An information processing system, method, device and medium for neural oscillation system regulation
By constructing phase response curves and determining the topology of the neural oscillation system based on geometric characteristics, and matching corresponding electrical stimulation strategies, the adaptability and stability problems of neural oscillation system modulation in existing technologies are solved, and efficient neural oscillation system modulation is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHEJIANG NEWROS MEDICAL TECH CO LTD
- Filing Date
- 2026-06-29
- Publication Date
- 2026-07-24
AI Technical Summary
Existing technologies lack the ability to identify and differentiate different types of neural oscillation systems in the modulation of neural oscillation systems. This makes it difficult to achieve stable and smooth phase traction modulation using traditional single stimulation modes. Furthermore, the operation steps are cumbersome and prone to parameter setting deviations.
Phase response curves are constructed by applying test perturbations. The topology type of the neural oscillation system is determined based on the geometric characteristics of the curves, and corresponding phase traction or reset electrical stimulation strategies are matched to generate control signals for regulation.
It enables precise control of different types of neural oscillation systems, improves the adaptability and stability of the control, reduces operational deviations caused by human intervention, and ensures the stability and accuracy of the control process.
Smart Images
Figure CN122450313A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of neural signal processing, brain-computer interface technology and closed-loop neural modulation, and particularly to an information processing system, method, device and medium for the modulation of neural oscillation systems. Background Technology
[0002] Current phase modulation techniques for neural oscillatory systems typically employ fixed electrical stimulation patterns, lacking the ability to identify and differentiate the dynamic topology of the system and thus failing to select appropriate stimulation schemes based on the system's response characteristics. When dealing with neural oscillatory systems exhibiting continuous response characteristics, traditional single stimulation patterns struggle to achieve stable and smooth phase traction modulation; conversely, for systems with jump responses, they cannot utilize the system's nonlinear characteristics for rapid phase correction, limiting the applicability of phase modulation and resulting in unstable effects. Furthermore, traditional modulation procedures often rely on manual assessment of system status and setting of stimulation parameters, leading to cumbersome steps, parameter setting deviations, and further impacting the overall quality of phase modulation in neural oscillatory systems.
[0003] In summary, how to adaptively select the corresponding phase modulation method for different types of neural oscillation systems in order to improve the adaptability and stability of modulation has become a technical problem that urgently needs to be solved in this field. Summary of the Invention
[0004] In view of the above background, the present invention aims to provide an information processing system, method, device, and medium for the modulation of neural oscillation systems, enabling the modulation of different types of neural oscillation systems. The specific solution is as follows: In a first aspect, this application discloses an information processing method for regulating a neural oscillation system, applied to a computer device, comprising: A topology type determination module is used to apply test perturbations to the subject's neural oscillation system to construct a phase response curve and determine the topology type of the neural oscillation system based on the geometric characteristics of the phase response curve. The first mode determination module is used to determine the phase traction electrical stimulation strategy as the target phase modulation stimulation strategy when the topology type is continuous response type. The second mode determination module is used to determine the phase reset electrical stimulation strategy as the target phase modulation stimulation strategy when the topology type is jump response type. The stimulation output module is used to generate a control signal corresponding to the target phase modulation stimulation strategy, and output electrical stimulation to the neural oscillation system according to the control signal to modulate the neural oscillation system.
[0005] Optionally, the topology type determination module includes: A perturbation application unit is used to apply a controlled test perturbation to the subject's neural oscillation system during the calibration phase and record the phase shift of the neural oscillation system in the next cycle after each test perturbation application; wherein the test perturbation covers at least one complete neural oscillation cycle. A curve construction unit is used to construct a phase response curve based on the correspondence between the phase offset and the phase of the neural oscillation when the test perturbation is applied.
[0006] Optionally, the topology type determination module includes: The feature extraction unit is used to extract the geometric features of the phase response curve; wherein, the geometric features include monotonicity indicators characterizing the overall trend of the curve, zero distribution of the curve's positive and negative value switching, discontinuities at abrupt change locations, and local curvature of equiphase lines calculated from the curve slope; The type determination unit is used to determine that the topology type is a continuous response type if the monotonicity flag, the zero-point distribution, and the local curvature of the equiphase line satisfy a preset continuous response condition; and to determine that the topology type is a jump response type if the monotonicity flag, the zero-point distribution, and the local curvature of the equiphase line satisfy a preset jump response condition.
[0007] Optionally, the type determination unit includes: The first type determination subunit is used to determine that the topology type is a continuous response type when all the first preset conditions occur simultaneously; wherein, each of the first preset conditions is that the phase response curve is monotonically increasing as a whole, there are no jump discontinuities, and the local curvature of the equiphase line is less than the first preset curvature threshold and the change is smooth. The second type determination subunit is used to determine that the topology type is a jump response type when all the second preset conditions occur simultaneously; wherein, each of the second preset conditions is that the phase response curve is non-monotonic, there is at least one jump discontinuity, the local curvature of the equiphase line is greater than the second preset curvature threshold and there is a folded shape. Wherein, the second preset curvature threshold is greater than the first preset curvature threshold.
