Nerve regulation and control device and system based on deep electrode

Abstract

The embodiment of the invention provides a nerve regulation and control device and system based on a deep electrode, and relates to the technical field of nerve regulation and control. The stimulator is used for being electrically connected with at least one deep electrode, and the stimulator is configured to determine electroencephalogram activity distribution information based on an electroencephalogram signal of a target area acquired by at least one contact of the deep electrode; determining target stimulation information based on the electroencephalogram activity distribution information and the position information of each contact; based on the target stimulation information, controlling at least two target contacts to output stimulation signals to form a target envelope waveform so as to perform nerve regulation on the target area; the electroencephalogram activity distribution information is used for representing the activity degree of the electroencephalogram activity of the target area, and the target stimulation information comprises target contact information and stimulation parameter information corresponding to each target contact. According to the embodiment of the invention, the target stimulation information can be determined according to the real-time electroencephalogram signal of the patient, and the accuracy and treatment effect of nerve regulation and control are improved.

Description

Technical Field

[0001] The present application relates to the field of neuromodulation technology, and more specifically, to a neuromodulation device and system based on deep electrodes. Background Art

[0002] In the field of neuromodulation, low-frequency envelope waveform stimulation is an important technical means used to treat neurological diseases such as Parkinson's disease and epilepsy.

[0003] Traditional low-frequency envelope waveform stimulation methods usually use fixed stimulation information for stimulation. These stimulation information include stimulation parameters such as stimulation frequency, intensity and duration. During the treatment process, all parameters in the stimulation information remain unchanged, thereby reducing the accuracy of neural regulation and the treatment effect. Summary of the invention

[0004] The embodiments of the present application provide a deep electrode-based neuromodulatory device and system for solving the technical problem in the prior art of using fixed stimulation information for stimulation, which results in reduced accuracy and therapeutic effect of neuromodulation.

[0005] In a first aspect, an embodiment of the present application provides a neural regulation device based on a deep electrode, comprising: A stimulator, for being electrically connected to at least one deep electrode, and configured to determine EEG activity distribution information based on an EEG signal of a target area acquired by at least one contact of the deep electrode; determine target stimulation information based on the EEG activity distribution information and position information of each contact; and control at least two target contacts to output stimulation signals to form a target envelope waveform based on the target stimulation information, so as to perform neural regulation on the target area; The EEG activity distribution information is used to characterize the activity of the EEG activity in the target area, and the target stimulation information includes target contact information and stimulation parameter information corresponding to each target contact.

[0006] In one possible implementation, the stimulator includes: An acquisition module, used to acquire an EEG signal of a target area using at least one contact of a deep electrode; A parameter generation module is used to determine the distribution information of EEG activity based on EEG signals; and to determine the target stimulation information based on the EEG activity distribution information and the position information of each contact point through a machine learning model; The control module is used to control at least two target contacts to output stimulation signals to form a target envelope waveform based on the target stimulation information.

[0007] In one possible implementation, the stimulator includes: a preprocessing module; The preprocessing module is used to preprocess the EEG signal to obtain the processed EEG signal; the preprocessing includes filtering processing; The parameter generation module is specifically used to determine the distribution information of EEG activity based on the processed EEG signal.

[0008] In a possible implementation, the acquisition module is specifically used to use at least one contact of the deep electrode to collect the EEG signal of the target area according to the set sampling parameter information; the sampling parameter information includes at least one of the following: sampling frequency, resolution.

[0009] In a possible implementation, the parameter generation module is specifically used to extract characteristic parameters of the EEG signal based on the EEG signal, and determine the EEG activity distribution information based on the characteristic parameters; the characteristic parameters include power spectrum density, phase information, and entropy information.

[0010] In a possible implementation, the stimulation parameter information includes at least one of the following: stimulation frequency, stimulation duration, amplitude, and a stimulation frequency difference between the stimulation frequency and an adjacent target contact point.

[0011] In a possible implementation, the EEG activity distribution information includes an energy value; The parameter generation module is also used to divide the target area into at least two sub-areas based on the energy value of the target area, and take the average energy value of each sub-area as the energy value of the sub-area; through the machine learning model, the target stimulation information is determined based on the energy value of each sub-area and the position information of each contact.

[0012] In a possible implementation, the parameter generation module is also used to determine the EEG activity distribution map based on the EEG activity distribution information, and add corresponding color labels or energy value labels to the EEG activity distribution map based on the EEG activity distribution map and the energy values ​​of each sub-area.

[0013] In a possible implementation, the machine learning model is specifically used to adjust the current stimulation information for each sub-region based on an energy difference between a current energy value of the sub-region and a previous energy value of the sub-region to obtain adjusted stimulation information; The control module is also used to use the adjusted stimulation information as target stimulation information, and control at least two target contacts to output stimulation signals to form a target envelope waveform.

