Stimulation system for deep brain stimulation, stimulation effect evaluation method, equipment and medium

Abstract

The invention provides a stimulation system for deep brain stimulation, a stimulation effect evaluation method, equipment and a medium. The method comprises the following steps: establishing a grid reference curve of scalp according to spatial position information of a multi-lead electroencephalogram electrode; selecting and setting position information of a plurality of groups of stimulation coils according to the grid reference curve, and calibrating the vector normal direction of each group of stimulation coils relative to the scalp; calculating spatial distribution of a spatial domain electromagnetic field; constructing electromagnetic field distribution of each stimulation coil under the time domain carrier; and extracting a low-frequency interference signal, and calculating a performance parameter according to the low-frequency interference signal to realize evaluation of the deep brain stimulation time interference effect. According to the invention, electromagnetic stimulation signals with different frequencies can be generated, high-quality electromagnetic signals can be generated, the generation of signals with different frequencies can be accurately controlled, and time domain and space domain stimulation effects can be considered at the same time, so that the deep brain stimulation time interference effect can be evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of non-invasive brain stimulation, and in particular to a stimulation system for deep brain stimulation and a stimulation effect evaluation method, device and medium. Background Art

[0002] In the field of neuroscience and brain stimulation, non-invasive brain stimulation technology has become an important means to study and treat neuropsychiatric diseases. Transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (TES) technologies have developed rapidly because of their ability to modulate brain activity and affect physiological and behavioral performance. However, these technologies face challenges in achieving stimulation of deep brain regions, mainly due to the trade-off between the deep penetration of the electric field (E-field) and spatial localization (called focality). Traditional TMS technology generates a magnetic field through a coil, which in turn generates an electric field in the brain area below the coil to activate specific nerve cells. However, due to the low-pass filtering characteristics of the neural membrane, high-frequency magnetic fields cannot directly activate neurons. To solve this problem, researchers explored the concept of temporal interferometry (TI), which generates two high-frequency electromagnetic fields with a small frequency difference through two independent coils, and uses the low-pass filtering characteristics of the neural membrane to enable neurons to follow the frequency difference of the two fields well at the intersection of the two electric fields. By adjusting the layout of the coil and the relevant parameters of magnetic stimulation, specific areas in the brain can be accurately located and activated.

[0003] Wang et al. studied the response of model cortical neurons to temporal interferometric stimulation (TIS) and related transcranial alternating current stimulation (tACS) patterns. Using a uniform field approximation, they simulated a range of stimulation parameters in biophysically realistic model cortical neurons, including different directions, frequencies, amplitude ratios, amplitude modulations, and phase differences of the electric field, and obtained thresholds for activation and conduction blockade. When the electric field vector generated by TIS is inconsistent in the two electric field directions, it exhibits rotation, and the TIS activation threshold is similar to that of high-frequency stimulation. However, at very high total electric field amplitudes, TIS can produce conduction blockade. Majid Memarian Sorkhabi et al. proposed a method to achieve focal and controllable stimulation through TMS. The method generates two high-frequency electric fields through two independent coils, and the time-varying magnetic field and the electric field penetration depth and activation volume under specific coil parameters are calculated by the finite element method. By controlling the intersection of the two electric fields, the optimal position and orientation of the two coils can be found to achieve deep stimulation and acceptable spatial spread. In addition, the target area can be dynamically driven by changing the voltage ratio of the coils instead of their mechanical orientation. The results demonstrate that TI technology is an effective method to improve stimulation depth and range and can serve as a reasonable alternative to complex coil designs.

[0004] At present, there is an urgent need for a deep brain stimulation system that can generate electromagnetic stimulation signals of different frequencies to generate high-quality electromagnetic signals and accurately control the generation of signals of different frequencies, as well as a stimulation effect evaluation method. Summary of the invention

[0005] In order to achieve the above-mentioned purpose and other advantages of the present invention, the first object of the present invention is to provide a stimulation effect evaluation method for deep brain stimulation, comprising the following steps:

[0006] A grid reference curve of the scalp is established based on the spatial position information of the multi-lead EEG electrodes;

[0007] Selecting and setting the position information of multiple groups of stimulation coils according to the grid reference curve, and calibrating the vector normal direction of each group of stimulation coils relative to the scalp;

[0008] Calculate the spatial distribution of electromagnetic fields in the space domain;

[0009] Construct the electromagnetic field distribution of each stimulation coil under the time domain carrier;

[0010] The low-frequency interference signal is extracted, and the performance parameters are calculated based on the low-frequency interference signal to achieve the evaluation of the time interference effect of deep brain stimulation.