[0008] Optionally, the phase-reset electrical stimulation strategy characterizes each round of stimulation as including double-pulse electrical stimulation, and the overall arrangement sequence of the double pulses is based on the inherent period of the neural oscillation system. In each round of stimulation, the first pulse is output in the positive phase window corresponding to the negative value region of the phase response curve, and the second pulse is output after the transient response induced by the first pulse decays. The pulse interval and pulse intensity of the first pulse and the second pulse are determined based on the recovery time constant and isophase folding angle of the neural oscillation system.
[0009] Optionally, the stimulation output module includes: The first pulse output unit is configured to generate a first control signal corresponding to the phase reset electrical stimulation strategy in each round of stimulation if the topology type is a jump response type, and output a first pulse to the neural oscillation system according to the first control signal to control the phase of the neural oscillation system to jump negatively. The second pulse output unit is used to generate a second control signal corresponding to the phase reset electrical stimulation strategy in each round of stimulation if the topology type is a jump response type, and output a second pulse to the neural oscillation system according to the second control signal, so as to push the phase of the neural oscillation system toward the target isophase line based on the nonlinear gain of the neural oscillation system.
[0010] Optionally, the phase traction electrical stimulation strategy characterizes each round of stimulation action as including single-pulse electrical stimulation, and the single pulse is output in the phase window corresponding to the non-negative value region of the phase response curve.
[0011] Secondly, this application discloses an information processing method for regulating a neural oscillation system, applied to a computer device, comprising: Test perturbations are applied to the subject's neural oscillation system to construct a phase response curve, and the topology type of the neural oscillation system is determined based on the geometric characteristics of the phase response curve; When the topology type is continuous response, the phase traction electrical stimulation strategy is determined as the target phase modulation stimulation strategy. When the topology type is jump response type, the phase reset electrical stimulation strategy is determined as the target phase modulation stimulation strategy; A control signal corresponding to the target phase modulation stimulation strategy is generated, and electrical stimulation is output to the neural oscillation system according to the control signal to modulate the neural oscillation system.
[0012] Thirdly, this application discloses an electronic device, including: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the steps of the aforementioned disclosed information processing method for modulating a neural oscillation system.
[0013] Fourthly, this application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned disclosed information processing method for regulating a neural oscillation system.
[0014] The beneficial effects of this application are as follows: This application is applied to a computer device, comprising: a topology type determination module, used to apply a test perturbation to the neural oscillation system of a subject to construct a phase response curve, and determine the topology type of the neural oscillation system based on the geometric characteristics of the phase response curve; a first mode determination module, used to determine a phase traction electrical stimulation strategy as a target phase modulation stimulation strategy when the topology type is continuous response type; a second mode determination module, used to determine a phase reset electrical stimulation strategy as a target phase modulation stimulation strategy when the topology type is jump response type; and a stimulation output module, used to generate a control signal corresponding to the target phase modulation stimulation strategy, and output electrical stimulation to the neural oscillation system according to the control signal to modulate the neural oscillation system. Therefore, this application, by applying a test perturbation to the neural oscillation system and constructing a phase response curve, and determining the system topology type based on the geometric characteristics of the curve, can accurately distinguish between two different types of neural oscillation systems: continuous response type and jump response type. Based on this, the first and second mode determination modules can be matched with appropriate electrical stimulation strategies, allowing different types of systems to obtain targeted modulation schemes. The subsequent stimulation output module generates corresponding control signals and outputs electrical stimulation according to the selected strategy, ensuring that the output electrical stimulation form, application location, and parameters match the operating characteristics of the corresponding system, avoiding the problem of a uniform stimulation method being unsuitable for different neural oscillation systems. This invention effectively improves the adaptability and modulation accuracy of phase modulation in neural oscillation systems. Simultaneously, the entire process is automatically completed by a computer device, including perturbation testing, type determination, strategy matching, and stimulation output, reducing operational deviations caused by manual intervention and ensuring a stable and orderly modulation process. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0016] Figure 1 This is a structural diagram of an information processing system for regulating a neural oscillation system disclosed in this application; Figure 2 This application discloses a specific structural diagram of an information processing system for regulating a neural oscillation system. Figure 3 This is a flowchart of an information processing method for regulating a neural oscillation system disclosed in this application; Figure 4 This is a specific topological fractal and equiphase line geometric diagram disclosed in this application; Figure 5 This is another specific topological fractal and equiphase line geometrical diagram disclosed in this application; Figure 6 This is a demonstration diagram of a specific isophase folding and double-pulse directional reset disclosed in this application; Figure 7 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation
[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.
