Closed-loop information processing systems, methods, devices, and media for neural rhythm signal modulation

Abstract

The application discloses a closed-loop information processing system, method, equipment and medium for nerve rhythm signal regulation, relates to the technical field of neural signal processing and brain-computer interface, and is applied to a computer device, and comprises the following steps: an initial type determination module divides a regulation period into multiple periods, and obtains a target dynamic type of a first period of a subject nerve rhythm signal; a drift signal determination module takes a current period from a second period, regulates according to a target stimulation mode, and determines a drift signal according to an actual and theoretical phase offset deviation; a cross-period determination module constructs a drift trajectory based on the signal, extrapolates and predicts a cross-period number according to a drift evolution parameter; a switching time determination module determines a pre-switching time when the cross-period number is less than a safety threshold; if the pre-switching time is in a next period, a first regulation module converts the target dynamic type and updates the current period; otherwise, a second regulation module directly updates the current period, and the method is cyclically executed. The method effectively suppresses the cross-period accumulation of the drift deviation.

Description

Technical Field

[0001] This invention relates to the fields of neural signal processing and brain-computer interfaces, and particularly to closed-loop information processing systems, methods, devices and media for the regulation of neural rhythm signals. Background Technology

[0002] In the application of techniques related to the modulation of neural rhythm signals, common approaches use a fixed stimulation pattern throughout the entire process. While some improved approaches have introduced feedback mechanisms, such as closed-loop deep brain stimulation that adjusts the stimulation intensity in real time based on changes in the amplitude or frequency band energy of local field potentials, adaptive neuromodulation systems that correct the stimulation frequency or pulse width online based on the phase or amplitude characteristics of neural oscillations, and intermittent recalibration schemes that collect neural signals during modulation intervals and re-estimate neuronal dynamic parameters before updating the stimulation pattern, these approaches are essentially still limited to simple corrections of scalar parameters such as stimulation intensity, frequency, and pulse width, failing to address the overall structural changes in the characteristics of neural rhythm dynamics.

[0003] Because of physiological processes such as short-range synaptic repression and slow adaptation of ion channels in the human nervous system, the actual sensitive window of the neural rhythm shifts over long-term regulation. This shift manifests as a phase response curve drift in neurodynamics. This "drift" is fundamentally different from the fast-timescale random "jitter" commonly found in neural signals: random jitter is characterized by random, non-directional fluctuations around a steady-state value, mainly stemming from inherent noise in membrane potential or measurement errors, and can usually be suppressed by averaging multiple stimuli; while phase response curve drift is a slow-timescale, gradual shift with a systematic direction. Its physiological basis lies in the processes of short-range synaptic repression and slow adaptation of ion channels in the human nervous system—these processes continuously alter the dynamic characteristics of the neural rhythm, causing the overall shape and phase sensitivity of the phase response curve to shift unidirectionally or cyclically over time. Therefore, while a fixed stimulation pattern or a regulation method that only adjusts the intensity parameter can overcome the white noise caused by random jitter to some extent, it cannot compensate for the gradual mismatch between the actual sensitive window of the neural rhythm and the preset stimulus phase caused by drift, leading to problems such as phase-lock failure and decreased regulation effectiveness.

[0004] In existing technologies, some adaptive neuromodulation schemes employ an intermittent stimulation-recalibration strategy, which involves periodically pausing intervention during modulation to reacquire neural signals and calibrate the dynamic type to update stimulation parameters. This approach directly interrupts the continuity of modulation and is a passive correction after phase-lock failure or a decline in modulation effectiveness. As a result, the deviation caused by phase response curve drift accumulates continuously over multiple modulation cycles, making it difficult to guarantee the reliability and adaptability of modulation throughout the entire process.

[0005] In summary, the field of neural rhythm signal modulation urgently needs a solution that can continuously track and compensate for phase response curve drift without interrupting stimulation, in order to overcome the major defect of drift bias accumulation across cycles in existing intermittent recalibration strategies and achieve full-process adaptation of stimulation patterns to the dynamic changes of neural rhythms. Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide a closed-loop information processing system, method, device, and medium for neural rhythm signal modulation, aiming to solve the problem in the prior art where phase response curve drift leads to a gradual mismatch between the stimulation pattern and the actual state of the neural rhythm, thereby causing cross-cycle cumulative errors. The specific solution is as follows: In a first aspect, the present invention discloses a closed-loop information processing method for regulating neural rhythm signals, applied to a computer device, comprising: The initial type determination module is used to divide the regulation time period into multiple regulation cycles and obtain the target dynamic type of the subject's neural rhythm signal in the first regulation cycle; A drift signal determination module is used to determine the second regulation cycle as the current regulation cycle, and to regulate the neural rhythm signal based on the target stimulation pattern corresponding to the target dynamics type in the current regulation cycle, and to determine the drift signal based on the deviation between the actual phase shift after regulation and the theoretical phase shift. The cycle crossing determination module is used to construct a drift trajectory based on the drift signal and extrapolate and predict the drift evolution parameters obtained based on the drift trajectory to predict the number of cycles crossing the dynamic topological boundary for the first time the drift trajectory crosses the boundary; wherein, the number of cycles crossing is used to characterize the number of remaining control cycles before the expected topological transformation of the current dynamic type. The switching time determination module is used to determine the pre-switching time if the number of spanned cycles is less than a preset safety cycle threshold. The first control module is used to convert the target dynamic type if the pre-switching time falls within the time range of the next control cycle, determine the converted dynamic type as the new target dynamic type, and perform neural rhythm signal control in the next control cycle. The second control module is used to perform neural rhythm signal control for the next control cycle if the pre-switching time does not fall within the time range of the next control cycle.

