CN117409946A - Personalized brain stimulation instrument control method, device, terminal and storage medium - Google Patents

Abstract

The invention discloses a personalized brain stimulation instrument regulation and control method, a device, a terminal and a storage medium, and relates to the technical field of intelligent regulation and control. The invention judges the user category by acquiring brain wave data of different brain regions of the user and analyzing signal cooperativity among the brain wave data. The user category can be used for knowing what user group the user belongs to, and a proper stimulation parameter combination is provided for the user in a targeted manner, so that the brain stimulation instrument can be objectively and accurately regulated and controlled. The problem of in prior art because there is individual difference between different users, transcranial electric stimulation instrument need to regulate and control according to professional's subjective judgement and experience, lead to regulating and control the required human cost of transcranial electric stimulation instrument and time cost higher is solved.

Description

Personalized brain stimulation instrument control method, device, terminal and storage medium

Technical field

The present invention relates to the field of intelligent control technology, and in particular to a personalized brain stimulation instrument control method, device, terminal and storage medium.

Background technique

With the advancement of neuroscience and biomedical engineering technology, the development speed of non-invasive neuromodulation is also accelerating. Transcranial electrical stimulation technology has the characteristics of high safety, few side effects, and easy operation. It has become a research hotspot among brain neuroscientists in recent years. Currently, due to individual differences between different users, transcranial electrical stimulation equipment needs to be adjusted based on the subjective judgment and experience of professionals, resulting in high labor and time costs for regulating transcranial electrical stimulation equipment.

Therefore, the existing technology still needs to be improved and developed.

Contents of the invention

The technical problem to be solved by the present invention is to provide a personalized brain stimulation instrument control method, device, terminal and storage medium in view of the above-mentioned defects of the prior art, aiming to solve the problem of individual differences between different users in the prior art. Transcranial electrical stimulation equipment needs to be adjusted based on the subjective judgment and experience of professionals, resulting in high labor and time costs for regulating transcranial electrical stimulation equipment.

The technical solutions adopted by the present invention to solve the problem are as follows:

In a first aspect, embodiments of the present invention provide a personalized brain stimulation instrument control method, which method includes:

Obtain the brain wave data corresponding to several brain areas of the target user, and calculate the signal synergy between each pair of the brain areas based on each of the brain wave data;

Determine the target user category according to the synergy of each of the signals, and determine the stimulation parameter combination according to the target user category;

The brain stimulation instrument used by the target user is controlled according to the stimulation parameter combination.

In one embodiment, calculating the signal cooperativity between each of the brain regions based on each of the brain wave data includes:

Calculate the phase amplitude coupling degree according to the brain wave data of the two brain regions;

The signal cooperativity between the two brain regions is calculated based on the phase amplitude coupling degree.

In one implementation, determining the target user category based on the synergy of each of the signals includes:

Screen out a number of brain area combinations with abnormal synergy from each of the brain areas based on the synergy of each of the signals;

The target user category is determined based on the combination of the brain regions with synergistic abnormalities.

In one embodiment, the combination of several synergistically abnormal brain areas is screened out from each of the brain areas based on the signal synergy, including:

Obtain signal cooperativity thresholds between each of the brain regions;

According to the cooperativity of each of the signals and the cooperativity threshold of each of the signals, a number of brain area combinations with abnormal synergy are screened out from each of the brain areas.

In one embodiment, the stimulation parameter combination includes stimulation sites and stimulation waveforms, and determining the stimulation parameter combination according to the target user category includes:

Determine the desired stimulation effect of the target user according to the target user category;

Obtain the stimulation effect labels corresponding to each of the brain areas, wherein the stimulation effect labels of each of the brain areas are used to reflect several stimulation effects associated with the brain area;

Match the desired stimulation effect with the stimulation effect labels of each of the brain regions, and determine several target brain regions according to the matching results;

Each target brain area is used as the stimulation site, and the stimulation waveform corresponding to each target brain area is determined based on the brain wave data of each target brain area.