[0011] Furthermore, before the step of establishing a grid reference surface of the scalp according to the multi-lead EEG electrode positions, the method further includes:

[0012] Acquire brain stimulation related parameters; wherein the brain stimulation related parameters include the number of groups, shape, stimulation frequency, and stimulation current of the stimulation coil.

[0013] Furthermore, the step of calibrating the vector normal direction of each group of stimulation coils relative to the scalp includes:

[0014] Encrypting the spatial position information of the EEG electrodes and establishing a boundary element segmentation network of the scalp;

[0015] Obtaining a normal vector of each boundary element node according to the boundary element segmentation network;

[0016] The vector normal direction of each set of stimulation coils relative to the scalp is calibrated by the normal vector of each boundary element node.

[0017] Furthermore, the step of calculating the spatial distribution of the electromagnetic field in the spatial domain includes:

[0018] Establishing a rotation model of multiple groups of stimulation coils relative to the normal direction of the vector;

[0019] Refer to the boundary element segmentation grid, control the effective rotation angle, increase the degree of freedom of the stimulation coil, and obtain a more accurate stimulation electromagnetic field distribution;

[0020] The position of the stimulation coil is optimized according to the stimulation electromagnetic field distribution.

[0021] Furthermore, the step of constructing the electromagnetic field distribution of each stimulation coil under the time domain carrier includes:

[0022] Construct a time distribution model of frequency interference to drive the signal source;

[0023] Establish a fast computational model of the stimulation coil and the simulation head model to calculate the spatial domain of the electromagnetic field induced by the stimulation coil;

[0024] The stimulation coil is set according to the grid reference curve and the angle is adjusted to quickly optimize the stimulation coil vector orientation.

[0025] Furthermore, the step of extracting the low-frequency interference signal and calculating the performance parameter according to the low-frequency interference signal to achieve the evaluation of the time interference effect of deep brain stimulation includes:

[0026] The low-frequency time interference signal is extracted by combining the electromagnetic field distribution in the time and space domains induced by the stimulation coil;

[0027] The maximum stimulation intensity was evaluated based on the electric field distribution;

[0028] Calculating the minimum value of the distance of all points in the half-value area with reference to the grid reference curve, and calculating the maximum value of the depth of all points in the half-value area to obtain the stimulation depth;

[0029] The area that meets the half value is integrated and divided by the above-mentioned stimulation depth to obtain the focal area.

[0030] The second object of the present invention is to provide an electronic device, comprising: a memory on which a program code is stored; a processor connected to the memory and implementing the above method when the program code is executed by the processor.

[0031] A third object of the present invention is to provide a computer-readable storage medium having program instructions stored thereon, wherein the program instructions implement the above method when executed by a processor.

[0032] The fourth object of the present invention is to provide a stimulation system for deep brain stimulation, which implements the above method, including a host computer, a control platform, a signal generator, a signal amplification module, and a power amplification module; wherein,

[0033] The host computer is used for the user to set the excitation signal parameters, activate the excitation signal generation function, and send the excitation signal parameter setting instructions to the control platform;

[0034] The control platform is used to receive the excitation signal parameter setting instruction and send it to the signal generator;

[0035] The signal generator is used to parse the excitation signal parameter setting instruction and generate an excitation signal according to the excitation signal parameters obtained by the parsing;

[0036] The signal amplification module is used to preliminarily amplify the excitation signal so that the amplitude of the excitation signal matches the power amplification module;

[0037] The power amplifier module is used to power amplify the signal after preliminary amplification to generate a high-frequency current for driving the coil to achieve deep brain stimulation.

[0038] Furthermore, the signal amplification module includes an analog switch, a hysteresis comparator, and an amplitude amplification circuit; wherein,

[0039] The analog switch is used to control whether the channel of the signal generator is effective;

[0040] The hysteresis comparator is used to implement level conversion to ensure that the analog switch receives a correct control signal;

[0041] The amplitude amplification circuit is used to realize hysteresis comparison and amplitude amplification.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention provides a stimulation system for deep brain stimulation and a stimulation effect evaluation method, device, and medium, which can not only generate electromagnetic stimulation signals of different frequencies and generate high-quality electromagnetic signals to achieve precise control of the generation of signals of different frequencies, but also can simultaneously consider the stimulation effects in the time domain and space domain to achieve the evaluation of the time interference effect of deep brain stimulation.