[0014] In one possible implementation, the machine learning model is specifically used to adjust the stimulation parameter information so that the intensity of the output stimulation signal is reduced when the energy difference is greater than a first preset value; and to adjust the stimulation parameter information so that the intensity of the output stimulation signal is increased when the energy difference is less than a second preset value; the first preset value is not less than the second preset value.

[0015] In a possible implementation, the frequency of the target envelope waveform is 8 Hz to 10 kHz; and / or the amplitude of the target envelope waveform is 0 volts to 30 volts.

[0016] In a second aspect, an embodiment of the present application provides a neural regulation system, comprising: at least one deep electrode and the neural regulation device based on the deep electrode of the first aspect; Each depth electrode is implanted in a target area, each depth electrode comprising at least one contact; Each contact is electrically connected to the stimulator, and the contact is used to collect brain electrical signals and / or output stimulation signals under the control of the stimulator.

[0017] The beneficial effects of the technical solution provided by the embodiment of the present application are: The stimulator of the embodiment of the present application is used to be electrically connected to at least one deep electrode, and can obtain the EEG signal of the target area through at least one contact of the deep electrode, so as to determine the EEG activity distribution information based on the EEG signal, and then determine the target stimulation information based on the EEG activity distribution information and the position information of each contact. Then, the stimulator can control at least two target contacts to output stimulation signals to form a target envelope waveform based on the target stimulation information, so as to perform neural regulation on the target area. Therefore, the embodiment of the present application can determine the target stimulation information based on the patient's real-time EEG signal, and the target stimulation information can be adjusted along with the patient's EEG signal. It can be applicable to the first treatment, or it can be used for continued treatment after at least one treatment, as well as in the application scenario of treatment. The target stimulation information is determined based on the patient's EEG signal, thereby improving the accuracy of neural regulation and the treatment effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in describing the embodiments of the present application are briefly introduced below.

[0019] Figure 1 A schematic diagram of a neural regulation system provided in an embodiment of the present application; Figure 2 A schematic diagram of an application scenario in which multiple contacts of a deep electrode provide an embodiment of the present application and output stimulation signals to form a target envelope waveform; Figure 3 A schematic diagram of the structure of a first stimulator provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of a second stimulator provided in an embodiment of the present application; Figure 5 A schematic diagram of a target envelope waveform provided in an embodiment of the present application.

[0020] 1-Neural regulatory system; 11-deep electrode, 110-contact; 12-Deep electrode-based neuromodulation devices; 120 - stimulator, 121 - acquisition module, 122 - parameter generation module, 123 - control module, 124 - preprocessing module. DETAILED DESCRIPTION

[0021] The embodiments of the present application are described below in conjunction with the drawings in the present application. It should be understood that the implementation methods described below in conjunction with the drawings are exemplary descriptions for explaining the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions of the embodiments of the present application.

[0022] It will be understood by those skilled in the art that, unless specifically stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the terms "including" and "comprising" used in the embodiments of the present application refer to that the corresponding features can be implemented as the presented features, information, data, steps, operations, elements and / or components, but do not exclude the implementation as other features, information, data, steps, operations, elements, components and / or combinations thereof supported by the technical field. It should be understood that when we say that an element is "connected" or "coupled" to another element, the one element may be directly connected or coupled to the other element, or it may refer to that the one element and the other element establish a connection relationship through an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein indicates at least one of the items defined by the term, for example, "A and / or B" indicates that it is implemented as "A", or is implemented as "A", or is implemented as "A and B".

[0023] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0024] In the field of neuromodulation, low-frequency envelope waveform stimulation is an important technical means for treating neurological diseases such as Parkinson's disease and epilepsy. However, there are some obvious deficiencies in the existing technology. Traditional low-frequency envelope waveform stimulation methods usually use fixed stimulation parameters for stimulation, including stimulation frequency, intensity and duration, which remain unchanged during the treatment process. This method ignores the dynamic changes of the patient's EEG signals, resulting in the inability to optimize the effectiveness of the stimulation in real time, thereby affecting the accuracy of neuromodulation and the treatment effect.

[0025] Specifically, the disadvantages of traditional methods are mainly reflected in the following aspects: 1. Lack of dynamic adjustment: Since the stimulation parameters are fixed and cannot be adjusted in real time according to the actual activity of the patient's EEG signals, the stimulation may be too strong or too weak, and the optimal therapeutic effect cannot be achieved.

[0026] 2. Insufficient stimulation accuracy: Stimulation methods with fixed parameters cannot accurately stimulate specific brain areas, which may lead to unnecessary side effects, such as overstimulation of healthy brain areas or failure to effectively stimulate diseased areas.