[0007] Optionally, the initial type determination module includes: The curve construction unit is used to apply a controlled perturbation sequence covering the entire regulation cycle to the subject's neural rhythm signal in the first regulation cycle to obtain the baseline phase response curve; The type determination unit is used to determine the target dynamic type of the subject's neural rhythm signal in the first modulation cycle based on the dynamic characteristics of the reference phase response curve; wherein, the dynamic characteristics include the monotonicity, zero-point distribution, discontinuity points, and isophase curvature of the reference phase response curve.

[0008] Optionally, the drift signal determination module includes: A rhythm regulation unit is used to determine a target stimulation pattern corresponding to the target dynamics type, and output a stimulation signal to the neural rhythm signal based on the target stimulation pattern during the current regulation cycle, so as to regulate the neural rhythm signal. The theoretical offset determination unit is used to obtain the theoretical phase offset based on the stimulus phase corresponding to the stimulus signal output and the preset phase response model. The drift determination unit is used to generate a phase drift amount from the difference between the actual phase shift after regulation and the theoretical phase shift, and to determine the phase drift amount as the drift signal of the current regulation cycle.

[0009] Optionally, the drift evolution parameters include drift velocity and drift acceleration; the cycle span determination module includes: The trajectory construction unit is used to update the dynamic features according to the drift signal, and to correlate and map the updated dynamic features of each control cycle to construct the drift trajectory. The evolution parameter acquisition unit is used to determine the drift velocity of the current control cycle based on the difference between the drift trajectory of the previous control cycle and the current control cycle, and to determine the drift acceleration of the current control cycle based on the difference between the drift velocity of the previous control cycle and the current control cycle.

[0010] Optionally, the cycle determination module includes: The extrapolation prediction unit is used to input the drift evolution parameters obtained based on the drift trajectory into the extrapolation equation to obtain the predicted drift trajectory position for multiple future control cycles. The period determination unit is used to compare the predicted drift trajectory position with the boundary conditions, and determine the number of control cycles that first touch or cross the boundary conditions in the predicted drift trajectory position as the number of crossing cycles; wherein, the boundary conditions are topological boundaries corresponding to any one or more of the dynamic characteristics among monotonicity, zero-point distribution, discontinuity points and equiphase curvature.

[0011] Optionally, the switching time determination module includes: The transition cycle number determination unit is used to determine the number of transition cycles based on the switching delay of different stimulation modes and the transient response time of neural rhythm signals to new stimulation modes. The pre-switching time determination unit is used to determine the pre-switching time based on the current control cycle, the number of cycles crossed, and the number of transition cycles.

[0012] Optionally, the pre-switching time determination unit includes: The initial pre-switching time determination subunit is used to subtract the number of transition cycles from the sum of the current control cycle and the number of cycles crossed to obtain the initial pre-switching time; The target pre-switching time determination subunit is used to determine the compensation amount based on the drift velocity and the drift acceleration, and to determine the difference between the initial pre-switching time and the compensation amount as the target pre-switching time.

[0013] Secondly, this invention discloses a closed-loop information processing method for regulating neural rhythm signals, comprising: The regulation period was divided into multiple regulation cycles, and the target dynamic type of the subject's neural rhythm signal in the first regulation cycle was obtained; The second regulation cycle is determined as the current regulation cycle. In the current regulation cycle, the neural rhythm signal is regulated based on the target stimulation pattern corresponding to the target dynamics type. The drift signal is determined based on the deviation between the actual phase shift after regulation and the theoretical phase shift. A drift trajectory is constructed based on the drift signal, and the drift evolution parameters obtained based on the drift trajectory are extrapolated and predicted to predict the number of cycles crossed when the drift trajectory first crosses the dynamic topological boundary; wherein, the number of cycles crossed is used to characterize the number of remaining control cycles before the expected topological transformation of the current dynamic type. If the number of cycles crossed is less than a preset safety cycle threshold, then the pre-switching time is determined; If the pre-switching time falls within the time range of the next regulation cycle, the target dynamics type is converted, the converted dynamics type is determined as the new target dynamics type, and the neural rhythm signal regulation is performed in the next regulation cycle. If the pre-switching time does not fall within the time range of the next regulation cycle, then the neural rhythm signal regulation of the next regulation cycle will be performed.

[0014] Thirdly, the present invention discloses an electronic device, comprising: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the steps of the aforementioned disclosed closed-loop information processing method for regulating neural rhythm signals.

[0015] Fourthly, the present invention 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 closed-loop information processing method for regulating neural rhythm signals.