[0044] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0046] Figure 1 is a schematic diagram of a stimulation system for deep brain stimulation of Example 1;

[0047] Figure 2 The software flow chart of the host computer of Example 1 is as follows;

[0048] Figure 3 This is an overall schematic diagram of the signal amplification module of Example 1;

[0049] Figure 4 The internal logic diagram of the AD9959 chip of the DDS signal generator of Example 1;

[0050] Figure 5 is an overall schematic diagram of a DDS signal generator of Example 1;

[0051] Figure 6 This is a circuit diagram of a signal amplification module in Example 1;

[0052] Figure 7 The stimulation coil parameter calculation logic diagram of Example 2;

[0053] Figure 8 is a flow chart of the stimulation effect evaluation method for deep brain stimulation of Example 2;

[0054] Fig. 9 This is a flow chart of calibrating the vector normal direction of each group of stimulation coils relative to the scalp in Example 2;

[0055] Fig.10The flowchart of calculating the spatial distribution of electromagnetic field in the spatial domain of Example 2;

[0056] Fig.11 The electromagnetic field distribution flow chart of each stimulation coil under the time domain carrier of Example 2 is constructed;

[0057] Fig.12 This is a flow chart of extracting a low-frequency interference signal and calculating a performance parameter based on the low-frequency interference signal in Example 2;

[0058] Fig.13 is a schematic diagram of an electronic device of Example 3;

[0059] Fig.14 This is a schematic diagram of the storage medium of Example 4. DETAILED DESCRIPTION

[0060] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. It should be noted that, under the premise of no conflict, the embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0061] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0062] The figure numbers in this application are only used to distinguish the various steps in the scheme, and are not used to limit the execution order of the various steps. The specific execution order is subject to the description in the specification.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0064] In non-invasive brain stimulation technology, TI (time interferometry) technology is an effective method to improve the depth and range of stimulation. This method uses a high-frequency electromagnetic field generated by two independent coils with similar frequencies to achieve accurate response of neurons to the frequency difference between the two electric fields under the action of low-pass filtering of the neural membrane. The present invention provides a stimulation system for deep brain stimulation and a method for evaluating the stimulation effect, aiming to generate high-quality electromagnetic excitation signals to achieve accurate positioning and activation of specific brain area calculations. The stimulation system design includes an MCU control platform, a DDS signal generator, and a signal amplification module. The parameters are set through the host computer software to achieve accurate signal generation and convenient operation.

[0065] Example 1

[0066] A stimulation system for deep brain stimulation, which realizes adjustable frequency, phase and amplitude of the stimulation signal and is used for time-interference electromagnetic brain stimulation, such as Figure 1 As shown, the system includes a host computer, a control platform, a signal generator, a signal amplification module, a power amplification module, and a power supply module; wherein,

[0067] The host computer is used for the user to set the excitation signal parameters, activate the excitation signal generation function, and send the excitation signal parameter setting instructions to the control platform;

[0068] The control platform is used to receive the excitation signal parameter setting instruction and send it to the signal generator;

[0069] The signal generator is used to parse the excitation signal parameter setting instruction and generate an excitation signal according to the excitation signal parameters obtained by the parsing;

[0070] The signal amplification module is used to preliminarily amplify the excitation signal so that the amplitude of the excitation signal matches the power amplification module;

[0071] The power amplifier module is used to power amplify the signal after preliminary amplification to generate a high-frequency current for driving the coil to achieve deep brain stimulation.

[0072] In some embodiments, the control platform is an MCU control platform, and the signal generator uses a DDS signal generator. The entire system completes overall operation and logic control through the MCU control platform. The MCU control platform generates a sinusoidal signal of a specified frequency by writing instructions to the DDS signal generator module, which is initially amplified by the signal amplification module and then amplified by the audio power amplification module, so that it can generate a high-frequency current to drive the stimulation coil and meet the requirements of deep brain stimulation.