[0018] Existing closed-loop neuromodulation techniques are generally based on the weakly coupled limit loop assumption, which assumes that all individuals' neural oscillatory systems have monotonic and continuous phase response characteristics to external stimuli, thus employing a single continuous phase traction strategy (such as applying a weak pulse within the negative phase peak window). However, the phase response curves of neural oscillatory systems exhibit significant topological heterogeneity: the phase response curves of some individuals show a non-monotonic structure and include negative regions (jump response type). Under the weak pulse continuous traction strategy, the stimulus happens to fall into the negative region of the phase response curve, causing a phase jump in the opposite direction rather than positive traction, causing the system to deviate from the target attraction domain, triggering a rebound in neural excitability after stimulation and intervention failure.
[0019] Therefore, this application provides an information processing scheme for the regulation of neural oscillation systems, which regulates different types of neural oscillation systems.
[0020] See Figure 1 As shown in the embodiments of this application, an information processing system for the modulation of a neural oscillation system is disclosed, applied to a computer device, comprising: The topology type determination module 11 is used to apply test perturbations to the subject's neural oscillation system to construct a phase response curve and determine the topology type of the neural oscillation system based on the geometric characteristics of the phase response curve. The first mode determination module 12 is used to determine the phase traction electrical stimulation strategy as the target phase modulation stimulation strategy when the topology type is continuous response type. The second mode determination module 13 is used to determine the phase reset electrical stimulation strategy as the target phase modulation stimulation strategy when the topology type is jump response type. The stimulation output module 14 is used to generate a control signal corresponding to the target phase modulation stimulation strategy, and output electrical stimulation to the neural oscillation system according to the control signal to modulate the neural oscillation system.
[0021] It is understood that the topology type determination module 11 includes: a perturbation application unit 111, used to apply a controlled test perturbation to the subject's neural oscillation system during the calibration phase, and record the phase shift corresponding to the next cycle of the neural oscillation system after each test perturbation application; wherein the test perturbation covers at least one complete neural oscillation cycle; and a curve construction unit 112, used to construct a phase response curve based on the correspondence between the phase shift and the phase of the neural oscillation when the test perturbation is applied.
[0022] The topology type determination module 11 includes a disturbance application unit 111 and a curve construction unit 112.
[0023] During the calibration phase, the perturbation application unit 111 first performs a full-cycle scan-type controlled test perturbation on the subject's neural oscillation system. Following a preset timing sequence, standardized weak test pulses are sequentially applied to different phase points of the neural oscillation to ensure the test perturbation fully covers a neural oscillation cycle from 0° to 360°, avoiding phase sampling omissions. Each time a test perturbation is applied, the real-time phase of the neural oscillation corresponding to the perturbation application moment is recorded synchronously and accurately. After the current perturbation ends and the system enters the next oscillation cycle, the actual phase of the next cycle is captured using a phase tracking algorithm (such as a phase-locked loop or Hilbert transform). The difference between this actual phase and the theoretical reference phase in the undisturbed state is calculated to obtain the phase offset corresponding to a single perturbation. Repeat the above perturbation application, phase tracking, and difference calculation process, traversing all sampled phase points within the complete cycle, and finally collect a set of one-to-one corresponding "perturbation application phase - phase offset" raw data pairs, providing a foundation for subsequent curve construction.
[0024] Curve construction unit 112 receives multiple sets of perturbation application phase-phase offset values output by perturbation application unit 111. Based on the data pairs, a phase response curve is constructed. Specifically, the phase of the neural oscillation at the time of perturbation is plotted on the horizontal axis, and the corresponding phase shift is plotted on the horizontal axis. A two-dimensional rectangular coordinate system is established with the vertical axis as the ordinate; finally, multiple sets of perturbations are applied with phase-phase offsets. Data pairs are mapped onto this coordinate system, and connecting discrete data points forms a continuous function curve. That is, the phase response curve, where, Apply phase to the disturbance, The phase shift is represented by a positive value indicating phase advance and a negative value indicating phase lag. This curve visually illustrates the dynamic relationship between the phase shift and the applied phase of the perturbation after the neural oscillation system is disturbed at different phases, providing a core basis for the subsequent topological classification of the phase response curve.
[0025] Furthermore, the topology type determination module 11 includes: a feature extraction unit 113, used to extract the geometric features of the phase response curve; wherein, the geometric features include a monotonicity indicator characterizing the overall trend of the curve, a zero-point distribution of the curve's positive and negative value switching, discontinuities at abrupt change locations, and the local curvature of the equiphase line calculated from the curve slope; and a type determination unit 114, used to determine that the topology type is a continuous response type if the monotonicity indicator, the zero-point distribution, and the local curvature of the equiphase line satisfy a preset continuous response condition; and to determine that the topology type is a jump response type if the monotonicity indicator, the zero-point distribution, and the local curvature of the equiphase line satisfy a preset jump response condition.