[0016] The beneficial effects of this invention are as follows: This invention is applied to a computer device and includes: an initial type determination module, used to divide the modulation time period into multiple modulation cycles and obtain the target dynamic type of the subject's neural rhythm signal in the first modulation cycle; a drift signal determination module, used to determine the second modulation cycle as the current modulation cycle, and to modulate the neural rhythm signal based on the target stimulation pattern corresponding to the target dynamic type in the current modulation cycle, and to determine the drift signal based on the deviation between the actual phase shift and the theoretical phase shift after modulation; and a cycle crossing determination module, used to construct a drift trajectory based on the drift signal, and to extrapolate and predict the drift evolution parameters obtained based on the drift trajectory to predict the drift trajectory. The first crossing of the dynamic topology boundary corresponds to the number of crossing cycles; wherein, the number of crossing cycles is used to characterize the number of remaining control cycles before the expected topology change of the current dynamic type; a switching time determination module is used to determine the pre-switching time if the number of crossing cycles is less than a preset safety cycle threshold; a first control module is used to convert the target dynamic type if the pre-switching time falls within the time range of the next control cycle, determine the converted dynamic type as the new target dynamic type, and perform neural rhythm signal control for the next control cycle; a second control module is used to perform neural rhythm signal control for the next control cycle if the pre-switching time does not fall within the time range of the next control cycle. Therefore, this invention first divides the regulation cycle and determines the target dynamic type of the first cycle, providing a benchmark for subsequent regulation. It calculates the deviation between the actual phase shift and the theoretical phase shift cycle by cycle to obtain the drift signal, which can accurately capture the dynamic shift of the neural rhythm signal caused by physiological changes within each regulation cycle. Then, based on the drift signal, it constructs a drift trajectory and extrapolates to predict the number of remaining regulation cycles from the current regulation cycle to the point where the dynamic type changes, i.e., the number of cycles crossed. This allows for advance prediction of the time when the dynamic characteristics of the neural rhythm signal will change, avoiding passive responses to characteristic changes. Subsequently, when the number of cycles crossed is less than a safety threshold, a pre-switching time is determined, enabling preparation for switching before the actual change in dynamic type. Finally, based on whether the pre-switching time has been reached, the target dynamic type is dynamically adjusted and the regulation steps are executed cyclically. This ensures that the regulation strategy always follows the actual dynamic characteristics changes of the neural rhythm signal, reducing regulation failures caused by strategy lag, lowering the probability of inducing abnormal neural excitability, and improving the stability and continuity of neural rhythm signal regulation. Attached Figure Description

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

[0018] Figure 1 This is a structural diagram of a closed-loop information processing system for regulating neural rhythm signals disclosed in this invention; Figure 2 This is a structural diagram of a specific closed-loop information processing system for regulating neural rhythm signals disclosed in this invention; Figure 3 This is a flowchart of a closed-loop information processing method for regulating neural rhythm signals disclosed in this invention. Figure 4 This is a schematic diagram illustrating a specific drift trajectory and topological boundary crossing disclosed in this invention; Figure 5 This is a schematic diagram of a specific pre-switching time and hysteresis compensation disclosed in this invention; Figure 6 This is a specific control flowchart disclosed in this invention; Figure 7 This is a schematic diagram of a specific control law topology leading according to the present invention; Figure 8 This is a structural diagram of an electronic device disclosed in this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Current closed-loop neuromodulation techniques are generally based on the static limiting loop assumption, which idealizes neural rhythms (such as periodic firing of neurons and EEG oscillations) as a stable limiting loop whose shape, period, and amplitude do not change over time. Phase response curves and stimulation strategies are then derived based on this assumption. However, repetitive periodic stimulation can induce short-range synaptic repression and slow adaptation of ion channels, leading to cross-cycle drift in the phase response curve. This manifests as a gradual change in the curvature of isophase lines, a shift in the location of discontinuities, and a possible reversal of monotonicity after several cycles. When the topology of the control law mismatches with the topology of the drifted phase response curve, the system may enter a newly formed negative region after the corresponding cycle, causing phase locking failure, increased phase bounce, and even inducing abnormal neural excitability.

[0021] Neural oscillations can be mathematically modeled as limit cycles. The geometric shape of isophase lines in their phase space is determined by parameters such as membrane time constant, synaptic coupling strength, and ion channel opening probability. The slow changes in these parameters with repetitive stimulation will cause deformation of the isophase lines and geometric cross-period drift of the phase response curve. Short-range synaptic suppression causes the synaptic weight to decay exponentially under repetitive stimulation, and the coupling strength decreases with the period, which is equivalent to the contraction of the phase response curve amplitude. Slow adaptation of ion channels will change the neuronal resonance characteristics, causing the zero point and discontinuity point of the phase response curve to shift.

[0022] Therefore, the present invention provides a closed-loop information processing scheme for the regulation of neural rhythm signals, which dynamically switches the stimulation mode before the stimulation mode becomes incompatible during the regulation of neural rhythm signals in order to adapt to the dynamic changes of the signal and ensure the regulation effect.

[0023] See Figure 1 As shown, this embodiment of the invention discloses a closed-loop information processing system for regulating neural rhythm signals, applied to a computer device, comprising: The initial type determination module 11 is used to divide the regulation time period into multiple regulation cycles and obtain the target dynamic type of the subject's neural rhythm signal in the first regulation cycle; The drift signal determination module 12 is used to determine the second regulation cycle as the current regulation cycle, and to regulate the neural rhythm signal based on the target stimulation pattern corresponding to the target dynamics type in the current regulation cycle, and to determine the drift signal based on the deviation between the actual phase shift after regulation and the theoretical phase shift. The cycle crossing determination module 13 is used to construct a drift trajectory based on the drift signal and extrapolate and predict the drift evolution parameters obtained based on the drift trajectory to predict the number of cycles crossing the dynamic topology boundary for the first time the drift trajectory crosses the boundary; wherein, the number of cycles crossing is used to characterize the number of remaining control cycles before the expected topological change of the current dynamic type. The switching time determination module 14 is used to determine the pre-switching time if the number of spanning cycles is less than a preset safety cycle threshold. The first control module 15 is used to convert the target dynamic type if the pre-switching time falls within the time range of the next control cycle, determine the converted dynamic type as the new target dynamic type, and perform neural rhythm signal control in the next control cycle. The second control module 16 is used to perform neural rhythm signal control for the next control cycle if the pre-switching time does not fall within the time range of the next control cycle.

