decreased non-target inhibition effect.

[0134] Based on the above steps, the parameter update mechanism of each microelectrode frequency and phase guides the frequency to accurately approach the optimal response frequency range of the target nerve fiber through gradient information, and dynamically calibrates the phase to the direction that is conducive to constructive interference in the target area and destructive interference in the non-target area, so that the updated parameters can more efficiently meet the regulation requirements of the first expression and the second expression.

[0135] Step 403: The neural electrode discharge parameter determination device determines the convergence condition.

[0136] The convergence condition is the difference between the joint objective function of the current iteration and the previous iteration and the set threshold. If the difference is less than the set threshold, the iteration is stopped when the convergence condition is met. If the difference is greater than the set threshold, the gradient is continued to be calculated and the frequency and phase of the current emitted by each microelectrode are updated.

[0137] It should be pointed out that the core of the convergence condition is to ensure that the parameters can stably meet the preset conditions when the iteration stops, and the current at the target nerve fiber reaches the activation threshold and is lower than the safety threshold at the non-target. Therefore, the threshold setting needs to take into account both efficiency and accuracy: an excessively large threshold may lead to insufficient parameter optimization (such as the target current only slightly exceeding the threshold), while an excessively small threshold will increase the number of unnecessary iterations and prolong the calculation time.

[0138] As an example, the convergence threshold is set to 0.001. The joint objective function value is 0.58 at the initial iteration, and 0.45 after the first iteration (difference 0.13>0.001), and the iteration continues; the objective function value is 0.0032 after the fifth iteration, and 0.00305 for the sixth iteration (difference 0.00015<0.001). At this time, it is determined that the convergence condition is met and the iteration is stopped. After verification, the current at the target nerve fiber is 110μA (exceeding the 100μA activation threshold), and the current at the non-target nerve fiber is 28μA (below the 30μA safety threshold), which meets the preset control conditions.

[0139] Based on the above technical solution, a complete iterative optimization closed loop is formed through the collaborative mechanism of gradient calculation, parameter update and convergence judgment: gradient calculation provides quantitative direction guidance for parameter adjustment to avoid blind optimization; parameter update dynamically calibrates frequency and phase through gradient information, pushing the current at the target nerve fiber to approach the activation threshold and the current in the non-target area to converge below the safety threshold; convergence judgment ensures that the parameters at the time of iteration stop can stably meet the preset conditions and balance the optimization efficiency and accuracy through reasonable threshold control. The synergistic effect of the three not only significantly improves the convergence speed and final accuracy of parameter optimization, but also ensures the targeting and safety of neural electrical stimulation parameters through the gradient adjustment mechanism, effectively solving the problem of insufficient control precision caused by electric field diffusion in traditional technologies, and accurately balancing the effects of target nerve fiber activation and non-target area inhibition.

[0140] The above mainly introduces the scheme of the embodiment of the present application from the perspective of device implementation. It can be understood that each device, for example, the neural electrode discharge parameter determination device, includes at least one of the hardware structure and software modules corresponding to the execution of each function in order to realize the above functions. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0141] In the embodiment of the present application, the functional units of the device for determining the discharge parameters of neural electrodes can be divided according to the above-mentioned method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0142] In the case of an integrated unit, Figure 5 A possible structural diagram of the neural electrode discharge parameter determination device involved in the above embodiment (referred to as the neural electrode discharge parameter determination device 50) is shown. The neural electrode discharge parameter determination device 50 includes a processing unit 501 and a communication unit 502, and may also include a storage unit 503. Figure 5 The structural schematic diagram shown can be used to illustrate the structure of the neural electrode discharge parameter determination device involved in the above embodiments.

[0143] when Figure 5 The structural schematic diagram shown is used to illustrate the structure of the neural electrode discharge parameter determination device involved in the above-mentioned embodiment. The processing unit 501 is used to control and manage the operation of the neural electrode discharge parameter determination device, the communication unit 502 is used for the neural electrode discharge parameter determination device to communicate with other devices, and the storage unit 503 is used to store the program code and data of the neural electrode discharge parameter determination device.