[0073] Optionally, the human-computer interaction software of the host computer is implemented based on the PyQt6 framework and runs on the Windows operating system. The software defines a set of communication protocols internally, connects to the microcontroller unit (MCU) processor through a Micro USB interface, and establishes a stable communication channel to ensure that data packets can be transmitted efficiently. The software allows users to set the parameters of the sine waveform through its visual user interface, such as building a graphical user interface (GUI), and sends these parameter information to the lower computer connected to the MCU control platform for processing. The sine waveform parameters include amplitude, phase and frequency, where the output range of the frequency is 1 to 10kHz, the setting range of the amplitude is 0 to 3Vpp, the phase is from 0 to 360°, and the number of output channels is 4 channels. The user can enter the required parameter values ​​through an intuitive interface. When the user completes the setting of the sine wave parameters or activates the waveform generation function, the host computer will send the corresponding parameter setting instructions to the downstream device according to the user's operation. After receiving the instruction, the lower computer will parse the parameters and generate the corresponding sine waveform accordingly. The design strategy ensures that the user has precise control over the waveform output to meet diverse test and application requirements. The logic block diagram of the host computer software is as follows: Figure 2 shown.

[0074] Optionally, the microcontroller unit (MCU) control platform uses the STM32F103ZET6 microcontroller chip as its control core, which has advanced processing capabilities and rich peripheral interfaces. The chip is combined with an RS232 serial port adapter to facilitate program downloading and debugging. The connection with the host computer is achieved through a Micro USB data cable, which facilitates the transmission of control commands and remote monitoring functions. In practical applications, the MCU control platform can receive data from the host computer and feed it back to the slave computer. The MCU control platform is equipped with a wealth of input and output (IO) interfaces, which are mainly used to connect to the direct digital synthesis (DDS) signal generator to generate analog sine wave excitation signals. In addition, the MCU is also responsible for controlling peripheral devices, including status indicators, signal output lights, and the on and off of analog switches. By accurately operating the registers of the STM32F103ZET6 chip, the MCU control platform can program the frequency, phase, and amplitude parameters of the DDS signal generator module to achieve precise signal generation and adjustment. This design improves the flexibility and scalability of the system.

[0075] Optionally, the DDS signal generator is controlled by digital instructions from the MCU control platform by reading the digital signal at the data input and storing it in a register. These data are processed by the AD9959 DDS chip, which can generate a sine wave output with a bandwidth of up to 200MHz, with a minimum frequency step of 1Hz and an adjustable output amplitude of up to 500mVPP, ultimately realizing the output of digital signals. The module integrates four synchronous and independent DDS channels, each of which supports fine frequency, phase and amplitude control, ensuring more than 65dB of channel isolation, and can perform up to 16 levels of modulation. The AD9959 chip also contains four 10-bit integrated DACs, providing a frequency tuning resolution of more than 0.12Hz and an output amplitude adjustment resolution of 10 bits. The signal passes through the 10-bit digital-to-analog converter (DAC) of the four channels to generate the required sine wave excitation signal. The clock input is provided by the onboard crystal oscillator. The internal logic structure of the AD9959 chip is as follows Figure 4 As shown, the system block diagram of the DDS signal generator is as follows Figure 5 shown.

[0076] In this embodiment, a set of signal amplification and level conversion circuits are designed for the analog signal output by direct digital synthesis (DDS) to achieve effective signal amplification and precise control. The signal amplification module is mainly used to match the amplitude of the signal generated by the DDS signal generator with the back-end power amplifier module so as to control the amplitude and frequency more accurately.

[0077] Optional, such as Figure 3 As shown in the figure, the signal amplification module mainly includes an analog switch, a hysteresis comparator, and an amplitude amplification circuit. The CD4066 chip is used to control whether the channel is valid through the microcontroller instruction. The hysteresis comparator is used as the MCU level conversion, and the amplitude amplification circuit uses the OPA4197 chip to realize hysteresis comparison and amplitude amplification.

[0078] Specifically, Figure 6 As shown in the figure, the circuit shows the key components of signal amplification, level conversion and analog switch control. The circuit includes two precision resistors R1 and R2 and an OPA4197 operational amplifier, which are used to preliminarily amplify the analog signal output by the DDS. The signal output is connected to the analog switch. The level conversion part is realized by controlling the voltage difference between the level and VREF. It is essentially a hysteresis comparator circuit, which converts the 3.3V logic level of the microcontroller into the ±5V logic level required by the analog switch. It includes two precision resistors R3 and R4 and an OPA4197 operational amplifier, which are used to set the threshold voltage of the hysteresis comparator. This conversion process ensures that the analog switch can receive the correct control signal to perform its switching function.