In one embodiment, determining the stimulation waveform corresponding to each target brain area based on the brain wave data of each target brain area includes:

Determine the target waveform interval to be stimulated according to the brain wave data of each target brain area;

According to the local brain wave data of each of the target brain areas in the target waveform interval, determine the expected brain wave data corresponding to each of the target brain areas in the target waveform interval;

According to each of the local brain wave data and each of the expected brain wave data, determine the phase adjustment information and amplitude adjustment information corresponding to each of the target brain areas;

According to the phase adjustment information and the amplitude adjustment information of each target brain area, the stimulation waveform corresponding to each target brain area is constructed.

In one embodiment, determining the expected brain wave data corresponding to each target brain area in the target waveform interval based on the local brain wave data of each target brain area in the target waveform interval includes: :

Input each of the local brain wave data into a preset reinforcement learning model to obtain the expected brain wave data corresponding to each target brain area;

Calculate the update signal cooperativity between each pair of the brain areas according to each of the expected brain wave data;

Calculate a reward value based on the synergy of each update signal, and determine whether the reward value reaches a preset reward threshold;

If not, the reinforcement learning model is updated according to the reward value, and after the update, the step of inputting each of the local brainwave data into the preset reinforcement learning model is continued until the reward value reaches the The reward threshold is used to obtain the expected brain wave data corresponding to each of the target brain areas.

In a second aspect, embodiments of the present invention also provide a personalized brain stimulation instrument control device, which includes:

The data analysis module is used to obtain the brain wave data corresponding to several brain areas of the target user, and calculate the signal synergy between each pair of the brain areas based on each of the brain wave data;

A category analysis module, configured to determine a target user category according to the synergy of each signal, and determine a stimulation parameter combination according to the target user category;

An intelligent control module is used to control the brain stimulation instrument used by the target user according to the combination of stimulation parameters.

In a third aspect, embodiments of the present invention further provide a terminal, which includes a memory and more than one processor; the memory stores more than one program; and the program includes a program for executing any of the above. instructions for a personalized brain stimulation instrument control method; the processor is used to execute the program.

In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium on which a plurality of instructions are stored, and the instructions are suitable for being loaded and executed by a processor to implement any of the above personalized Steps of the brain stimulation instrument control method.

Beneficial effects of the present invention: Embodiments of the present invention determine the user category by acquiring brain wave data from different brain areas of the user and analyzing the signal synergy between the brain wave data. The user category can be used to know which user group the user belongs to, and provide the user with an appropriate combination of stimulation parameters, thereby achieving objective and accurate control of the brain stimulation instrument. It solves the problem in the existing technology that due to individual differences between different users, the transcranial electrical stimulation instrument needs to be adjusted based on the subjective judgment and experience of professionals, resulting in high labor costs and time costs for regulating the transcranial electrical stimulation instrument. question.

Description of the drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only These are some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without exerting creative efforts.

Figure 1 is a schematic flow chart of a personalized brain stimulation instrument control method provided by an embodiment of the present invention.

Figure 2 is a schematic module diagram of a personalized brain stimulation instrument control device provided by an embodiment of the present invention.

Figure 3 is a functional block diagram of a terminal provided by an embodiment of the present invention.

Detailed ways

The present invention discloses a personalized brain stimulation instrument control method, device, terminal and storage medium. In order to make the purpose, technical solutions and effects of the present invention clearer and clearer, the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention.

Those skilled in the art will understand that, unless expressly stated otherwise, the singular forms "a", "an", "the" and "the" used herein may also include the plural form. It should be further understood that the word "comprising" used in the description of the present invention refers to the presence of stated features, integers, steps, operations, elements and/or components, but does not exclude the presence or addition of one or more other features, Integers, steps, operations, elements, components and/or groups thereof. It will be understood that when we refer to an element being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Additionally, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. As used herein, the term "and/or" includes all or any unit and all combinations of one or more of the associated listed items.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It should also be understood that terms, such as those defined in general dictionaries, are to be understood to have meanings consistent with their meaning in the context of the prior art, and are not to be used in an idealistic or overly descriptive manner unless specifically defined as here. to explain the formal meaning.