[0027] 3. Unable to achieve full-pathway regulation: Traditional methods cannot achieve accurate stimulation or inhibition of the entire target neural pathway, resulting in limited treatment effects or uneven stimulation effects. For example, some neurostimulation systems may use a simple electrode configuration that cannot achieve synergy between electrodes, resulting in limited stimulation effects. These systems may not be able to form an effective, fully covered, and accurate low-frequency envelope waveform stimulation network throughout a specific area, making it impossible to achieve overall regulation of the entire specific area.

[0028] 4. Single stimulation waveform: Since the stimulation waveform is fixed, it cannot be adaptively adjusted according to changes in the activity of the EEG signal, which limits the flexibility and adaptability of the stimulation method.

[0029] The deep electrode-based neuromodulatory device and system provided in this application are intended to solve the above technical problems in the prior art.

[0030] The following is a detailed description of the technical solution of the present application and how the technical solution of the present application solves the above technical problems with specific embodiments. It should be noted that the following implementations can refer to, draw on or combine with each other, and the same terms, similar features and similar implementation steps in different implementations will not be described repeatedly.

[0031] See also Figure 1 As shown, the present application embodiment provides a schematic diagram of the framework of a neural regulation system 1. Figure 1 As shown, the neuroregulatory system 1 includes: at least one deep electrode 11 and a deep electrode-based neuroregulatory device 12 according to an embodiment of the present application, and the deep electrode-based neuroregulatory device 12 includes a stimulator 120 .

[0032] Each deep electrode 11 is implanted in the target area, and each deep electrode 11 includes at least one contact 110. Each contact 110 is electrically connected to the stimulator 120, and the contact 110 is used to collect brain electrical signals and / or output stimulation signals under the control of the stimulator 120.

[0033] Optionally, the deep electrode 11 may be provided with a plurality of annular contacts 110, and the plurality of contacts 110 are spaced apart on the deep electrode 11. In some embodiments, a portion of the contacts 110 may be used to collect EEG signals, and a portion of the contacts 110 may be used to output stimulation signals; in other embodiments, the contacts 110 may collect EEG signals and output stimulation signals, and the same contact 110 may output stimulation signals and collect EEG signals at different times.

[0034] Optionally, the deep electrode 11 is implanted in a target area of ​​a target object, the target object being a patient, and the target area needs to cover an area of ​​the patient that needs to be treated so that the patient can be treated. The target area is determined in advance based on the patient's condition.

[0035] In the embodiment of the present application, the deep electrode 11 can be implanted into a target area (the target area refers to a specific location in a specific area that is actually selected and accurately determined by a doctor for placing electrodes or pacemakers to send electrical stimulation).

[0036] The embodiment of the present application may include at least two deep electrodes 11, and each deep electrode 11 may be provided with a plurality of contacts 110, so as to achieve synergistic effect between the electrodes, thereby solving the technical problem of limitation of stimulation effect.

[0037] See also Figure 2 As shown, the present application embodiment provides a schematic diagram of an application scenario in which multiple contacts 110 of a deep electrode 11 output stimulation signals to form a target envelope waveform. Figure 2 As shown, the deep electrode 11 is implanted in the brain of the target subject, and a plurality of contacts 110 are spaced apart on the deep electrode 11. Based on the target stimulation information, the plurality of target contacts 110 output stimulation signals to form a target envelope waveform. The target envelope waveform can be matched with the patient's lesion for treatment, thereby improving the effectiveness and accuracy of the treatment.

[0038] See also Figure 2 As shown, each target contact 110 can be used as a stimulation source, and the target envelope waveform is a waveform formed by combining multiple different stimulation sources. The frequency, amplitude, phase and waveform of the stimulation signal output by each stimulation source can be different.

[0039] The regulated device 12 of the embodiment of the present application includes: a stimulator 120. The stimulator 120 is used to be electrically connected to at least one deep electrode 11.

[0040] The stimulator 120 is configured to determine EEG activity distribution information based on the EEG signal of the target area acquired by at least one contact 110 of the deep electrode 11; determine target stimulation information based on the EEG activity distribution information and the position information of each contact 110; and control at least two target contacts 110 to output stimulation signals to form a target envelope waveform based on the target stimulation information, so as to perform neural regulation on the target area.

[0041] The EEG activity distribution information is used to characterize the activity of the EEG activity in the target area, and the target stimulation information includes target contact information and stimulation parameter information corresponding to each target contact 110 .

[0042] The stimulator 120 of the embodiment of the present application is used to be electrically connected to at least one deep electrode 11, and can obtain the EEG signal of the target area through at least one contact 110 of the deep electrode 11, so as to determine the EEG activity distribution information based on the EEG signal, and then determine the target stimulation information according to the EEG activity distribution information and the position information of each contact. Then, the stimulator 120 can control at least two target contacts 110 to output stimulation signals to form a target envelope waveform based on the target stimulation information, so as to perform neural regulation on the target area.