[0024] It is understood that the initial type determination module 11 includes: a curve construction unit, used to apply a controlled perturbation sequence covering the entire regulation cycle to the subject's neural rhythm signal in the first regulation cycle to obtain a reference phase response curve; and a type determination unit, used to determine the target dynamic type of the subject's neural rhythm signal in the first regulation cycle based on the dynamic characteristics of the reference phase response curve; wherein the dynamic characteristics include the monotonicity, zero-point distribution, discontinuities, and isophase curvature of the reference phase response curve.

[0025] The initial type determination module 11 is used to divide the overall regulation time period into multiple sequential regulation cycles. In the first regulation cycle after the regulation is officially started, the target dynamic type corresponding to the subject's neural rhythm signal is obtained.

[0026] The initial type determination module 11 specifically includes a curve construction unit and a type determination unit. In the first regulation cycle, the curve construction unit applies a controlled perturbation sequence covering the entire regulation cycle duration to the subject's neural rhythm signal. By collecting neural rhythm response data under the perturbation effect, a baseline phase response curve is constructed. The type determination unit is used to extract key dynamic features from the baseline phase response curve to determine the target dynamic type of the subject's neural rhythm signal in the first modulation cycle. The dynamic features used for determination specifically include the monotonicity of the baseline phase response curve. Zero point distribution Discontinuity point characteristics and the curvature values ​​of equiphase lines .

[0027] In this embodiment, the drift signal determination module 12 is used to set the second regulation period as the current regulation period. Within the current regulation period, according to the target stimulation pattern that matches the determined target dynamics type, the module applies regulatory intervention to the subject's neural rhythm signal, and simultaneously records the actual phase shift of the neural rhythm signal after this regulation. Based on the baseline phase response curve established in the first regulation cycle, the theoretical phase shift corresponding to the current stimulus phase is calculated. Then, the difference between the actual phase offset and the theoretical phase offset is calculated. Finally, the drift signal that can reflect the cross-cycle changes of the phase response curve was determined.

[0028] In this embodiment, the drift evolution parameters include drift velocity and drift acceleration; the cycle determination module 13 includes: a trajectory construction unit 131, used to update the dynamic features according to the drift signal, and correlate and map the updated dynamic features of each control cycle to construct a drift trajectory; and an evolution parameter acquisition unit 132, used to determine the drift velocity of the current control cycle according to the difference between the drift trajectory of the previous control cycle and the current control cycle, and to determine the drift acceleration of the current control cycle according to the difference between the drift velocity of the previous control cycle and the current control cycle.

[0029] Specifically, the cycle determination module 13 includes a trajectory construction unit 131 and an evolution parameter acquisition unit 132. The trajectory construction unit 131 updates the dynamic characteristics such as monotonicity, zero-point distribution, discontinuities, and equiphase curvature based on the drift signal, and then correlates and maps the updated dynamic characteristics for each control cycle to construct a drift trajectory that reflects the dynamic changes of the phase response curve. ,in, This represents a monotonicity measure estimated based on the cumulative data from the first k periods. This indicates the zero-point position drift estimate. Indicates the depth evolution of discontinuities. This represents the change in the curvature of the isophase lines. The evolution parameter acquisition unit 132 is used to determine the drift velocity corresponding to the current control cycle by calculating the difference between the drift trajectory of the previous control cycle and the current control cycle. ,Right now Then, by calculating the difference in drift velocity between the previous control cycle and the current control cycle, the drift acceleration corresponding to the current control cycle is further determined. ,Right now It should be noted that the drift speed described in this embodiment... With drift acceleration Instead of differentiating with respect to time, it is based on discrete differences of the control cycle number, which is different from the dimensions of conventional "velocity" and "acceleration".

[0030] Furthermore, the cross-cycle determination module 13 includes: an extrapolation prediction unit 133, used to input the drift evolution parameters obtained based on the drift trajectory into the extrapolation equation to obtain the predicted drift trajectory positions for multiple future control cycles; and a cycle determination unit 134, used to compare the predicted drift trajectory positions with boundary conditions, and determine the number of control cycles in the predicted drift trajectory positions that first touch or cross the boundary conditions as the cross-cycle number; wherein, the boundary conditions are topological boundaries corresponding to any one or more of the dynamic characteristics among monotonicity, zero-point distribution, discontinuity points, and equiphase curvature.

[0031] Specifically, the period determination module 13 further includes an extrapolation prediction unit 133 and a period determination unit 134. The extrapolation prediction unit 133 is used to input drift evolution parameters such as drift velocity and drift acceleration obtained from the drift trajectory into the extrapolation equation. Based on this, the predicted drift trajectory positions corresponding to multiple future control cycles are calculated. The cycle determination unit 134 is then used to compare the obtained predicted drift trajectory positions with preset boundary conditions, and select the control cycle number N corresponding to the first touch or crossover of the boundary conditions from all predicted drift trajectory positions, and determine it as the number of crossover cycles. The boundary conditions are specifically the topological boundaries corresponding to any one or more of the following dynamic characteristics: monotonicity, zero distribution, discontinuities, and equiphase curvature. Standards, specifically, define topological boundaries. For the monotonicity measure M in parameter space to change from positive to negative (or the curvature of equiphase lines) Crossing the critical value The critical hypersurface of ).