In view of the above-mentioned defects of the prior art, the present invention provides a personalized brain stimulation instrument control method. The method is used to: obtain brain wave data corresponding to several brain areas of the target user, and calculate according to each of the brain wave data. The signal synergy between each of the brain areas; determining the target user category according to the signal synergy; determining the stimulation parameter combination according to the target user category; regulating the stimulation parameters used by the target user according to the stimulation parameter combination Brain stimulation equipment. The present invention determines the user category by acquiring brain wave data from different brain areas of the user and analyzing the signal synergy between the brain wave data. Through the user category, it can be known which user group the user belongs to, and the appropriate stimulation parameter combination can be provided to the user in a targeted manner, thereby achieving objective and accurate control of the brain stimulation instrument. It solves the problem in the existing technology that due to individual differences between different users, the transcranial electrical stimulation instrument needs to be adjusted based on the subjective judgment and experience of professionals, resulting in high labor costs and time costs for regulating the transcranial electrical stimulation instrument. question.

As shown in Figure 1, the method includes:

Step S100: Obtain brain wave data corresponding to several brain areas of the target user, and calculate the signal synergy between each pair of brain areas based on each of the brain wave data.

Specifically, the target user in this embodiment can be any user who has a need to use the brain stimulation instrument. There are differences in the brain states of different users, and there are also differences in the brain states of the same user in different scenes/times. The precise regulation of brain stimulation equipment needs to be guided by the user's current actual brain state. Therefore, this embodiment needs to obtain the current brain wave data of different brain areas of the target user, and comprehensively calculate the signal synergy between different brain areas through all the acquired brain wave data. Signal cooperativity can reflect the communication between brain areas and thus reflect the current brain state of the target user. Generally speaking, the better the brain state, the higher the signal cooperativity between brain regions, and vice versa.

In one implementation, calculating the signal cooperativity between each pair of the brain areas based on each of the brain wave data includes:

Calculate the phase amplitude coupling degree according to the brain wave data of the two brain regions;

The signal cooperativity between the two brain regions is calculated based on the phase amplitude coupling degree.

This embodiment uses two brain areas as an example to illustrate the calculation process of signal cooperativity. Specifically, the brain wave data of two brain regions are first obtained, and a target frequency band of interest can be selected for specific analysis. Extract the phase information and amplitude information of the target frequency band in the two brain wave data, and calculate the phase-amplitude coupling degree based on the extracted phase information and amplitude information. The higher the phase-amplitude coupling degree, the better the signal exchange between the two brain areas. If it is normal, the signal cooperativity is higher, and vice versa.

In one implementation, the phase information and amplitude information of two brain wave data are used to detect the occurrence frequency of the situation that the amplitude of the high-frequency time series oscillates at low frequency (for example, for two brain wave data A and B) In other words, the frequency at which the amplitude of A's high-frequency time series oscillates at the low frequency of B at the corresponding time point, or the frequency at which the amplitude of B's high-frequency time series oscillates at the low frequency of A at the corresponding time point), the higher the frequency High, the higher the phase amplitude coupling degree, the higher the signal cooperativity. In short, if there is phase-amplitude coupling between two brainwave data, the amplitude of the high-frequency time series will oscillate at low frequency.

In another implementation, the consistency of the amplitude values corresponding to different phase values is calculated through the phase information and amplitude information of the two brain wave data. For example, for two brainwave data A and B, the phase is extracted from the low-frequency signal of A, the amplitude is extracted from the high-frequency signal of B, the corresponding relationship between the phase and amplitude is determined based on the time point, and the different phase values are calculated. Corresponding amplitude consistency. The lower the consistency, the higher the phase amplitude coupling, and vice versa. In short, if there is phase-amplitude coupling between two brainwave data, then there will be an amplitude at a specific phase value that is significantly higher than other phase values; conversely, if there is no phase-amplitude coupling, then different phase values will correspond to The amplitudes will be relatively similar.