[0043] Therefore, the embodiments of the present application can determine the target stimulation information based on the patient's real-time EEG signals. The target stimulation information can be adjusted along with the patient's EEG signals. It can be applicable to the first treatment, or to the continued treatment after at least one treatment, as well as to the intermediate application scenarios of treatment. The target stimulation information is determined based on the patient's EEG signals, thereby improving the accuracy of neural regulation and the therapeutic effect.

[0044] Optionally, the target stimulation information includes target contact information and stimulation parameter information corresponding to each target contact 110, so as to determine which contacts 110 output stimulation signals and the stimulation parameters of the stimulation signals output by each contact 110, and then form the required target envelope waveform through the synergistic effect between the target contacts 110 to achieve neural regulation of the target area.

[0045] This application can determine the specific area where EEG signals need to be collected based on the patient's condition and treatment needs. The specific area refers to the brain area selected by the doctor based on the patient's specific condition and surgical purpose, where electrodes or pacemakers need to be implanted to send electrical stimulation, and the specific area is used as the target area.

[0046] The embodiments of the present application can achieve the acquisition of EEG signals during treatment, so as to monitor the changes of the patient's EEG signals, determine the target stimulation information based on the patient's current EEG signals, form a target envelope waveform, and carry out targeted treatment for the patient's current brain condition. Existing stimulation methods ignore the dynamic changes of the patient's current EEG signals during treatment, resulting in the inability to optimize the effectiveness of the stimulation in real time. The embodiments of the present application can optimize the effectiveness of the stimulation in real time.

[0047] See also Figure 3 As shown, the embodiment of the present application provides a structural schematic diagram of a first stimulator 120.

[0048] like Figure 3 As shown, the stimulator 120 includes: an acquisition module 121 , a parameter generation module 122 and a control module 123 .

[0049] The acquisition module 121 is used to acquire the EEG signal of the target area using at least one contact 110 of the deep electrode 11 .

[0050] The parameter generation module 122 is used to determine the EEG activity distribution information based on the EEG signal; and determine the target stimulation information based on the EEG activity distribution information and the position information of each contact 110 through a machine learning model.

[0051] The control module 123 is used to control at least two target contacts 110 to output stimulation signals to form a target envelope waveform based on the target stimulation information.

[0052] Optionally, the acquisition module 121 can acquire the EEG signal of the target area under the control of the control module 123. The parameter generation module 122 includes a machine learning model, and the target stimulation information is determined by using the machine learning model. The machine learning model can be obtained by learning the correspondence between the EEG signal, the target stimulation information, the target envelope waveform and the stimulation effect. The target stimulation information can be determined based on the EEG activity distribution information and the position information of each contact 110, so that at least two target contacts 110 output stimulation signals to form a target envelope waveform to perform neural regulation on the target area.

[0053] The control module 123 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present application. The control module 123 may also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. Similarly, the parameter generation module 122 may also be a CPU (Central Processing Unit) and a general-purpose processor, etc.

[0054] The stimulator 120 of the embodiment of the present application may also include a memory for storing various data of the stimulator 120, such as stimulation information, EEG signals, and machine learning models.

[0055] The memory may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium that can be used to carry or store computer programs and can be read by a computer, without limitation herein.

[0056] The memory is used to store the computer program for executing the embodiment of the present application, and the execution is controlled by the control module 123. The control module 123 is used to execute the computer program stored in the memory to realize the function of the control module 123.

[0057] The embodiment of the present application can provide a corresponding target envelope waveform according to the activity of the entire neural pathway of the characteristic area, thereby achieving neural stimulation or inhibition of the entire pathway and avoiding damage caused by unnecessary stimulation.

[0058] Furthermore, the embodiments of the present application avoid unnecessary stimulation of non-target areas by precisely controlling the stimulation waveform, reduce the damage caused by stimulation, achieve overall control of the entire target area, and improve the stimulation efficiency. Moreover, the embodiments of the present application can adjust the target envelope waveform in real time according to the activity of EEG activity, have strong adaptability, are suitable for different neural control needs, and have strong adaptability.

[0059] See also Figure 4 As shown, the present embodiment provides a schematic diagram of the structure of a second stimulator 120. The stimulator 120 includes: a pre-processing module 124.

[0060] The preprocessing module 124 is used to preprocess the EEG signal to obtain a processed EEG signal; the preprocessing includes filtering. The parameter generation module 122 is specifically used to determine the EEG activity distribution information based on the processed EEG signal.

[0061] The embodiment of the present application performs filtering processing on the collected EEG signal to remove noise and interference and extract effective EEG signal. The parameter generation module 122 of the embodiment of the present application performs data processing based on the processed EEG signal to obtain more accurate target stimulation information.