[0032] In this embodiment, the switching time determination module 14 includes: a transition cycle number determination unit 141, used to determine the number of transition cycles based on the switching delay of different stimulation modes and the transient response time of the neural rhythm signal to the new stimulation mode; and a pre-switching time determination unit 142, used to determine the pre-switching time based on the current regulation cycle, the number of cycles crossed, and the number of transition cycles.

[0033] Specifically, the switching time determination module 14 includes a transition cycle number determination unit 141 and a pre-switching time determination unit 142. The transition cycle number determination unit 141 is used to comprehensively determine the number of transition cycles required for the switching process based on the switching delay generated when different stimulation modes switch to each other and the transient response time of the neural rhythm signal to the new stimulation mode. The pre-switching time determination unit is used to combine the current control cycle number, the calculated number of cycles crossed, and the number of transition cycles. The pre-switching time for switching the stimulation mode is determined according to the preset calculation logic. .

[0034] The first regulation module 15 is used to determine whether the pre-switching time falls within the time range of the next regulation cycle. If the determination result is yes, the currently set target dynamics type is converted, and the converted dynamics type is determined as the new target dynamics type. At the same time, the next regulation cycle is updated to the new current regulation cycle. Then, it returns to the execution step of regulating the neural rhythm signal according to the target stimulation pattern matching the target dynamics type within the current regulation cycle. For example, the regulation cycles are the first, second, third, fourth, and fifth cycles in sequence. The current cycle is the third cycle. After calculation, the number of cycles crossed is 2 and the number of transition cycles is 1, so the pre-switching time is the fourth cycle. Since the pre-switching time falls within the range of the next regulation cycle, i.e., the fourth cycle, the first regulation module converts the original target dynamics type (continuous response type) into the new target dynamics type (jump response type), determines the fourth cycle as the new current regulation cycle, and then jumps back to the step of regulating the neural rhythm signal based on the target stimulation pattern corresponding to the new target dynamics type within the current regulation cycle.

[0035] Furthermore, in the next control cycle after the current target dynamics type is converted, that is, in the first control cycle after the pre-switching operation is completed, the actual phase shift of the neural rhythm signal after control is measured to verify whether the actual phase shift is within the target acquisition domain corresponding to the new control law. If the deviation between the measured actual phase shift and the target acquisition domain exceeds the preset tolerance range, the emergency dynamics type recalibration process is immediately triggered as a backup measure to ensure the safe operation of the control.

[0036] The second regulation module 16 is used to determine whether the pre-switching time is not within the time range of the next regulation cycle. If the determination result is yes, there is no need to change the target dynamics type. The next regulation cycle is directly updated to the new current regulation cycle. Then, it jumps back to the execution step of regulating the neural rhythm signal according to the target stimulation pattern matching the target dynamics type within the current regulation cycle. For example, the regulation cycles are the first, second, third, fourth, fifth, and sixth cycles in sequence. The current cycle is the third cycle. After calculation, the number of cycles crossed is 3 and the number of transition cycles is 1, so the pre-switching time is the fifth cycle. Since the pre-switching time does not fall within the range of the next regulation cycle, i.e., the fourth cycle, the second regulation module directly determines the fourth cycle as the new current regulation cycle without changing the target dynamics type and the corresponding stimulation pattern. Then, it jumps back to the step of regulating the neural rhythm signal based on the target stimulation pattern corresponding to the original target dynamics type within the current regulation cycle.

[0037] The beneficial effects of this invention are as follows: This invention is applied to a computer device and includes: an initial type determination module, used to divide the modulation time period into multiple modulation cycles and obtain the target dynamic type of the subject's neural rhythm signal in the first modulation cycle; a drift signal determination module, used to determine the second modulation cycle as the current modulation cycle, and to modulate the neural rhythm signal based on the target stimulation pattern corresponding to the target dynamic type in the current modulation cycle, and to determine the drift signal based on the deviation between the actual phase shift and the theoretical phase shift after modulation; and a cycle crossing determination module, used to construct a drift trajectory based on the drift signal, and to extrapolate and predict the drift evolution parameters obtained based on the drift trajectory to predict the drift trajectory. The first crossing of the dynamic topology boundary corresponds to the number of crossing cycles; wherein, the number of crossing cycles is used to characterize the number of remaining control cycles before the expected topology change of the current dynamic type; a switching time determination module is used to determine the pre-switching time if the number of crossing cycles is less than a preset safety cycle threshold; a first control module is used to convert the target dynamic type if the pre-switching time falls within the time range of the next control cycle, determine the converted dynamic type as the new target dynamic type, and perform neural rhythm signal control for the next control cycle; a second control module is used to perform neural rhythm signal control for the next control cycle if the pre-switching time does not fall within the time range of the next control cycle. Therefore, this invention first divides the regulation cycle and determines the target dynamic type of the first cycle, providing a benchmark for subsequent regulation. It calculates the deviation between the actual phase shift and the theoretical phase shift cycle by cycle to obtain the drift signal, which can accurately capture the dynamic shift of the neural rhythm signal caused by physiological changes within each regulation cycle. Then, based on the drift signal, it constructs a drift trajectory and extrapolates to predict the number of remaining regulation cycles from the current regulation cycle to the point where the dynamic type changes, i.e., the number of cycles crossed. This allows for advance prediction of the time when the dynamic characteristics of the neural rhythm signal will change, avoiding passive responses to characteristic changes. Subsequently, when the number of cycles crossed is less than a safety threshold, a pre-switching time is determined, enabling preparation for switching before the actual change in dynamic type. Finally, based on whether the pre-switching time has been reached, the target dynamic type is dynamically adjusted and the regulation steps are executed cyclically. This ensures that the regulation strategy always follows the actual dynamic characteristics changes of the neural rhythm signal, reducing regulation failures caused by strategy lag, lowering the probability of inducing abnormal neural excitability, and improving the stability and continuity of neural rhythm signal regulation.