In another implementation, calculating the phase amplitude coupling degree based on the brain wave data of the two brain regions includes:

Obtain standard fusion oscillation characteristic data corresponding to the two brain areas, wherein the method for generating the standard fusion oscillation characteristic data includes: acquiring standard brain wave data corresponding to the two brain areas, wherein the standard brain wave data is obtained. The radio wave data is collected by users with normal brain states; standard oscillation feature data corresponding to the two standard brain wave data are obtained, and standard fusion oscillation feature data is generated based on the two standard oscillation feature data;

Obtain oscillation characteristic data corresponding to the brain wave data of the two brain regions, and generate fused oscillation characteristic data based on the two oscillation characteristic data;

Calculate oscillation similarity according to the standard fused oscillation feature data and the fused oscillation feature data;

The phase amplitude coupling degree is calculated based on the oscillation similarity.

Specifically, in this embodiment, the brain wave data of these two brain areas under normal communication are collected in advance from a user with a normal brain state, that is, two standard brain wave data are obtained. Then the oscillation characteristic data of these two standard brain wave data are fused to obtain standard fused oscillation characteristic data. In the actual application scenario, the current fused oscillation feature data of the two brain areas is calculated, the phase amplitude coupling degree of the standard fused oscillation feature data is taken as the full value, and the oscillation similarity between the fused oscillation feature data and the standard fused oscillation feature data is calculated. , the phase amplitude coupling degree of the current brain wave data of these two brain areas can be converted.

This embodiment provides three calculation methods for phase amplitude coupling degree. In actual application scenarios, you can choose to use one of the calculation methods, or choose to use multiple calculation methods to take the average.

As shown in Figure 1, the method also includes:

Step S200: Determine a target user category based on the signal synergy, and determine a stimulation parameter combination based on the target user category.

Specifically, different user groups have different connections of cranial nerves, so there will be differences in signal communication between brain areas, which can be quantified through the signal synergy between brain areas. In this embodiment, the user group to which the target user belongs can be determined through the signal synergy between different brain areas of the target user, that is, the target user category is obtained. Through the identified target user categories, the stimulation parameter combination suitable for the target users is accurately selected.

In an implementation manner, determining the target user category based on the synergy of each of the signals includes:

Screen out a number of brain area combinations with abnormal synergy from each of the brain areas based on the synergy of each of the signals;

The target user category is determined based on the combination of the brain regions with synergistic abnormalities.

Specifically, the signal synergy between two brain areas can reflect whether the signal communication between the two brain areas is normal. Therefore, this embodiment can screen out brains with abnormal synergy through the signal synergy between pairs of brain areas. District combination. Different user groups have different synergy abnormalities in brain areas. Therefore, it is possible to analyze which user group the target user should belong to based on the selected brain area combinations with synergy abnormalities, and then determine the user category corresponding to the target user, that is, obtain the target user category.

In one implementation, the combination of several brain areas with abnormal synergy is screened out from each of the brain areas based on the synergy of each of the signals, including:

Obtain signal cooperativity thresholds between each of the brain regions;

According to the cooperativity of each of the signals and the cooperativity threshold of each of the signals, a number of brain area combinations with abnormal synergy are screened out from each of the brain areas.

Specifically, in this embodiment, the signal cooperation threshold between each brain region can be preset, and the signal cooperation threshold is equivalent to the threshold of normal cooperation state. If the currently calculated signal cooperativity is lower than the corresponding signal cooperativity threshold, it means that the brain wave data of the two corresponding brain areas have abnormal synergy, that is, the signal communication between the two brain areas is abnormal. Thus, combinations of brain regions with synergistic abnormalities can be screened out.

In one implementation, the stimulation parameter combination includes stimulation sites and stimulation waveforms, and determining the stimulation parameter combination according to the target user category includes:

Determine the desired stimulation effect of the target user according to the target user category;

Obtain the stimulation effect labels corresponding to each of the brain areas, wherein the stimulation effect labels of each of the brain areas are used to reflect several stimulation effects associated with the brain area;

Match the desired stimulation effect with the stimulation effect labels of each of the brain regions, and determine several target brain regions according to the matching results;

Each target brain area is used as the stimulation site, and the stimulation waveform corresponding to each target brain area is determined based on the brain wave data of each target brain area.