[0062] In some embodiments, the acquisition module 121 is specifically used to use at least one contact 110 of the deep electrode 11 to collect EEG signals of the target area according to the set sampling parameter information; the sampling parameter information includes at least one of the following: sampling frequency, resolution.

[0063] The embodiment of the present application can collect EEG signals according to the set parameters such as sampling frequency and resolution of the EEG signals to ensure the accuracy and reliability of the signals, thereby ensuring the effectiveness and precision of the treatment.

[0064] In some embodiments, the parameter generation module 122 is specifically used to extract characteristic parameters of the EEG signal based on the EEG signal, and determine the EEG activity distribution information based on the characteristic parameters; the characteristic parameters include power spectrum density, phase information, and entropy information.

[0065] The embodiment of the present application can extract characteristic parameters based on EEG signals, such as power spectral density (PSD), phase information, entropy information, etc., so as to facilitate subsequent targeted analysis and processing of EEG signals. Power spectral density refers to the measure of how the power of a signal is distributed with frequency, and is usually used to describe the energy distribution of a signal at different frequencies.

[0066] Specifically, entropy information is used to characterize the entropy of the characteristics of EEG signals, that is, the entropy of EEG characteristics. Entropy information is a quantitative indicator to measure the complexity of EEG signals. It can be used to describe the order and disorder of the signal, that is, the complexity of the nonlinear dynamic process of the signal. The concept of entropy originally came from thermodynamics and is used to describe the degree of disorder or uncertainty in the system. In EEG signal analysis, entropy is used to quantify the regularity and unpredictability of time series fluctuations.

[0067] The entropy of EEG features includes many types, such as wavelet entropy, multiscale entropy (MSE), approximate entropy, sample entropy, etc. Wavelet entropy is a quantitative indicator to measure the order and disorder of the multiscale dynamic behavior of nonlinear signals. It can provide information on the complexity of the nonlinear dynamic process of the signal and shows good application prospects in EEG signal analysis. The multiscale entropy algorithm is suitable for multiscale nonlinear signal analysis. It constructs a new sequence of the original signal at multiple scales to achieve the purpose of analyzing the time domain complexity of the signal at different time scales. Sample entropy is a nonlinear method used to measure the probability of generating a new pattern in a time series. It has been well applied in EEG feature extraction and can reflect the complexity of EEG. In addition, the equal probability symbolized sample entropy method combines symbolic dynamics with sample entropy for EEG signal analysis. It can quickly obtain the potential dynamic characteristics of short-time series and has great application value for EEG biofeedback technology.

[0068] Therefore, entropy information is an important tool for evaluating the complexity of EEG signals. It helps to reveal the dynamic mechanism of brain electrical activity and plays an important role in cognitive function research and disease diagnosis.

[0069] In some embodiments, the stimulation parameter information includes at least one of the following: stimulation frequency, stimulation duration, amplitude, and a stimulation frequency difference between the stimulation frequency and an adjacent target contact 110 .

[0070] In some embodiments, the parameter generation module 122 includes a machine learning model. The EEG activity distribution information includes an energy value; the parameter generation module 122 is further used to divide the target area into at least two sub-areas based on the energy value of the target area, and use the average energy value of each sub-area as the energy value of the sub-area; through the machine learning model, based on the energy value of each sub-area and the position information of each contact 110, determine the target stimulation information.

[0071] Optionally, the energy value of the EEG signal is used to analyze brain waves and provide targeted treatment to the target area. The energy value can be the average value of the relative energy of the EEG, which is a method for analyzing the characteristics of brain waves. It evaluates the state of brain activity by calculating the average energy of brain waves in different frequency bands. Brain waves are electrical signals generated when brain neurons are active. By measuring and analyzing brain waves, we can understand the functions and abnormalities of the brain.

[0072] In some embodiments, the parameter generation module 122 is also used to determine an EEG activity distribution map based on the EEG activity distribution information, and to add corresponding color labels or energy value labels to the EEG activity distribution map based on the EEG activity distribution map and the energy values ​​of each sub-region.

[0073] The embodiment of the present application extracts characteristic parameters of EEG signals, such as power spectral density (PSD), phase information, entropy information, etc., and can draw a distribution map of EEG activity, thereby facilitating the acquisition of distribution information.

[0074] At the same time, the embodiment of the present application draws a distribution map of EEG activity in the target area based on the extracted characteristic parameters, and can use different colors or energy levels to represent the activity of EEG activity. Furthermore, in some embodiments, we can also divide the target area into different sub-areas based on the activity of EEG activity, and use different colors or energy values ​​to represent the activity of each sub-area. Further, the embodiment of the present application generates a distribution map of EEG activity in the entire target area, which provides a basis for the subsequent adjustment of stimulation information.