[0144] For example, the communication unit 502 is used to obtain the first position coordinates of the target nerve fiber and the second position coordinates of the non-target nerve fiber. The target nerve fiber is the nerve fiber to be activated, and the non-target nerve fiber is the nerve fiber located in the adjacent area of ​​the same nerve trunk as the target nerve fiber, which does not need to be activated and whose excitation needs to be suppressed.

[0145] The processing unit 501 is used to deploy a microelectrode for stimulating nerve fibers and record the third position coordinates of the microelectrode; determine a first distance between the first position coordinate and the third position coordinate, and determine a second distance between the second position coordinate and the third position coordinate; based on the first distance and the second distance, determine the frequency and phase of the current emitted by the microelectrode when a preset condition is met, wherein the preset condition includes: successfully activating the target nerve fibers and inhibiting non-target nerve fibers.

[0146] In one possible implementation, the processing unit 501 is further used to determine the frequency and phase of the current emitted by the microelectrode when a preset condition is met based on the first spacing and the second spacing, including: determining a first expression based on the first spacing, the frequency coding factor and the phase coding factor; the first expression is used to characterize the current value of the microelectrode current reaching the target nerve fiber at different frequencies and phases; the frequency coding factor is a nonlinear weight factor designed based on the optimal response frequency of the target nerve fiber, and the phase coding factor is a factor used to regulate the phase difference of each microelectrode signal at the target fiber; determining a second expression based on the second spacing, the frequency coding factor and the phase elimination difference; the second expression is used The current value at the non-target nerve fiber at different frequencies and phases is characterized; the phase difference is the phase difference that suppresses the current intensity of the current at the non-target nerve fiber due to destructive interference; the joint objective function of the first expression and the second expression is determined, and the frequency and phase of the current emitted by each microelectrode in the joint objective function are iteratively adjusted using the gradient descent method, so that the current value calculated based on the first expression reaches the activation threshold and the current value calculated based on the second expression is lower than the safety threshold; the activation threshold is the current value that the current at the target nerve fiber must reach to achieve successful activation; the safety threshold is the current value that the current at the non-target nerve fiber must be lower than to suppress its excitation.

[0147] In a possible implementation, the processing unit 501 is further configured to initialize the frequency and phase of the current emitted by each microelectrode, including: the frequency of the current emitted by each microelectrode is set to ω i =ω0+Δω·log2(i+1) increasing, where ω i is the frequency of the current emitted by each microelectrode, ω0 is the set basic frequency, Δω is the frequency step, i=1,…,Q is the index of the microelectrode, and Q is the number of microelectrodes; the phase of the current emitted by each microelectrode is Calculate, where is the phase of the current emitted by each microelectrode, d i is the distance from each microelectrode to the nerve fiber, λ i is the wavelength.

[0148] In one possible implementation, the frequency coding factor satisfies the following formula:

[0149]

[0150] Among them, ω opt is the optimal response frequency of the target nerve fiber, k i is the frequency coding factor.

[0151] In one possible implementation, the phase coding factor satisfies the following formula:

[0152]

[0153] in, is the desired phase of the target nerve fiber, p i is the phase encoding factor.

[0154] In one possible implementation, the processing unit 501 is further configured to determine the first expression and the second expression, including:

[0155] The first expression satisfies the following formula:

[0156]

[0157] Where T represents the first position coordinate of the target nerve fiber, t represents the time variable, and I i0 is the initial current amplitude of the i-th microelectrode, r i is the first distance between each microelectrode and the target nerve fiber;

[0158] The second expression satisfies the following formula:

[0159]

[0160] Where S represents the second position coordinate of the non-target nerve fiber, is the phase difference between the microelectrode to the first position coordinate T and to the second position coordinate S, is the second distance between each microelectrode and the non-target nerve fiber.

[0161] In one possible implementation, the joint objective function F satisfies the following formula:

[0162]

[0163] Where α represents the first weighted coefficient for optimizing the target nerve fiber, β represents the second weighted coefficient for optimizing the non-target nerve fiber, and I th is the activation threshold, is the safety threshold.