Specifically, different user groups have different motivations for using brain stimulation equipment. For example, some user groups expect to use brain stimulation equipment to improve memory, and some user groups expect to use brain stimulation equipment to improve concentration. Therefore, different user groups have different expectations for brain stimulation equipment. There are differences in the desired stimulation effects achieved after the use of stimulation instruments. Different brain areas as stimulation sites will produce different stimulation effects. This embodiment determines the stimulation effect expected to be achieved by the target user category, and then selects the brain area that can achieve the stimulation effect as the target brain area. When using brain stimulation equipment, each target brain area is used as a stimulation site, and the appropriate stimulation waveform is set based on the brain wave data collected from each target brain area, thereby optimizing the local brain state of each target brain area. Then achieve the desired stimulation effect of the target users.

In one implementation, determining the stimulation waveform corresponding to each target brain area based on the brain wave data of each target brain area includes:

Determine the target waveform interval to be stimulated according to the brain wave data of each target brain area;

According to the local brain wave data of each of the target brain areas in the target waveform interval, determine the expected brain wave data corresponding to each of the target brain areas in the target waveform interval;

According to each of the local brain wave data and each of the expected brain wave data, determine the phase adjustment information and amplitude adjustment information corresponding to each of the target brain areas;

According to the phase adjustment information and the amplitude adjustment information of each target brain area, the stimulation waveform corresponding to each target brain area is constructed.

Specifically, an optimal synchronization interval is first determined by analyzing the brain wave data of each target brain area, that is, the target waveform interval to be stimulated is obtained. For example, the interval with the weakest phase-amplitude coupling of each brain wave data can be found as the target. waveform interval. Then, the local band corresponding to the target waveform interval is intercepted, that is, several local brainwave data are obtained. By comprehensively analyzing the current waveform of each local brain wave data, the respective corresponding adjustment targets are determined, that is, the expected brain wave data of each local brain wave data is obtained. For each target brain area, by comparing the local brain wave data of the target brain area with the corresponding expected brain wave data, the phase adjustment information and amplitude adjustment information of the target brain area are calculated, and then the phase adjustment information and amplitude adjustment are used Information-oriented construction of the stimulation waveform corresponding to the target brain area.

In one implementation, determining the expected brain wave data corresponding to each target brain area in the target waveform interval based on the local brain wave data of each target brain area in the target waveform interval includes: :

Input each of the local brain wave data into a preset reinforcement learning model to obtain the expected brain wave data corresponding to each target brain area;

Calculate the update signal cooperativity between each pair of the brain areas according to each of the expected brain wave data;

Calculate a reward value based on the synergy of each update signal, and determine whether the reward value reaches a preset reward threshold;

If not, the reinforcement learning model is updated according to the reward value, and after the update, the step of inputting each of the local brainwave data into the preset reinforcement learning model is continued until the reward value reaches the The reward threshold is used to obtain the expected brain wave data corresponding to each of the target brain areas.

Specifically, this embodiment builds a reinforcement learning model in advance. The input data of the reinforcement learning model is all local brain wave data, and the output data is the expected brain wave data corresponding to each local brain wave data. The reinforcement learning model itself has a reward mechanism that automatically updates parameters. It calculates the update signal synergy between each target brain area based on the expected brain wave data output in each round, and calculates the reward value through the synergy of all update signals. The reward value can be used Evaluate the quality of the optimization actions performed by the reinforcement learning model on each local brainwave data in this round, and then use it to guide the reinforcement learning model to self-update. If the corresponding reward value of this round does not reach the reward threshold, it means that the effect of electrical stimulation of each target brain area based on the expected brain wave data of this round cannot meet expectations, and they cannot be used as the final expected brain wave data of each target brain area. Based on the radio wave data, the reinforcement learning model also needs to optimize the strategy for executing actions; if the reward value corresponding to the round reaches the reward threshold, it means that the effect of electrical stimulation of each target brain area based on the expected brain wave data of the round as a reference can achieve the expected effect. , then use them as the final expected brain wave data of each target brain area.