[0075] In some embodiments, the machine learning model is specifically used to adjust the current stimulation information for each sub-region based on the energy difference between the current energy value of the sub-region and the previous energy value of the sub-region to obtain adjusted stimulation information.

[0076] The control module 123 is further configured to use the adjusted stimulation information as target stimulation information, and control at least two target contacts 110 to output stimulation signals to form a target envelope waveform.

[0077] The embodiment of the present application can transmit the key parameters in the target stimulation information to the target area to form a stimulation network for effective stimulation. Further, the stimulation effect is evaluated according to the activity of the entire neural pathway of the target area, and the key stimulation parameters are dynamically adjusted according to the stimulation effect.

[0078] In some embodiments, the machine learning model is specifically used to adjust the stimulation parameter information so that the intensity of the output stimulation signal is reduced when the energy difference is greater than a first preset value; when the energy difference is less than a second preset value, adjust the stimulation parameter information so that the intensity of the output stimulation signal is increased; the first preset value is not less than the second preset value.

[0079] The embodiment of the present application can analyze the activity of the entire neural pathway before and after the stimulation of the target area, and determine the neural pathway that needs to be stimulated or inhibited. If the energy difference is small, the stimulation needs to be enhanced later, and if the energy difference is large, the stimulation needs to be reduced.

[0080] For example, the first preset value is 3, the second preset value is 2, and when the energy difference is 4, the energy difference is large, and the stimulation parameter information is adjusted to reduce the intensity of the output stimulation signal; when the energy difference is 1, the energy difference is small, and the stimulation parameter information is adjusted to increase the intensity of the output stimulation signal. The first preset value and the second preset value of the embodiment of the present application are only used as an example. In actual applications, the first preset value and the second preset value can be adaptively adjusted as needed.

[0081] In the embodiment of the present application, the stimulation network can be optimized according to the activity of the neural pathway to achieve precise stimulation or inhibition of a specific neural pathway. At the same time, according to the change in the activity of the neural pathway, the stimulation parameters of the target stimulation information are dynamically adjusted to achieve adaptive and precise control. The embodiment of the present application not only improves the accuracy of neural control, but also achieves adaptive and precise control, which has significant advantages.

[0082] The embodiment of the present application can adopt a low-frequency envelope waveform stimulation method based on feedback control theory, and realize adaptive adjustment of stimulation parameters in stimulation information by establishing a dynamic model between EEG signals and stimulation effects. For example: real-time acquisition of EEG signals in the target area; establishment of a dynamic model between EEG signals and stimulation effects to describe the relationship between stimulation information and EEG signal activity; based on the dynamic model and real-time EEG signal activity, dynamic adjustment of various stimulation parameters of stimulation information through feedback control algorithm to obtain target stimulation information, thereby realizing accurate low-frequency envelope waveform stimulation network.

[0083] In some embodiments, the frequency of the target envelope waveform is 8 Hz to 10 kHz; and / or the amplitude of the target envelope waveform is 0 volts to 30 volts.

[0084] The DBS (Deep Brain Stimulation) used in the embodiments of the present application to treat epilepsy has a relatively wide range of stimulation frequencies. According to different studies and clinical practices, the stimulation frequency can range from 8 Hz (Hertz) to 250 Hz. In practical applications, the most commonly used stimulation frequencies are 25 Hz, 50 Hz, and 100 Hz. For DBS treatment of the anterior thalamic nucleus (ANT), the frequency range of high-frequency stimulation is 110 Hz-170 Hz. Therefore, the stimulation frequency range of DBS treatment of epilepsy can be adjusted according to the specific treatment plan and the patient's condition.

[0085] In practical applications, the stimulation frequency range can be 8Hz-25Hz, 25Hz-50Hz, 50Hz-100Hz, 100Hz-170Hz, 170Hz-250Hz, 250Hz-5KHz, and 5KHz-10KHz.

[0086] Optionally, the frequency ranges listed in the embodiments of the present application all include endpoint values, and the listed frequency ranges are only used as an example. In actual applications, the frequency ranges can be adaptively adjusted as needed.

[0087] Optionally, the amplitude of the target envelope waveform is 0 volts to 30 volts, which can be selected as needed, for example: 1v-10v, 10v-20v, 20v-30v, etc. In actual applications, the amplitude range can be adaptively adjusted as needed.

[0088] As an example, the embodiment of the present application takes two simplified independent sinusoidal stimulation sources as an example, that is, two target contacts 110 output stimulation signals. Each independent stimulation source can generate a specific high-frequency sinusoidal waveform. If it is represented by a×sin(ωt+β), the amplitude of the stimulation waveform is a, the frequency is ω, and the phase is β. The envelope waveform of the interference waveform also has amplitude, frequency and phase information.