[0164] In one possible implementation, the processing unit 501 is also used to iteratively adjust the current frequency and phase of each microelectrode based on the gradient descent method, including: calculating the gradient of the joint objective function with respect to the frequency and phase of the current emitted by each microelectrode; updating the frequency and phase of the current emitted by each microelectrode; judging the difference between the joint objective function of the current iteration and the previous iteration and the size of a set threshold, if the difference is less than the set threshold, the iteration is stopped when the convergence condition is met, and if the difference is greater than the set threshold, the gradient continues to be calculated and the frequency and phase of the current emitted by each microelectrode are updated.

[0165] In one possible implementation, the processing unit 501 is further configured to update the frequency and phase of the current emitted by each microelectrode, including: a frequency update process of the current emitted by each microelectrode satisfies the following formula:

[0166]

[0167] Where m represents the iteration index of the frequency update of the current emitted by each microelectrode, η ω The step size of the frequency update of the current emitted by each microelectrode;

[0168] The phase update process of the current emitted by each microelectrode satisfies the following formula:

[0169]

[0170] Where l represents the iteration index of the current phase update of each microelectrode, The step size for updating the current phase emitted for each microelectrode.

[0171] The processing unit 501 may be a processor or a controller, and the communication unit 502 may be a communication interface, a transceiver, a transceiver, a transceiver circuit, a transceiver device, etc. The communication interface is a general term and may include one or more interfaces. The storage unit 503 may be a memory. When the neural electrode discharge parameter determination device 50 is a chip, the processing unit 501 may be a processor or a controller, and the communication unit 502 may be an input interface and / or output interface, a pin or a circuit, etc. The storage unit 503 may be a storage unit within the chip (for example, a register, a cache, etc.), or a storage unit located outside the chip (for example, a read-only memory (ROM), a random access memory (RAM), etc.).

[0172] Among them, the communication unit can also be called a transceiver unit. The antenna and control circuit with transceiver functions in the neural electrode discharge parameter determination device 50 can be regarded as the communication unit 502 of the neural electrode discharge parameter determination device 50, and the processor with processing function can be regarded as the processing unit 501 of the neural electrode discharge parameter determination device 50. Optionally, the device used to implement the receiving function in the communication unit 502 can be regarded as a communication unit, and the communication unit is used to perform the receiving steps in the embodiment of the present application. The communication unit can be a receiver, a receiver, a receiving circuit, etc. The device used to implement the sending function in the communication unit 502 can be regarded as a sending unit, and the sending unit is used to perform the sending steps in the embodiment of the present application. The sending unit can be a transmitter, a transmitter, a sending circuit, etc.

[0173] Figure 5 If the integrated units are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The storage medium for storing computer software products includes various media that can store program codes, such as USB flash drives, mobile hard drives, read-only memories, random access memories, magnetic disks or optical disks.

[0174] Figure 5 A unit in a can also be called a module, for example, a processing unit can be called a processing module.

[0175] The embodiment of the present application also provides a hardware structure diagram of a neural electrode discharge parameter determination device (denoted as a neural electrode discharge parameter determination device 60), see Figure 6 The neural electrode discharge parameter determination device 60 includes a processor 601 and, optionally, a memory 602 connected to the processor 601 .

[0176] In the first possible implementation, see Figure 6 The neural electrode discharge parameter determination device 60 further includes a transceiver 603. The processor 601, the memory 602, and the transceiver 603 are connected via a bus. The transceiver 603 is used to communicate with other devices or a communication network. Optionally, the transceiver 603 may include a transmitter and a receiver. The device used to implement the receiving function in the transceiver 603 can be regarded as a receiver, and the receiver is used to perform the receiving step in the embodiment of the present application. The device used to implement the sending function in the transceiver 603 can be regarded as a transmitter, and the transmitter is used to perform the sending step in the embodiment of the present application.

[0177] Based on the first possible implementation, Figure 6 The structural schematic diagram shown can be used to illustrate the structure of the neural electrode discharge parameter determination device involved in the above embodiments.