As shown in Figure 1, the method also includes:

Step S300: Regulate the brain stimulation instrument used by the target user according to the stimulation parameter combination.

Specifically, the stimulation parameter combination is obtained by analyzing the brain wave data of the target user, so the stimulation parameter combination matches the current brain state of the target user. By controlling the brain stimulation equipment used by the target user through a combination of stimulation parameters, the current brain state of the target user can be effectively improved and a better experience can be brought to the target user.

Based on the above embodiments, the present invention also provides a personalized brain stimulation instrument control device, as shown in Figure 2, the device includes:

The data analysis module 01 is used to obtain the brain wave data corresponding to several brain areas of the target user, and calculate the signal synergy between each pair of the brain areas based on each of the brain wave data;

Category analysis module 02 is used to determine the target user category according to the synergy of each signal, and determine the stimulation parameter combination according to the target user category;

The intelligent control module 03 is used to control the brain stimulation instrument used by the target user according to the combination of the stimulation parameters.

In one implementation, the data analysis module 01 includes:

A coupling analysis unit, configured to calculate the phase amplitude coupling degree based on the brain wave data of the two brain regions;

A cooperativity calculation unit, configured to calculate the signal cooperativity between the two brain regions according to the phase amplitude coupling degree.

In one implementation, the category analysis module 02 includes:

A combination screening unit, configured to screen out a number of synergistically abnormal brain area combinations from each of the brain areas based on the synergy of each of the signals;

A category determination unit is configured to determine the target user category based on the combination of each of the brain areas with synergistic abnormalities.

In one implementation, the combined screening unit includes:

A threshold acquisition unit is used to acquire the signal cooperativity threshold between each of the brain areas;

A numerical comparison unit is used to screen out a number of brain area combinations with abnormal synergy from each of the brain areas based on the signal synergy and the signal synergy threshold.

In one implementation, the stimulation parameter combination includes stimulation sites and stimulation waveforms, and the category analysis module 02 also includes:

An effect determination unit, configured to determine the desired stimulation effect of the target user according to the target user category;

A label acquisition unit is used to obtain stimulation effect labels corresponding to each of the brain regions, wherein the stimulation effect label of each brain region is used to reflect several stimulation effects associated with the brain region;

An effect matching unit, configured to match the desired stimulation effect with the stimulation effect labels of each of the brain regions, and determine several target brain regions according to the matching results;

A waveform determination unit is configured to use each target brain area as the stimulation site, and determine the stimulation waveform corresponding to each target brain area based on the brain wave data of each target brain area.

In one implementation, the waveform determination unit includes:

An interval determination unit, configured to determine the target waveform interval to be stimulated based on the brain wave data of each of the target brain areas;

A local analysis unit, configured to determine the expected brain wave data corresponding to each of the target brain areas in the target waveform interval based on the local brain wave data of each of the target brain areas in the target waveform interval;

An information determination unit, configured to determine the phase adjustment information and amplitude adjustment information corresponding to each of the target brain areas based on each of the local brain wave data and each of the expected brain wave data;

A waveform construction unit is configured to construct the stimulation waveform corresponding to each of the target brain areas according to the phase adjustment information and the amplitude adjustment information of each of the target brain areas.

In one implementation, the local analysis unit includes:

A model calling unit, used to input each of the local brain wave data into a preset reinforcement learning model to obtain the expected brain wave data corresponding to each target brain area;

A cooperativity update unit, configured to calculate the update signal cooperativity between each pair of the brain areas according to each of the expected brain wave data;

A reward value calculation unit, configured to calculate a reward value based on the synergy of each update signal, and determine whether the reward value reaches a preset reward threshold;

an iterative update unit, configured to update the reinforcement learning model according to the reward value if not, and continue to execute the step of inputting each of the local brainwave data into the preset reinforcement learning model after the update until the When the reward value reaches the reward threshold, the expected brain wave data corresponding to each of the target brain areas is obtained.