[0089] Among them, the envelope waveform frequency of the interference waveform is affected by the frequency difference of the two independent stimulus source waveforms, the envelope waveform phase is affected by the phase difference of the two independent stimulus source waveforms, and the envelope waveform amplitude is affected by the amplitude difference of the two independent stimulus source waveforms. If A represents the amplitude of the envelope waveform, Ω represents the frequency of the envelope waveform, and B represents the phase of the envelope waveform, it can be expressed in mathematical language as: A=F(Δa), Ω=F(Δω), A=F(Δβ). This means that the frequency, phase and amplitude of the envelope waveform can be modulated by independently adjusting the waveform frequency, phase and amplitude of each stimulus source.

[0090] See also Figure 5 As shown, an embodiment of the present application provides a schematic diagram of a target envelope waveform. Figure 5 As shown in the figure, the horizontal axis is time and the vertical axis is amplitude. The target envelope waveform can be a waveform with both positive and negative polarities in the same period. Different amplitudes and frequencies of the envelope waveform mean different activation areas. By combining multiple different stimulation sources, the envelope waveform can be used to stimulate a wider area.

[0091] Based on the technical solution of the present application, the embodiments of the present application can achieve: 1. The embodiment of the present application can collect EEG signals of the target area in real time and adaptively adjust the stimulation waveform according to the activity of the EEG signals.

[0092] 2. The embodiment of the present application can pre-process the EEG signal to obtain the EEG activity distribution map of the target area, and use different colors or different energy values ​​to indicate the activity of the EEG activity.

[0093] 3. The embodiment of the present application can obtain key parameter information such as the target contact 110, the stimulation frequency, the frequency difference between the electrodes, etc. according to the activity of the EEG activity, the position of the deep electrode 11 and the contact 110.

[0094] 4. The embodiments of the present application can form an effective, full-coverage, and precise low-frequency envelope waveform stimulation network to achieve full-pathway neural stimulation or inhibition.

[0095] 5. The embodiment of the present application can stimulate the waveforms of different frequencies emitted by each stimulation contact 110 in the stimulation network to coordinate and restrict each other, avoid the damage caused by unnecessary stimulation, and achieve overall regulation of the entire target area.

[0096] 6. The embodiment of the present application can provide a stimulation waveform based on the activity of EEG activity, thereby achieving adaptive and precise regulation.

[0097] Compared with the prior art, the embodiments of the present application mainly solve the following technical problems: 1) The stimulation parameters of the low-frequency envelope waveform are fixed and cannot be adjusted in real time according to the actual activity of the patient's EEG signal; 2) Insufficient stimulation precision, which makes it impossible to accurately stimulate specific brain areas and may lead to technical problems such as unnecessary side effects; 3) It is impossible to achieve precise stimulation or inhibition of the entire neural pathway, resulting in technical problems that limit the therapeutic effect; 4) The stimulation waveform is single and cannot be adaptively adjusted according to changes in the activity of the EEG signal, which limits the flexibility and adaptability of the stimulation method; 5) The existing system uses a simple electrode configuration, which cannot achieve synergistic effects between electrodes, resulting in technical problems such as limited stimulation effects; 6) The existing system is unable to form an effective, full-coverage, and precise low-frequency envelope waveform stimulation network in the entire target area, and is unable to achieve overall regulation of the entire target area.

[0098] By applying the embodiments of the present application, at least the following beneficial effects can be achieved: 1. Improve the accuracy of neural regulation: The embodiment of the present application realizes adaptive adjustment based on the activity of EEG signals by real-time acquisition of EEG signals in the target area and determining the stimulation waveform according to the activity of EEG activity, thereby improving the accuracy of neural regulation. It can be applied to the first treatment, the continued treatment after at least one treatment, and the intermediate application scenario of treatment.

[0099] 2. Realize full-pathway neural stimulation or inhibition: The embodiment of the present application can give a corresponding target envelope waveform according to the activity of the entire neural pathway of the characteristic area, thereby realizing full-pathway neural stimulation or inhibition and avoiding damage caused by unnecessary stimulation.

[0100] 3. Reduce stimulation damage: The embodiments of the present application avoid unnecessary stimulation to non-target areas and reduce the damage caused by stimulation by precisely controlling the stimulation waveform.

[0101] 4. Improve stimulation efficiency: The embodiment of the present application realizes overall regulation of the entire target area and improves stimulation efficiency.

[0102] 5. Strong adaptability: The embodiment of the present application can adjust the target envelope waveform in real time according to the activity of EEG activity, has strong adaptability, and is suitable for different neural regulation needs.

[0103] It should be understood that, although each operation step is indicated by arrows in the flowchart of the embodiment of the present application, the implementation order of these steps is not limited to the order indicated by the arrows. Unless clearly stated herein, in some implementation scenarios of the embodiment of the present application, the implementation steps in each flowchart can be performed in other orders according to demand. In addition, some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on actual implementation scenarios. Some or all of these sub-steps or stages may be executed at the same time, and each sub-step or stage in these sub-steps or stages may also be executed at different times respectively. In different scenarios of execution time, the execution order of these sub-steps or stages may be flexibly configured according to demand, and the embodiment of the present application does not limit this.