[0026] The topology type determination module 11 includes a feature extraction unit 113 and a type determination unit 114.
[0027] The feature extraction unit 113 performs geometric feature extraction on the constructed phase response curve. First, it extracts monotonicity indicators representing the overall trend of the curve to determine whether the curve monotonically increases throughout the oscillation period. Second, it extracts the zero-point distribution of the curve's positive and negative value transitions to determine the phase position corresponding to the transition from negative to positive or from positive to negative. Next, it identifies discontinuities at abrupt change points and detects whether the curve has jump discontinuities where the phase shift amplitude exceeds a set threshold. Finally, it extracts features based on the slope of the phase response curve. The local curvature of the equiphase lines is calculated to reflect the system's response mode to disturbances. Neural oscillations (such as...) Rhythmic, slow-wave oscillations can be mathematically modeled as limit cycle oscillators, which have stable periodic orbits in their state space. Each point in the phase space can be mapped to a specific phase of the limit cycle through equiphase lines. The geometric curvature of the equiphase lines determines the system's response sensitivity and direction to external disturbances.
[0028] The type determination unit 114 determines the topology type of the neural oscillation system based on the extracted geometric features. When the monotonicity indicator shows that the curve is monotonically increasing overall, there are no abnormal switching points in the zero distribution, there are no discontinuities, and the local curvature of the equiphase lines is small, satisfying the preset continuous response condition, the topology type is determined to be continuous response type. When the monotonicity indicator shows that the curve is not monotonic, there are abnormal switching points in the zero distribution, there are discontinuities that meet the amplitude requirements, and the local curvature of the equiphase lines is large, satisfying the preset jump response condition, the topology type is determined to be jump response type.
[0029] When the topological type of the neural oscillation system is determined to be continuous response type (Type A), the first mode determination module 12 determines the phase traction electrical stimulation strategy as the target phase modulation stimulation strategy based on the dynamic mechanism matching constraint.
[0030] In this embodiment, the phase traction electrical stimulation strategy characterizes each round of stimulation action as including single-pulse electrical stimulation, and the single pulse is output in the phase window corresponding to the non-negative value region of the phase response curve.
[0031] The phase response curve for continuous response systems is monotonically continuous, with smooth isophase lines and low curvature. Phase traction electrical stimulation strategies employ a parameter structure of weak pulse intensity, negative phase window, and single pulse, suitable for phase traction modulation of weakly coupled limit loop systems. These strategies estimate the neural oscillation phase in real time (e.g., using Hilbert transform or phase-locked loops) and apply stimulation within a fixed phase window (usually near the negative phase peak), attempting to enhance endogenous oscillations through repeated traction. For Type A systems, the phase evolution equation of the strategy is a continuous differential equation: Where ε is a small parameter, Given the shape function of the phase response curve, it is required that... (Ensure that you do not cross the folding zone of the isophase line).
[0032] When the topology of the neural oscillation system is determined to be of the jump response type (Type B), the second mode determination module 13 determines the phase reset electrical stimulation strategy as the target phase modulation stimulation strategy based on the dynamic mechanism matching constraint. The phase response curve corresponding to the jump response type is non-monotonic and has jump discontinuities, isophase lines are geometrically folded and have large curvature. The phase reset electrical stimulation strategy adopts a parameter structure of strong pulse intensity, positive phase window and double pulse, which is suitable for phase reset modulation of strongly coupled limit loop systems.
[0033] The stimulation output module 14 generates a corresponding control signal according to the determined target phase modulation stimulation strategy. The control signal includes stimulation intensity, application window and pulse form parameters that match the strategy. Based on the control signal, the module outputs corresponding electrical stimulation to the neural oscillation system, thereby implementing phase traction or phase reset modulation of the neural oscillation system according to the strategy topology, and realizing precise intervention of neural oscillation.
[0034] The beneficial effects of this application are as follows: This application is applied to a computer device, comprising: a topology type determination module, used to apply a test perturbation to the neural oscillation system of a subject to construct a phase response curve, and determine the topology type of the neural oscillation system based on the geometric characteristics of the phase response curve; a first mode determination module, used to determine a phase traction electrical stimulation strategy as a target phase modulation stimulation strategy if the topology type is a continuous response type; a second mode determination module, used to determine a phase reset electrical stimulation strategy as a target phase modulation stimulation strategy if the topology type is a jump response type; and a stimulation output module, used to generate a control signal corresponding to the target phase modulation stimulation strategy, and output electrical stimulation to the neural oscillation system according to the control signal to modulate the neural oscillation system. Therefore, this application applies test perturbations to the neural oscillation system and constructs phase response curves. Based on the geometric characteristics of the curves, the system topology type is determined, which can accurately distinguish between two different types of neural oscillation systems: continuous response and jump response. On this basis, the first mode determination module and the second mode determination module can match and adapt electrical stimulation strategies, allowing different types of systems to obtain targeted control schemes. Subsequently, the stimulation output module generates corresponding control signals and outputs electrical stimulation according to the selected strategy, so that the output electrical stimulation form, application location, and parameters can match the operating characteristics of the corresponding system, avoiding the problem that a uniform stimulation method cannot be adapted to different neural oscillation systems. This effectively improves the adaptability and control accuracy of phase modulation of the neural oscillation system. At the same time, the entire process is automatically completed by a computer device, including perturbation testing, type determination, strategy matching, and stimulation output, reducing operational deviations caused by manual intervention and ensuring that the control process is carried out stably and orderly.