[0079] The stimulation system for deep brain stimulation provided in this embodiment can output intermediate frequency signals and drive stimulation coils after amplification for use in deep brain stimulation experimental tests.

[0080] Example 2

[0081] For a detailed description of the stimulation system for deep brain stimulation, please refer to the corresponding description in the above-mentioned system embodiment, which will not be repeated here.

[0082] In this embodiment, the stimulation effect evaluation for deep brain stimulation considers both the time domain and space domain stimulation effects, wherein the time domain characteristics need to be combined with the time interference model, refer to the number of stimulation coil groups, and establish the time distribution characteristics of frequency interference; the spatial domain calculation needs to establish an overall model of the stimulation coil and the simulation head model, including the precise positioning of the stimulation coil vector that is more in line with the actual application needs. This embodiment proposes a method for establishing a grid reference surface of the scalp based on the position of the EEG electrode, further realizing the calculation of the stimulation coil vector orientation, and calibrating the coil position. On the basis of the above, the spatial distribution of the electromagnetic field in the space domain is calculated; combined with the time domain carrier characteristics, the space domain data is expanded, and the time interference electromagnetic field model in the time-frequency domain is calculated; the low-frequency interference signal is further extracted, and the maximum stimulation intensity, stimulation depth, focusing and other performance parameters are calculated accordingly to achieve the evaluation of the time interference effect of deep brain stimulation.

[0083] The stimulation effect evaluation method corresponding to the stimulation system for deep brain stimulation provided in Example 1 is as follows: Figure 8 As shown, the following steps are included:

[0084] S1. Establish a grid reference curve of the scalp based on the spatial position information of multi-lead EEG electrodes;

[0085] In some embodiments, before the step of establishing a grid reference surface of the scalp according to the multi-lead EEG electrode positions, the method further includes:

[0086] Acquire brain stimulation related parameters; wherein the brain stimulation related parameters include the number of groups, shape, stimulation frequency, and stimulation current of the stimulation coil.

[0087] Specifically, Figure 7 As shown, in the stimulation effect evaluation model for deep brain stimulation, the number of stimulation coils, shape and other parameters are first determined, the stimulation frequency information and the stimulation current of each coil are given, and then the grid reference curve of the scalp is established with reference to the spatial position coordinates of the multi-lead EEG electrodes. The multi-lead EEG can be 64 / 128 / 256 channels.

[0088] S2, selecting and setting the position information of multiple groups of stimulation coils according to the grid reference curve, and calibrating the vector normal direction of each group of stimulation coils relative to the scalp;

[0089] In some embodiments, Fig. 9 As shown, the step of calibrating the vector normal direction of each group of stimulation coils relative to the scalp includes:

[0090] S21, encrypting the spatial position information of the EEG electrodes and establishing a boundary element segmentation network of the scalp;

[0091] S22, obtaining a normal vector of each boundary element node according to the boundary element segmentation network;

[0092] S23. Calibrate the vector normal direction of each group of stimulation coils relative to the scalp using the normal vector of each boundary element node.

[0093] S3, calculate the spatial distribution of electromagnetic fields in the spatial domain;

[0094] In some embodiments, Fig.10 As shown, the step of calculating the spatial distribution of electromagnetic fields in the spatial domain includes:

[0095] S31, establishing a rotation model of multiple groups of stimulation coils relative to the normal direction of the vector;

[0096] S32, referring to the boundary element segmentation grid, controlling the effective rotation angle, increasing the degree of freedom of the stimulation coil, and obtaining a more accurate stimulation electromagnetic field distribution;

[0097] S33. In particular, when the target area nerve fiber bundle has direction selectivity, the position of the stimulation coil can be optimized according to the stimulation electromagnetic field distribution.

[0098] Taking the Cartesian coordinate system (X, Y, Z) as an example, the rotation angles relative to the three axes are (φ, θ, ψ) respectively, and the rotation matrix A can be expressed as A = A Z A Y A X ,in

[0099]

[0100] S4, constructing the electromagnetic field distribution of each stimulation coil under the time domain carrier;

[0101] In some embodiments, Fig.11 As shown, the step of constructing the electromagnetic field distribution of each stimulation coil under the time domain carrier includes:

[0102] S41, constructing a time distribution model of frequency interference to drive a signal source;

[0103] Specifically, the time domain characteristics of the electromagnetic field induced by the coil need to be combined with the time interference model. For the kth group of coils, the driving current model is:

[0104] Ik =I kmax sin(2πf k t+φ k ),

[0105] The variable is defined as the maximum current intensity I kmax , frequency f k , phase φ k For the time interference model, the frequency f k Generally, it is in the kHz range, with high-frequency carrier signals and low-frequency signals. Different coil driving frequencies differ by less than 100 Hz, thereby constructing a low-frequency field envelope to induce deep neural cell responses. With reference to the number of stimulation coil groups, a time distribution model of frequency interference is established to drive the signal source.