[0038] Reference Figure 2 As shown, this embodiment of the invention discloses a specific closed-loop information processing system module framework for neural rhythm signal modulation. Compared with the previous embodiment, this embodiment further explains and optimizes the technical solution. Specifically: The drift signal determination module 12 includes: a rhythm regulation unit 121, used to determine a target stimulation pattern corresponding to the target dynamics type, and output a stimulation signal to the neural rhythm signal based on the target stimulation pattern in the current regulation cycle to regulate the neural rhythm signal; a theoretical offset determination unit 122, used to obtain a theoretical phase offset based on the stimulation phase corresponding to the output of the stimulation signal and a preset phase response model; and a drift determination unit 123, used to generate a phase drift amount from the difference between the actual phase offset after regulation and the theoretical phase offset, and determine the phase drift amount as the drift signal of the current regulation cycle.

[0039] The pre-switching time determination unit 142 includes: an initial pre-switching time determination subunit 1421, used to subtract the number of transition cycles from the sum of the current control cycle and the number of cycles crossed to obtain the initial pre-switching time; and a target pre-switching time determination subunit 1422, used to determine a compensation amount based on the drift velocity and the drift acceleration, and to determine the difference between the initial pre-switching time and the compensation amount as the target pre-switching time.

[0040] Understandably, the drift signal determination module 12 includes a rhythm control unit 121, a theoretical offset determination unit 122, and a drift determination unit 123. The rhythm control unit 121 determines the target stimulus pattern corresponding to the target dynamics type and outputs a stimulus signal to the neural rhythm signal according to this target stimulus pattern within the current control cycle, thereby controlling the neural rhythm signal. The theoretical offset determination unit 122 predicts the phase offset that should occur under the stimulus phase at the time of stimulus signal output, thus obtaining the theoretical phase offset. The drift determination unit 123 compares the actual measured phase offset after control with the calculated theoretical phase offset, generates a phase drift amount from the difference between the two, and determines the phase drift amount as the drift signal corresponding to the current control cycle. ,Right now .

[0041] In this embodiment, the dynamic types are continuous response type and jump response type. The phase response curve of the continuous response type is monotonically smooth, without folds or discontinuities, and the isophase lines are in a regular shape, which is suitable for the stimulation mode of continuous phase traction. The phase response curve of the jump response type has folded structure and discontinuities, the monotonicity can be reversed, and the isophase lines show spiral deformation, which is suitable for the stimulation mode of discrete reset.

[0042] The pre-switching time determination unit 142 includes an initial pre-switching time determination subunit 1421 and a target pre-switching time determination subunit 1422. It should be noted that if the pre-switching command is issued at the pre-switching time, but the actual full activation of the control law requires a delay of several cycles, there is a lag in strategy activation. Therefore, it is necessary to correct the output time of the command used to implement the conversion of the target dynamic type. The initial pre-switching time determination subunit 1421 calculates the initial pre-switching time by adding the current control cycle and the number of cycles crossed, and then subtracting the number of transition cycles. ,Right now The target pre-switching time determination subunit 1422 is used to first determine the compensation amount based on the drift velocity and drift acceleration, then subtract the compensation amount from the initial pre-switching time to finally determine the target pre-switching time after hysteresis compensation. The specific formula is as follows: ; In the formula, This represents the stability coefficient.

[0043] As mentioned earlier, the drift speed With drift acceleration It is not obtained by differentiating with respect to time, but rather by the discrete difference based on the control cycle number. This term is dimensionless and its units are consistent with those of the other terms in the equation.

[0044] Therefore, performing target dynamics type conversion at the target pre-switching moment can avoid the situation where the strategy takes effect late.

[0045] See Figure 3 As shown, this embodiment of the invention discloses a closed-loop information processing method for neural rhythm signal modulation, applied to a computer device, comprising: Step S11: Divide the regulation time period into multiple regulation cycles and obtain the target dynamic type of the subject's neural rhythm signal in the first regulation cycle.

[0046] Step S12: Determine the second regulation cycle as the current regulation cycle, and regulate the neural rhythm signal based on the target stimulation pattern corresponding to the target dynamics type in the current regulation cycle, and determine the drift signal based on the deviation between the actual phase shift after regulation and the theoretical phase shift.

[0047] Step S13: Construct a drift trajectory based on the drift signal, and extrapolate and predict the drift evolution parameters obtained based on the drift trajectory to predict the number of cycles to be crossed; wherein, the number of cycles to be crossed represents the number of remaining control cycles from the current control cycle to the point where the dynamic type changes.