[0035] Reference Figure 2 As shown, this embodiment of the invention discloses a specific information processing system module framework for the modulation of neural oscillation systems. Compared with the previous embodiment, this embodiment further explains and optimizes the technical solution. Specifically: In this embodiment, the type determination unit 114 includes: a first type determination subunit 1141, used to determine that the topology type is a continuous response type when all first preset conditions occur simultaneously; wherein each first preset condition is that the phase response curve is monotonically increasing overall, there are no jump discontinuities, and the local curvature of the equiphase lines is less than a first preset curvature threshold and the change is smooth; a second type determination subunit 1142, used to determine that the topology type is a jump response type when all second preset conditions occur simultaneously; wherein each second preset condition is that the phase response curve is non-monotonic, there is at least one jump discontinuity, the local curvature of the equiphase lines is greater than a second preset curvature threshold and there is a folded shape; wherein the second preset curvature threshold is greater than the first preset curvature threshold.
[0036] In this embodiment, the stimulation output module 14 includes: a first pulse output unit 141, configured to generate a first control signal corresponding to the phase reset electrical stimulation strategy in each round of stimulation if the topology type is a jump response type, and output a first pulse to the neural oscillation system according to the first control signal to control the phase of the neural oscillation system to jump negatively; and a second pulse output unit 142, configured to generate a second control signal corresponding to the phase reset electrical stimulation strategy in each round of stimulation if the topology type is a jump response type, and output a second pulse to the neural oscillation system according to the second control signal to push the phase of the neural oscillation system toward the target isophase line based on the nonlinear gain of the neural oscillation system.
[0037] It is understood that the type determination unit 114 includes a first type determination subunit 1141 and a second type determination subunit 1142. When all the first preset conditions occur simultaneously, the first type determination subunit 1141 determines that the topology type is continuous response type. That is, when the first preset conditions, namely, the monotonicity indicator representing the overall monotonically increasing phase response curve, the zero-point distribution representing the absence of jump discontinuities in the phase response curve, and the local curvature of the equiphase line being less than the first preset curvature threshold and changing smoothly, occur simultaneously, the topology type is determined to be continuous response type. When all the second preset conditions, namely, the second type determination subunit 1142, occur simultaneously, the topology type is determined to be jump response type. In other words, when the second preset conditions, namely, the monotonicity indicator representing the non-monotonic change of the phase response curve, the zero-point distribution representing the presence of at least one jump discontinuity in the phase response curve, and the local curvature of the equiphase line being greater than the second preset curvature threshold and the phase response curve exhibiting a folded shape, occur simultaneously, the topology type is determined to be jump response type. It should be noted that the second preset curvature threshold is greater than the first preset curvature threshold.
[0038] In this embodiment, the phase reset electrical stimulation strategy represents that each round of stimulation includes double-pulse electrical stimulation, and the overall arrangement sequence of the double pulses is based on the inherent period of the neural oscillation system. In each round of stimulation, the first pulse is output in the positive phase window corresponding to the negative value region of the phase response curve, and the second pulse is output after the transient response caused by the first pulse decays. The pulse interval and pulse intensity of the first pulse and the second pulse are determined based on the recovery time constant and isophase folding angle of the neural oscillation system.
[0039] The phase evolution equation for the phase-reset electrical stimulation strategy is a discrete mapping: ,in, The phase jump induced by the double pulse requires the double pulse interval to match the system's natural period, so that the jump endpoint falls within the attraction domain of the target isophase line. Specifically, each round of stimulation includes double pulse electrical stimulation, and the overall timing of the double pulses needs to be set according to the natural period of the neural oscillation system. The first pulse in each round of stimulation is output in the positive phase window corresponding to the negative value region of the phase response curve (usually near the positive phase of 0°±15°). The second pulse is output after the transient response caused by the first pulse has sufficiently decayed. The pulse interval between the first and second pulses, as well as their respective pulse intensities, need to be determined based on the recovery time constant of the neural oscillation system and the folding angle of the isophase line to ensure that the double pulse sequence can utilize the discontinuous jump characteristics of the negative value region of the phase response curve to achieve directional phase reset of the jump-response neural oscillation system.