[0104] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program that has a predetermined function and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories), or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0105] The above is only an optional implementation method for some implementation scenarios of the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the technical concept of the solution of the present application, other similar implementation methods based on the technical ideas of the present application are also within the protection scope of the embodiments of the present application.

Claims

1. A neural regulation device based on deep electrodes, characterized in that: include: A stimulator, electrically connected to at least one deep electrode, configured to determine EEG activity distribution information based on an EEG signal of a target area acquired by at least one contact of the deep electrode; determine target stimulation information based on the EEG activity distribution information and position information of each of the contacts; and control at least two target contacts to output stimulation signals to form a target envelope waveform based on the target stimulation information, so as to perform neural regulation on the target area; The EEG activity distribution information is used to characterize the activity of the EEG activity in the target area, and the target stimulation information includes target contact information and stimulation parameter information corresponding to each target contact.

2. The deep electrode-based neural control device according to claim 1, characterized in that: The stimulator comprises: An acquisition module, used to acquire an EEG signal of a target area using at least one contact of the deep electrode; A parameter generation module, used to determine the distribution information of brain electrical activity based on the brain electrical signal; and determine the target stimulation information based on the distribution information of brain electrical activity and the position information of each of the contacts through a machine learning model; The control module is used to control at least two target contacts to output stimulation signals to form a target envelope waveform based on the target stimulation information.

3. The deep electrode-based neural control device according to claim 2, characterized in that: The stimulator comprises: a pre-processing module; The preprocessing module is used to preprocess the EEG signal to obtain a processed EEG signal; the preprocessing includes filtering processing; The parameter generation module is specifically used to determine the EEG activity distribution information based on the processed EEG signal.

4. The deep electrode-based neural control device according to claim 2, characterized in that: The acquisition module is specifically used to use at least one contact of the deep electrode to collect the EEG signal of the target area according to the set sampling parameter information; the sampling parameter information includes at least one of the following: sampling frequency and resolution.

5. The deep electrode-based neural control device according to claim 2, characterized in that: The parameter generation module is specifically used to extract characteristic parameters of the EEG signal based on the EEG signal, and determine the EEG activity distribution information based on the characteristic parameters; the characteristic parameters include power spectrum density, phase information, and entropy information.

6. The deep electrode-based neural control device according to claim 1, characterized in that: The stimulation parameter information includes at least one of the following: stimulation frequency, stimulation duration, amplitude, and stimulation frequency difference between adjacent target contacts.

7. The deep electrode-based neural control device according to claim 2, characterized in that: The EEG activity distribution information includes energy value; The parameter generation module is further used to divide the target area into at least two sub-areas based on the energy value of the target area, and use the average energy value of each of the sub-areas as the energy value of the sub-area; Through a machine learning model, target stimulation information is determined based on the energy value of each sub-area and the position information of each contact point.

8. The deep electrode-based neural control device according to claim 7, characterized in that: The parameter generation module is also used to determine a brain electrical activity distribution map based on the brain electrical activity distribution information, and to add a corresponding color mark or energy value mark in the brain electrical activity distribution map based on the brain electrical activity distribution map and the energy value of each sub-region.

9. The deep electrode-based neural control device according to claim 7, characterized in that: The machine learning model is specifically used to adjust the current stimulation information for each of the sub-regions based on the energy difference between the current energy value of the sub-region and the previous energy value of the sub-region to obtain adjusted stimulation information; The control module is further configured to use the adjusted stimulation information as the target stimulation information, and control at least two target contacts to output stimulation signals to form a target envelope waveform.

10. The deep electrode-based neural regulation device according to claim 9, characterized in that: The machine learning model is specifically used to adjust the stimulation parameter information so that the intensity of the output stimulation signal is reduced when the energy difference is greater than a first preset value; and to adjust the stimulation parameter information so that the intensity of the output stimulation signal is increased when the energy difference is less than a second preset value; the first preset value is not less than the second preset value.

11. The deep electrode-based neural control device according to claim 1, characterized in that: The frequency of the target envelope waveform is 8 Hz to 10 kHz; and / or the amplitude of the target envelope waveform is 0 volts to 30 volts.

12. A neural regulation system, characterized in that: include: At least one deep electrode and a deep electrode-based neuromodulation device as described in any one of claims 1 to 11; Each of the deep electrodes is implanted in a target area, each of the deep electrodes comprising at least one contact; Each of the contacts is electrically connected to the stimulator, and the contacts are used to collect brain electrical signals and / or output stimulation signals under the control of the stimulator.

Images