[0048] like Figure 4 As shown, Figure 4 The left figure shows the drift trajectory S(k) in parameter space, flowing from region A to region B, with the trajectory represented by the velocity vector. and acceleration Tangential. Figure 2 The right figure in the image shows the cross-period deformation of the corresponding equiphase geometry, which gradually evolves from a smooth concentric circle in the first period to a spiral folded structure in the Nth period.

[0049] Step S14: If the number of cycles crossed is less than a preset safety cycle threshold, then determine the pre-switching time.

[0050] like Figure 5 As shown, the timeline displays: current period k, predicted period to be crossed. Uncorrected pre-switching time Corrected pre-switching time The shaded area represents the transition delay of the control law. The correction term ensures that the phase response curve is exactly within the effective domain of the new strategy when the new control law takes effect.

[0051] Step S15: If the pre-switching time falls within the time range of the next regulation cycle, the target dynamics type is converted, the converted dynamics type is determined as the new target dynamics type, and the neural rhythm signal regulation of the next regulation cycle is performed.

[0052] Step S16: If the pre-switching time does not fall within the time range of the next regulation cycle, then the neural rhythm signal regulation of the next regulation cycle is performed.

[0053] like Figure 6 As shown, in the process of neural rhythm signal modulation, the first cycle calibration is completed first, then the initial control law is applied, followed by periodic stimulation and residual response measurement. The drift trajectory is updated based on the measurement results, and then topological boundary prediction is performed to determine the number of cycles the predicted boundary crosses. Is it less than the preset safety period threshold? If the determination result is negative, the current control law continues to be executed. If the determination result is positive, the pre-switching time after lag compensation correction is calculated, the control law topology pre-switching is executed, and finally the phase offset is verified to see if it falls into the target capture domain of the new control law to complete the closed-loop control process.

[0054] like Figure 7 As shown, in phase space, the geometry of the isophase line of the limit cycle of the target system slowly deforms with the period (solid arrow); the capture domain boundary of the control law (dashed box) migrates in advance through pre-switching, always leading the deformation of the isophase line by more than half a cycle.

[0055] This invention designs the following mechanisms: 1) A residual phase response cross-cycle tracking mechanism, which continuously constructs the drift trajectory by comparing the measured response with the reference phase response curve of the first cycle cycle by cycle, replacing the traditional method of only a single static measurement in the first cycle; 2) A phase response curve drift trajectory velocity and acceleration extrapolation prediction mechanism, which extrapolates and predicts the topological boundary crossing cycle based on the velocity and acceleration characteristics of the drift trajectory in the parameter space, realizing a paradigm upgrade from passive response to active prediction; 3) A topological boundary crossing pre-switching mechanism, which actively executes the topological transformation of the control law at the pre-switching moment before the predicted actual crossing of the topological boundary, ensuring the control policy 4) Control law lag compensation mechanism, based on drift acceleration to correct the pre-switching time, compensates for the extra drift generated during the switching transition delay stage, and ensures that the effective time of the control strategy is accurately matched with the state of the target system; 4) Cross-cycle optimization mechanism, breaks through the limitation of existing technologies that only adjust scalar parameters such as stimulation intensity and frequency, and integrates the mathematical topology of the dynamic switching control law throughout the intervention process; 6) Drift kinetic physical constraint mechanism, attributes the drift of the phase response curve to two quantifiable neurophysiological mechanisms: short-range synaptic suppression and slow adaptation of ion channels, giving the drift trajectory a clear neurophysiological interpretability.

[0056] Furthermore, embodiments of the present invention also provide an electronic device. Figure 8 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 the invention.

[0057] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. 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 closed-loop information processing method for neural rhythm signal modulation disclosed in any of the foregoing embodiments.

[0058] 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 invention, 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.

[0059] 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.

[0060] 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.

[0061] The operating system 221 manages and controls the various hardware devices and computer programs 222 on the electronic device, enabling the processor 21 to perform calculations and processing on the massive amounts of data 223 in the memory 22. The computer program 222, in addition to including a computer program capable of performing the closed-loop information processing method for neural rhythm signal modulation executed by the electronic device as disclosed in any of the foregoing embodiments, 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.

[0062] Furthermore, the present invention 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 closed-loop information processing method for regulating neural rhythm signals. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0063] 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.

[0064] 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 implement the described functions using different methods for each specific application, but such implementation should not be considered beyond the scope of the invention. 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.

[0065] 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.

[0066] The foregoing has provided a detailed description of a closed-loop information processing system, method, device, and medium for regulating neural rhythm signals 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, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A closed-loop information processing system for regulating neural rhythm signals, characterized in that, Applied to computer devices, including: The initial type determination module is used to divide the regulation time period into multiple regulation cycles and obtain the target dynamic type of the subject's neural rhythm signal in the first regulation cycle; A drift signal determination module is used to determine the second regulation cycle as the current regulation cycle, and to regulate the neural rhythm signal based on the target stimulation pattern corresponding to the target dynamics type in the current regulation cycle, and to determine the drift signal based on the deviation between the actual phase shift after regulation and the theoretical phase shift. The cycle crossing determination module is used to construct a drift trajectory based on the drift signal and extrapolate and predict the drift evolution parameters obtained based on the drift trajectory to predict the number of cycles crossing the dynamic topological boundary for the first time the drift trajectory crosses the boundary; wherein, the number of cycles crossing is used to characterize the number of remaining control cycles before the expected topological transformation of the current dynamic type. The switching time determination module is used to determine the pre-switching time if the number of spanned cycles is less than a preset safety cycle threshold. The first control module is used to convert the target dynamic type if the pre-switching time falls within the time range of the next control cycle, determine the converted dynamic type as the new target dynamic type, and perform neural rhythm signal control in the next control cycle. The second control module is used to perform neural rhythm signal control for the next control cycle if the pre-switching time does not fall within the time range of the next control cycle.