[0040] The stimulation output module 14 includes a first pulse output unit 141 and a second pulse output unit 142.
[0041] The first pulse output unit 141 is used to generate a first control signal corresponding to the phase reset electrical stimulation strategy in each round of stimulation when the topology type is a jump response type, and output a first pulse to the neural oscillation system according to the first control signal to control the phase of the neural oscillation system to jump negatively. The second pulse output unit 142 is used to generate a second control signal corresponding to the phase reset electrical stimulation strategy in each round of stimulation when the topology type is a jump response type, and output a second pulse to the neural oscillation system according to the second control signal to push the phase of the neural oscillation system toward the target isophase line based on the nonlinear gain of the neural oscillation system.
[0042] See Figure 3 As shown in the embodiments of this application, an information processing method for regulating a neural oscillation system is disclosed, applied to a computer device, including: Step S11: Apply a test perturbation to the subject's neural oscillation system to construct a phase response curve, and determine the topology type of the neural oscillation system based on the geometric characteristics of the phase response curve.
[0043] In this embodiment, a test perturbation is applied to the subject's neural oscillation system to construct a phase response curve. Extract the geometric features of the phase response curve, including monotonicity indicators, zero-point locations, discontinuity amplitudes, and equiphase curvature estimates. If the phase response curve is monotonic and has no... A jump is classified as Type A (continuous response); if the phase response curve is non-monotonic and exists The jump is classified as Type B (jump response type).
[0044] like Figure 4 As shown, the phase response curve (monotonically increasing) and smooth isophase lines (concentric circles) of the Type A system indicate that a weak pulse causes the phase to slide continuously along the isophase lines. The conclusion is that a weak pulse causes the phase to be continuously pulled along the smooth isophase lines, making the phase-pulsing electrical stimulation strategy (Type A) suitable. Figure 5 As shown, the phase response curve of the Type B system (non-monotonic, including negative regions and jump discontinuities) and the folded isophase line (spiral) show that weak pulses cause the phase to jump in the opposite direction across the folded region, while double pulses utilize the folded structure to achieve directional reset. The conclusion is that weak pulses cause phase to jump in the opposite direction, and double pulses utilize the folded structure to achieve directional reset, making the phase reset electrical stimulation strategy (Type B) applicable.
[0045] Step S12: If the topology type is continuous response type, then the phase traction electrical stimulation strategy is determined as the target phase modulation stimulation strategy.
[0046] If it is Type A, the system loads a continuous phase traction strategy, i.e., a phase traction electrical stimulation strategy. Specifically, a weakly coupled Adler-type control law is used, and a single pulse is applied in the negative phase peak window (180°±15°), with the pulse intensity lower than the isophase folding threshold.
[0047] Step S13: If the topology type is a jump response type, then the phase reset electrical stimulation strategy is determined as the target phase modulation stimulation strategy.
[0048] If it is a Type B system, a discrete phase reset strategy is applied. Specifically, a phase reset electrical stimulation strategy is used, employing a strongly coupled Winfried-type reset mapping. Two pulses are applied in the positive phase window (0°±15°). The first pulse induces a phase jump across the folding region, and the second pulse pushes the system into the target isophase line.
[0049] Step S14: Generate a control signal corresponding to the target phase modulation stimulation strategy, and output electrical stimulation to the neural oscillation system according to the control signal to modulate the neural oscillation system.
[0050] When generating control signals corresponding to the target phase modulation stimulation strategy, different phase tracking logics are adopted according to the differences in strategy topology between continuous response and skip response. For continuous response, a phase-locked loop logic with continuous phase tracking is loaded, while for skip response, a discrete event triggering logic with pulse interval and period synchronization is adopted. During the intervention, the system continues to run with the loaded strategy topology and outputs electrical stimulation to the neural oscillation system to modulate the neural oscillation system.
[0051] It is understandable that the intervention or regulation can be stopped based on whether the neural oscillation system currently meets the preset stopping conditions. For example, in the sleep state regulation scenario, the preset stopping condition is the current sleep state transition of the neural oscillation system. Therefore, the intervention is terminated, that is, the intervention or regulation is stopped regardless of the strategy topology.
[0052] When modulating a neural oscillatory system with a topologically jump-response type and employing a phase-reset electrical stimulation strategy, the phase space trajectory, target attraction domain, and folding boundary are specifically as follows: Figure 6 As shown.