[0106] S42. The spatial domain calculation of the electromagnetic field induced by the stimulation coil requires the establishment of a fast calculation model of the stimulation coil and the simulation head model, for example, open source electromagnetic field analysis and calculation software such as simNIBS.

[0107] S43, combining the stimulation coil set according to the grid reference curve and adjusting the angle to quickly optimize the stimulation coil vector orientation.

[0108] S5. Extract the low-frequency interference signal, and calculate the performance parameters based on the low-frequency interference signal to evaluate the temporal interference effect of deep brain stimulation.

[0109] In some embodiments, Fig.12 As shown, the steps of extracting the low-frequency interference signal and calculating the performance parameters according to the low-frequency interference signal to achieve the evaluation of the time interference effect of deep brain stimulation include:

[0110] S51, extracting low-frequency time interference signals by combining the time-domain and space-domain electromagnetic field distributions induced by the stimulation coil;

[0111] S52, evaluating the maximum stimulation intensity Emax according to the electric field distribution;

[0112] S53, calculating the minimum value of the distance of all points in the half-value area (Emax / 2) with reference to the grid reference curve, and calculating the maximum value of the depth of all points in the half-value area to obtain the stimulation depth Depth;

[0113] S54, the area that meets the half value is divided by the above-mentioned stimulation depth Depth to obtain the focal area area S 1 / 2 , the calculation formula is as follows:

[0114]

[0115] Among them, V 1 / 2 is the half-value volume, which refers to the volume exposed to water greater than or equal to Emax / 2. This representation describes the volume of the brain region relative to half of the peak electric field E max / 2 distribution characteristics. 1 / 2 The smaller it is, the more concentrated the stimulation area is; d 1 / 2 The larger the value, the stronger the stimulation ability to the deep target area; the focal area S 1 / 2 The smaller it is, the better the focus.

[0116] At this point, the maximum stimulation intensity, stimulation depth, focusing and other performance parameters of the time interference model for deep brain stimulation are realized, and the evaluation of the time interference effect of deep brain stimulation is realized.

[0117] Example 3

[0118] An electronic device, such as Fig.13 As shown, it includes: a memory on which program code is stored; a processor connected to the memory, and when the program code is executed by the processor, a stimulation effect evaluation method for deep brain stimulation is implemented. For a detailed description of the method, reference can be made to the corresponding description in the above method embodiment, which will not be repeated here.

[0119] Example 4

[0120] A computer readable storage medium such as Fig.14 As shown, program instructions are stored thereon, and when the program instructions are executed by the processor, a stimulation effect evaluation method for deep brain stimulation is implemented. For a detailed description of the method, reference may be made to the corresponding description in the above method embodiment, and no further description is given here.

[0121] The number of devices and processing scales described here are used to simplify the description of the present invention. Applications, modifications and variations of the present invention will be obvious to those skilled in the art.

[0122] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

[0123] The apparatus, electronic device, non-volatile computer storage medium and method provided in the embodiments of this specification correspond to each other, and therefore, the apparatus, electronic device and non-volatile computer storage medium also have beneficial technical effects similar to those of the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, electronic device and non-volatile computer storage medium will not be repeated here.

[0124] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software units for implementing the method and structures within the hardware component.

[0125] The systems, devices or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described separately by functions in various units. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or more software and / or hardware.

[0126] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0127] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0130] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0131] The specification may be described in the general context of computer-executable instructions executed by a computer, such as program units. Generally, program units include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program units may be located in local and remote computer storage media, including storage devices.

[0132] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0133] The above description is only an embodiment of this specification and is not intended to limit one or more embodiments of this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of one or more embodiments of this specification.