[0178] in, Figure 6 The system chip in the apparatus for determining the discharge parameters of neural electrodes may also be shown. In this case, the actions performed by the apparatus for determining the discharge parameters of neural electrodes may be implemented by the system chip. The specific actions performed may be referred to above and will not be described in detail here.

[0179] During implementation, each step of the method provided in this embodiment can be completed by hardware integrated logic circuits in a processor or by software instructions. The steps of the method disclosed in the embodiments of this application can be directly implemented as execution by a hardware processor, or as a combination of hardware and software modules in a processor.

[0180] The processor in this application may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, and other types of computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform operations or processing. The processor may be a separate semiconductor chip, or it may be integrated into a semiconductor chip together with other circuits. For example, it may form an SoC (system on a chip) with other circuits (such as a codec circuit, a hardware acceleration circuit, or various bus and interface circuits), or it may be integrated into the ASIC as a built-in processor of the ASIC. The ASIC with the integrated processor may be packaged separately or together with other circuits. In addition to the core for executing software instructions to perform operations or processing, the processor may further include necessary hardware accelerators, such as a field programmable gate array (FPGA), a PLD (programmable logic device), or a logic circuit that implements dedicated logic operations.

[0181] The memory in the embodiments of the present application may include at least one of the following types: read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or electrically erasable programmable read-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0182] An embodiment of the present application also provides a computer-readable storage medium, comprising instructions, which, when executed on a computer, enables the computer to execute any of the above methods.

[0183] An embodiment of the present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute any of the above methods.

[0184] An embodiment of the present application also provides a chip, which includes a processor and an interface circuit, the interface circuit is coupled to the processor, the processor is used to run a computer program or instruction to implement the above method, and the interface circuit is used to communicate with other modules outside the chip.

[0185] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more media that can be integrated. The available media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVDs), or semiconductor media (eg, solid state disks (SSDs)).

[0186] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other variations of the disclosed embodiments by reviewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0187] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely illustrative of the present application as defined by the appended claims and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the claims of the present application and their equivalents.

Claims

1. A method for determining neural electrode discharge parameters, characterized in that: include: Obtaining a first position coordinate of a target nerve fiber and a second position coordinate of a non-target nerve fiber, wherein the target nerve fiber is a nerve fiber to be activated, and the non-target nerve fiber is a nerve fiber located in an adjacent area of ​​the same nerve trunk as the target nerve fiber, which does not need to be activated and whose excitation needs to be suppressed; deploying a microelectrode for stimulating nerve fibers and recording a third position coordinate of the microelectrode; determining a first distance between the first position coordinate and the third position coordinate, and determining a second distance between the second position coordinate and the third position coordinate; The frequency and phase of the current emitted by the microelectrode when a preset condition is met are determined based on the first spacing and the second spacing, wherein the preset condition includes: successfully activating the target nerve fiber and inhibiting the non-target nerve fiber.

2. The method according to claim 1, characterized in that The determining, based on the first spacing and the second spacing, of the frequency and phase of the current emitted by the microelectrode when a preset condition is satisfied includes: A first expression is determined based on the first spacing, the frequency coding factor, and the phase coding factor; the first expression is used to characterize the current value of the current of the microelectrode reaching the target nerve fiber at different frequencies and phases; the frequency coding factor is a nonlinear weight factor designed based on the optimal response frequency of the target nerve fiber, and the phase coding factor is a factor used to regulate the phase difference of each microelectrode signal at the target fiber; A second expression is determined based on the second spacing, the frequency coding factor, and the phase elimination difference; the second expression is used to represent the current value of the current of the microelectrode reaching the non-target nerve fiber at the different frequencies and phases; the phase elimination difference is the phase difference that suppresses the current intensity of the current at the non-target nerve fiber due to destructive interference; Determine the joint objective function of the first expression and the second expression, and use the gradient descent method to iteratively adjust the frequency and phase of the current emitted by each microelectrode in the joint objective function, so that the current value calculated based on the first expression reaches the activation threshold and the current value calculated based on the second expression is lower than the safety threshold; the activation threshold is the current value that the current at the target nerve fiber must reach to achieve successful activation; the safety threshold is the current value that the current at the non-target nerve fiber must be lower than to inhibit its excitation.