[0053] This application uses topological typing of phase response curves as a priori constraint mechanism for stimulus strategy selection. It uses the monotonicity and discontinuities of the phase response curves of the neural oscillatory system as prerequisites for strategy selection, rather than merely providing an ex post facto interpretation. Simultaneously, it adheres to the principle of matching dynamic mechanisms, ensuring that the mathematical topological structure of the stimulus strategy is compatible with the isophase geometry of the target system, rather than simply matching stimulus parameters. This fundamentally solves the problem of systemic intervention failure and harmful responses in the jumping response subgroup. Currently, all individuals are assumed to be continuous responders, leading to approximately 15%–20% of jumping response individuals accepting incorrect strategies. This application, through precise typing and matching of compatible strategies, increases the group intervention effectiveness from approximately 75% to near-full coverage, while effectively avoiding the risks of phase rebound and abnormally increased cortical excitability caused by incorrect strategies.
[0054] Secondly, this application proposes an anti-phase response utilization scheme for skip-response systems. It abandons the traditional approach of avoiding the negative region of the phase response curve, instead utilizing the discontinuous skipping characteristics of this region to achieve directional phase reset through a double-pulse sequence, transforming the originally harmful phase bounce into a controllable phase reset process. Simultaneously, based on the paradigm shift from parameter space optimization to phase space topology optimization, it overcomes the limitation of existing technologies that only optimize stimulus parameters, upgrading to a dynamic level optimization of the control law's mathematical structure. Combined with the theoretical constraint of using the curvature of equiphase lines as the upper limit of policy strength, it ensures that continuous-response systems do not cross the equiphase line folding region while guaranteeing the precise application of reset pulses in skip-response systems, achieving efficient and safe utilization of the nonlinear characteristics of neural oscillations.
[0055] Furthermore, this application establishes a personalized regulation framework with a neurophysical basis. Unlike existing technologies that focus on parameter tuning at the statistical level, this application achieves personalized adaptation at the dynamic mechanism level. The classification of continuous response type and jumping response type is not based on empirical parameter differences, but on first-principles classification based on limit cycle theory, isophase geometry, and phase response curve topology. It has universality across species, brain regions, and neural rhythms, providing solid theoretical support and a reliable technical path for closed-loop regulation of neural oscillations.
[0056] In this application, standard phase response curve calibration was performed on 20 healthy subjects: phase response curves were measured by scanning α period with a fixed weak pulse; the expected classification was performed, with approximately 80% being Type A (monotonic phase response curve) and 20% being Type B (non-monotonic phase response curve, including the negative value region).
[0057] Intervention comparison: All subjects first received traditional negative-phase window single pulse (Type I strategy), and the phase-locking success rate was recorded; then Type B subjects were switched to positive-phase window double pulse (Type II strategy), and the phase-locking success rate was recorded.
[0058] Expected results: Type B has a success rate of less than 30% under the traditional strategy and greater than 75% under the matching strategy, and after stimulation... Power bounce (harmful response) is significantly reduced.
[0059] Furthermore, embodiments of this application also provide an electronic device. Figure 7 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.
[0060] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Specifically, it may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the information processing method for regulating a neural oscillation system, as disclosed in any of the foregoing embodiments.
[0061] In this embodiment, the power supply 23 is used to provide operating voltage for various hardware devices on the electronic device; the communication interface 24 can create a data transmission channel between the electronic device and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.
[0062] The processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 21 may be implemented using at least one hardware form selected from digital signal processing, field-programmable gate arrays, and programmable logic arrays. The processor 21 may also include a main processor and a coprocessor. The main processor, also known as the central processing unit, is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may integrate an image processor, which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 21 may also include an artificial intelligence processor, which handles computational operations related to machine learning.
[0063] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored on it include operating system 221, computer program 222 and data 223, etc., and the storage method can be temporary storage or permanent storage.
[0064] The operating system 221 manages and controls the various hardware devices and computer programs 222 on the electronic device to enable the processor 21 to perform calculations and processing on the massive amounts of data 223 in the memory 22. The computer program 222 may include, in addition to a computer program capable of performing the information processing method for regulating a neural oscillation system disclosed in any of the foregoing embodiments, a computer program executed by the electronic device, and may further include computer programs capable of performing other specific tasks. The data 223 may include data received by the electronic device from external devices, as well as data collected by its own input / output interface 25.
[0065] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned information processing method for regulating a neural oscillation system. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.
[0066] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.
[0067] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application. The steps of the methods or algorithms described in connection with the embodiments disclosed herein can be implemented directly in hardware, software modules executed by a processor, or a combination of both. Software modules can be located in random access memory, memory, read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disks, removable disks, or any other form of storage medium known in the art.