Claims

1. A method for evaluating the stimulation effect of deep brain stimulation, characterized in that: The following steps are involved: A grid reference curve of the scalp is established based on the spatial position information of the multi-lead EEG electrodes; Selecting and setting the position information of multiple groups of stimulation coils according to the grid reference curve, and calibrating the vector normal direction of each group of stimulation coils relative to the scalp; Calculate the spatial distribution of electromagnetic fields in the space domain; Construct the electromagnetic field distribution of each stimulation coil under the time domain carrier; The low-frequency interference signal is extracted, and the performance parameters are calculated based on the low-frequency interference signal to achieve the evaluation of the time interference effect of deep brain stimulation.

2. A method for evaluating stimulation effects of deep brain stimulation according to claim 1, characterized in that: Before the step of establishing a grid reference surface of the scalp according to the multi-lead EEG electrode positions, the method further includes: Acquire brain stimulation related parameters; wherein the brain stimulation related parameters include the number of groups, shape, stimulation frequency, and stimulation current of the stimulation coil.

3. A method for evaluating stimulation effects of deep brain stimulation according to claim 1, characterized in that: The step of calibrating the vector normal direction of each group of stimulation coils relative to the scalp comprises: Encrypting the spatial position information of the EEG electrodes and establishing a boundary element segmentation network of the scalp; Obtaining a normal vector of each boundary element node according to the boundary element segmentation network; The vector normal direction of each set of stimulation coils relative to the scalp is calibrated by the normal vector of each boundary element node.

4. A method for evaluating stimulation effects of deep brain stimulation as claimed in claim 3, characterized in that: The step of calculating the spatial distribution of electromagnetic fields in the spatial domain comprises: Establishing a rotation model of multiple groups of stimulation coils relative to the normal direction of the vector; Refer to the boundary element segmentation grid, control the effective rotation angle, increase the degree of freedom of the stimulation coil, and obtain a more accurate stimulation electromagnetic field distribution; The position of the stimulation coil is optimized according to the stimulation electromagnetic field distribution.

5. A method for evaluating stimulation effects of deep brain stimulation according to claim 1, characterized in that: The step of constructing the electromagnetic field distribution of each stimulation coil under the time domain carrier comprises: Construct a time distribution model of frequency interference to drive the signal source; Establish a fast computational model of the stimulation coil and the simulation head model to calculate the spatial domain of the electromagnetic field induced by the stimulation coil; The stimulation coil is set according to the grid reference curve and the angle is adjusted to quickly optimize the stimulation coil vector orientation.

6. A method for evaluating stimulation effects of deep brain stimulation according to claim 1, characterized in that: The step of extracting the low-frequency interference signal and calculating the performance parameter according to the low-frequency interference signal to achieve the evaluation of the time interference effect of deep brain stimulation includes: The low-frequency time interference signal is extracted by combining the electromagnetic field distribution in the time and space domains induced by the stimulation coil; The maximum stimulation intensity was evaluated based on the electric field distribution; Calculating the minimum value of the distance of all points in the half-value area with reference to the grid reference curve, and calculating the maximum value of the depth of all points in the half-value area to obtain the stimulation depth; The area that meets the half value is integrated and divided by the above-mentioned stimulation depth to obtain the focal area.

7. An electronic device, characterized in that: include: A memory having program code stored therein; A processor is connected to the memory, and when the program code is executed by the processor, the method according to any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium, characterized in that: Program instructions are stored thereon, and when the program instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. A stimulation system for deep brain stimulation, implementing the method according to any one of claims 1 to 6, characterized in that: It includes a host computer, a control platform, a signal generator, a signal amplification module, and a power amplification module; among which, The host computer is used for the user to set the excitation signal parameters, activate the excitation signal generation function, and send the excitation signal parameter setting instructions to the control platform; The control platform is used to receive the excitation signal parameter setting instruction and send it to the signal generator; The signal generator is used to parse the excitation signal parameter setting instruction and generate an excitation signal according to the excitation signal parameters obtained by the parsing; The signal amplification module is used to preliminarily amplify the excitation signal so that the amplitude of the excitation signal matches the power amplification module; The power amplifier module is used to power amplify the signal after preliminary amplification to generate a high-frequency current for driving the coil to achieve deep brain stimulation.

10. A stimulation system for deep brain stimulation according to claim 9, characterized in that: The signal amplification module includes an analog switch, a hysteresis comparator, and an amplitude amplification circuit; wherein, The analog switch is used to control whether the channel of the signal generator is effective; The hysteresis comparator is used to implement level conversion to ensure that the analog switch receives a correct control signal; The amplitude amplification circuit is used to realize hysteresis comparison and amplitude amplification.

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