2. The closed-loop information processing system for neural rhythm signal modulation according to claim 1, characterized in that, The initial type determination module includes: The curve construction unit is used to apply a controlled perturbation sequence covering the entire regulation cycle to the subject's neural rhythm signal in the first regulation cycle to obtain the baseline phase response curve; The type determination unit is used to determine the target dynamic type of the subject's neural rhythm signal in the first modulation cycle based on the dynamic characteristics of the reference phase response curve; wherein, the dynamic characteristics include the monotonicity, zero-point distribution, discontinuity points, and isophase curvature of the reference phase response curve.

3. The closed-loop information processing system for neural rhythm signal modulation according to claim 2, characterized in that, The drift signal determination module includes: A rhythm regulation unit is used to determine a target stimulation pattern corresponding to the target dynamics type, and output a stimulation signal to the neural rhythm signal based on the target stimulation pattern during the current regulation cycle, so as to regulate the neural rhythm signal. The theoretical offset determination unit is used to obtain the theoretical phase offset based on the stimulus phase corresponding to the stimulus signal output and the preset phase response model. The drift determination unit is used to generate a phase drift amount from the difference between the actual phase shift after regulation and the theoretical phase shift, and to determine the phase drift amount as the drift signal of the current regulation cycle.

4. The closed-loop information processing system for neural rhythm signal modulation according to claim 3, characterized in that, The drift evolution parameters include drift velocity and drift acceleration; the cycle crossing determination module includes: The trajectory construction unit is used to update the dynamic features according to the drift signal, and to correlate and map the updated dynamic features of each control cycle to construct the drift trajectory. The evolution parameter acquisition unit is used to determine the drift velocity of the current control cycle based on the difference between the drift trajectory of the previous control cycle and the current control cycle, and to determine the drift acceleration of the current control cycle based on the difference between the drift velocity of the previous control cycle and the current control cycle.

5. The closed-loop information processing system for neural rhythm signal modulation according to claim 4, characterized in that, The cycle crossing determination module includes: The extrapolation prediction unit is used to input the drift evolution parameters obtained based on the drift trajectory into the extrapolation equation to obtain the predicted drift trajectory position for multiple future control cycles. The period determination unit is used to compare the predicted drift trajectory position with the boundary conditions, and determine the number of control cycles that first touch or cross the boundary conditions in the predicted drift trajectory position as the number of crossing cycles; wherein, the boundary conditions are topological boundaries corresponding to any one or more of the dynamic characteristics among monotonicity, zero-point distribution, discontinuity points and equiphase curvature.

6. The closed-loop information processing system for neural rhythm signal modulation according to any one of claims 4 or 5, characterized in that, The switching time determination module includes: The transition cycle number determination unit is used to determine the number of transition cycles based on the switching delay of different stimulation modes and the transient response time of neural rhythm signals to new stimulation modes. The pre-switching time determination unit is used to determine the pre-switching time based on the current control cycle, the number of cycles crossed, and the number of transition cycles.

7. The closed-loop information processing system for neural rhythm signal modulation according to claim 6, characterized in that, The pre-switching time determination unit includes: The initial pre-switching time determination subunit is used to subtract the number of transition cycles from the sum of the current control cycle and the number of cycles crossed to obtain the initial pre-switching time; The target pre-switching time determination subunit is used to determine the compensation amount based on the drift velocity and the drift acceleration, and to determine the difference between the initial pre-switching time and the compensation amount as the target pre-switching time.

8. A closed-loop information processing method for regulating neural rhythm signals, characterized in that, Applied to computer devices, including: The regulation period was divided into multiple regulation cycles, and the target dynamic type of the subject's neural rhythm signal in the first regulation cycle was obtained; The second regulation cycle is determined as the current regulation cycle. In the current regulation cycle, the neural rhythm signal is regulated based on the target stimulation pattern corresponding to the target dynamics type. The drift signal is determined based on the deviation between the actual phase shift after regulation and the theoretical phase shift. A drift trajectory is constructed based on the drift signal, and the drift evolution parameters obtained based on the drift trajectory are extrapolated and predicted to predict the number of cycles crossed when the drift trajectory first crosses the dynamic topological boundary; wherein, the number of cycles crossed is used to characterize the number of remaining control cycles before the expected topological transformation of the current dynamic type. If the number of cycles crossed is less than a preset safety cycle threshold, then the pre-switching time is determined; If the pre-switching time falls within the time range of the next regulation cycle, the target dynamics type is converted, the converted dynamics type is determined as the new target dynamics type, and the neural rhythm signal regulation is performed in the next regulation cycle. If the pre-switching time does not fall within the time range of the next regulation cycle, then the neural rhythm signal regulation of the next regulation cycle will be performed.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the steps of the closed-loop information processing method for neural rhythm signal modulation as described in claim 8.

10. A computer-readable storage medium, characterized in that, Used to store computer programs; wherein, when the computer programs are executed by a processor, they implement the steps of the closed-loop information processing method for neural rhythm signal modulation as described in claim 8.

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