3. The method according to claim 2, characterized in that Initializing the frequency and phase of the current emitted by each microelectrode, including: The frequency of the current emitted by each microelectrode is ω i =ω0+Δω·log2(i+1) increasing, where ω i is the frequency of the current emitted by each microelectrode, ω0 is the set basic frequency, Δω is the frequency step, i=1,…,Q is the index of the microelectrode, and Q is the number of microelectrodes; The phase of the current emitted by each microelectrode is Calculate, where is the phase of the current emitted by each microelectrode, d i is the distance from each microelectrode to the nerve fiber, λ i is the wavelength.

4. The method according to claim 2, characterized in that The frequency coding factor satisfies the following formula: Among them, ω opt is the optimal response frequency of the target nerve fiber, k i is the frequency coding factor.

5. The method according to claim 2, characterized in that The phase coding factor satisfies the following formula: in, is the desired phase of the target nerve fiber, p i is the phase coding factor.

6. The method according to claim 2, characterized in that Determining the first expression and the second expression includes: The first expression satisfies the following formula: Wherein, T represents the first position coordinate of the target nerve fiber, t represents the time variable, and I i0 is the initial current amplitude of the i-th microelectrode, r i is a first distance between each microelectrode and the target nerve fiber; The second expression satisfies the following formula: Wherein, S represents the second position coordinate of the non-target nerve fiber, is the phase difference between the microelectrode to the first position coordinate T and to the second position coordinate S, is a second distance between each of the microelectrodes and the non-target nerve fibers.

7. The method according to claim 2, characterized in that The joint objective function F satisfies the following formula: Wherein, α represents the first weighted coefficient for optimizing the target nerve fiber, β represents the second weighted coefficient for optimizing the non-target nerve fiber, and I th is the activation threshold, is the safety threshold.

8. The method according to claim 7, characterized in that Iteratively adjusting the current frequency and phase of each microelectrode based on the gradient descent method includes: Calculating the gradient of the joint objective function with respect to the frequency and phase of the current emitted by each microelectrode; updating the frequency and phase of the current emitted by each microelectrode; The difference between the joint objective function of the current iteration and the previous iteration and the set threshold are determined. If the difference is less than the set threshold, the convergence condition is reached and the iteration is stopped. If the difference is greater than the set threshold, the gradient is continued to be calculated and the frequency and phase of the current emitted by each microelectrode are updated.

9. The method according to claim 8, characterized in that Updating the frequency and phase of the current emitted by each microelectrode, including: The frequency update process of the current emitted by each microelectrode satisfies the following formula: Wherein, m represents the iteration number index of the frequency update of the current emitted by each microelectrode, η ω The step size for updating the frequency of current emitted by each microelectrode; The phase update process of the current emitted by each microelectrode satisfies the following formula: Wherein, l represents the iteration number index of the current phase update emitted by each microelectrode, The step size of the current phase update for each microelectrode.

10. A device for determining neural electrode discharge parameters, characterized in that: The device includes: a communication unit and a processing unit; The communication unit is configured to obtain a first position coordinate of a target nerve fiber and a second position coordinate of a non-target nerve fiber, wherein the target nerve fiber is a nerve fiber to be activated, and the non-target nerve fiber is a nerve fiber located in an adjacent area of ​​the same nerve trunk as the target nerve fiber, which does not need to be activated and whose excitation needs to be suppressed; The processing unit is used to deploy a microelectrode for stimulating nerve fibers and record the third position coordinates of the microelectrode; determine a first distance between the first position coordinate and the third position coordinate, and determine a second distance between the second position coordinate and the third position coordinate; based on the first distance and the second distance, determine the frequency and phase of the current emitted by the microelectrode when a preset condition is met, wherein the preset condition includes: successfully activating the target nerve fiber and inhibiting the non-target nerve fiber.

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