[0068] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0069] The above provides a detailed description of an information processing system, method, device, and medium for regulating a neural oscillation system provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only intended to help understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. An information processing system for regulating a neural oscillation system, characterized in that, Applied to computer devices, including: A topology type determination module is used to apply test perturbations to the subject's neural oscillation system to construct a phase response curve and determine the topology type of the neural oscillation system based on the geometric characteristics of the phase response curve. The first mode determination module is used to determine the phase traction electrical stimulation strategy as the target phase modulation stimulation strategy when the topology type is continuous response type. The second mode determination module is used to determine the phase reset electrical stimulation strategy as the target phase modulation stimulation strategy when the topology type is jump response type. The stimulation output module is used to generate a control signal corresponding to the target phase modulation stimulation strategy, and output electrical stimulation to the neural oscillation system according to the control signal to modulate the neural oscillation system.
2. The information processing system for regulating a neural oscillation system according to claim 1, characterized in that, The topology type determination module includes: A perturbation application unit is used to apply a controlled test perturbation to the subject's neural oscillation system during the calibration phase and record the phase shift of the neural oscillation system in the next cycle after each test perturbation application; wherein the test perturbation covers at least one complete neural oscillation cycle. A curve construction unit is used to construct a phase response curve based on the correspondence between the phase offset and the phase of the neural oscillation when the test perturbation is applied.
3. The information processing system for regulating a neural oscillation system according to claim 1, characterized in that, The topology type determination module includes: The feature extraction unit is used to extract the geometric features of the phase response curve; wherein, the geometric features include monotonicity indicators characterizing the overall trend of the curve, zero distribution of the curve's positive and negative value switching, discontinuities at abrupt change locations, and local curvature of equiphase lines calculated from the curve slope; The type determination unit is used to determine that the topology type is a continuous response type if the monotonicity flag, the zero-point distribution, and the local curvature of the equiphase line satisfy a preset continuous response condition; and to determine that the topology type is a jump response type if the monotonicity flag, the zero-point distribution, and the local curvature of the equiphase line satisfy a preset jump response condition.
4. The information processing system for regulating a neural oscillation system according to claim 3, characterized in that, The type determination unit includes: The first type determination subunit is used to determine that the topology type is a continuous response type when all the first preset conditions occur simultaneously; wherein, each of the first preset conditions is that the phase response curve is monotonically increasing as a whole, there are no jump discontinuities, and the local curvature of the equiphase line is less than the first preset curvature threshold and the change is smooth. The second type determination subunit is used to determine that the topology type is a jump response type when all the second preset conditions occur simultaneously; wherein, each of the second preset conditions is that the phase response curve is non-monotonic, there is at least one jump discontinuity, the local curvature of the equiphase line is greater than the second preset curvature threshold and there is a folded shape. Wherein, the second preset curvature threshold is greater than the first preset curvature threshold.
5. The information processing system for regulating a neural oscillation system according to claim 1, characterized in that, The phase-reset electrical stimulation strategy characterizes each round of stimulation as including double-pulse electrical stimulation, and the overall arrangement sequence of the double pulses is based on the inherent period of the neural oscillation system. In each round of stimulation, the first pulse is output in the positive phase window corresponding to the negative value region of the phase response curve, and the second pulse is output after the transient response induced by the first pulse decays. The pulse interval and pulse intensity of the first and second pulses are determined based on the recovery time constant and isophase folding angle of the neural oscillation system.
6. The information processing system for regulating a neural oscillation system according to claim 5, characterized in that, The stimulation output module includes: The first pulse output unit is configured to generate a first control signal corresponding to the phase reset electrical stimulation strategy in each round of stimulation if the topology type is a jump response type, and output a first pulse to the neural oscillation system according to the first control signal to control the phase of the neural oscillation system to jump negatively. The second pulse output unit is used to generate a second control signal corresponding to the phase reset electrical stimulation strategy in each round of stimulation if the topology type is a jump response type, and output a second pulse to the neural oscillation system according to the second control signal, so as to push the phase of the neural oscillation system toward the target isophase line based on the nonlinear gain of the neural oscillation system.
7. The information processing system for regulating a neural oscillation system according to claim 5, characterized in that, The phase-traction electrical stimulation strategy characterizes each round of stimulation action as including single-pulse electrical stimulation, with the single pulse output in the phase window corresponding to the non-negative region of the phase response curve.
8. An information processing method for regulating a neural oscillation system, characterized in that, Applied to computer devices, including: Test perturbations are applied to the subject's neural oscillation system to construct a phase response curve, and the topology type of the neural oscillation system is determined based on the geometric characteristics of the phase response curve; When the topology type is continuous response, the phase traction electrical stimulation strategy is determined as the target phase modulation stimulation strategy. When the topology type is jump response type, the phase reset electrical stimulation strategy is determined as the target phase modulation stimulation strategy; A control signal corresponding to the target phase modulation stimulation strategy is generated, and electrical stimulation is output to the neural oscillation system according to the control signal to modulate the neural oscillation system.
9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the information processing method for modulating a neural oscillation system as described in claim 8.
10. A computer-readable storage medium, characterized in that, Used to store a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the information processing method for regulating a neural oscillation system as described in claim 8.