Based on the above embodiments, the present invention also provides a terminal, the functional block diagram of which can be shown in Figure 3 . The terminal includes a processor, memory, network interface, and display screen connected through a system bus. Among them, the processor of the terminal is used to provide computing and control capabilities. The memory of the terminal includes non-volatile storage media and internal memory. The non-volatile storage medium stores operating systems and computer programs. This internal memory provides an environment for the execution of operating systems and computer programs in non-volatile storage media. The network interface of the terminal is used to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a personalized brain stimulation instrument control method. The terminal's display screen may be a liquid crystal display or an electronic ink display.

Those skilled in the art can understand that the principle block diagram shown in Figure 3 is only a block diagram of a partial structure related to the solution of the present invention, and does not constitute a limitation on the terminals to which the solution of the present invention is applied. Specific terminals may include There may be more or fewer parts than shown, or certain parts may be combined, or may have a different arrangement of parts.

In one implementation, more than one program is stored in the memory of the terminal, and is configured to be executed by more than one processor. The one or more programs include instructions for performing a personalized brain stimulation instrument control method.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage. In the media, when executed, the computer program may include the processes of the above method embodiments. Any reference to memory, storage, database or other media used in the various embodiments provided by the present invention may include non-volatile and/or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Synchlink DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

To sum up, the present invention discloses a personalized brain stimulation instrument control method, device, terminal and storage medium. The method is used to: obtain the brain wave data corresponding to several brain areas of the target user, and according to each brain The radio wave data calculates the signal synergy between each of the brain areas; determines the target user category according to the signal synergy; determines the stimulation parameter combination according to the target user category; regulates the target according to the stimulation parameter combination Brain stimulation device used by users. The invention determines the user category by acquiring brain wave data from different brain areas of the user and analyzing the signal synergy between the brain wave data. Through the user category, it can be known which user group the user belongs to, and the appropriate stimulation parameter combination can be provided to the user in a targeted manner, thereby achieving objective and accurate control of the brain stimulation instrument. It solves the problem in the existing technology that due to individual differences between different users, the transcranial electrical stimulation instrument needs to be adjusted based on the subjective judgment and experience of professionals, resulting in high labor costs and time costs for regulating the transcranial electrical stimulation instrument. question.

It should be understood that the application of the present invention is not limited to the above examples. Those of ordinary skill in the art can make improvements or changes based on the above descriptions. All these improvements and changes should fall within the protection scope of the appended claims of the present invention.

Claims (10)

1. A method of personalized brain stimulation apparatus modulation, the method comprising:

acquiring brain wave data corresponding to a plurality of brain regions of a target user, and calculating signal cooperativity between every two brain regions according to the brain wave data;

determining a target user category according to the signal cooperativity, and determining a stimulation parameter combination according to the target user category;

and regulating and controlling the brain stimulation instrument used by the target user according to the stimulation parameter combination.

2. The personalized brain stimulation instrument control method according to claim 1, wherein calculating signal cooperativity between each brain region according to each brain wave data comprises:

calculating phase amplitude coupling degree according to the brain wave data of the two brain areas;

and calculating the signal cooperativity between the two brain regions according to the phase amplitude coupling degree.

3. The method of claim 1, wherein said determining a target user class based on each of said signal cooperativity comprises:

screening a plurality of brain region combinations with abnormal synergy from each brain region according to the signal cooperativity;

and determining the target user category according to each brain area combination with abnormal coordination.

4. The personalized brain stimulation instrument control method of claim 3, wherein the screening of a plurality of synergistic abnormal brain region combinations from each of the brain regions based on each of the signal cooperativity comprises:

acquiring signal cooperativity threshold values between every two brain regions;

and screening out a plurality of brain region combinations with abnormal synergy from each brain region according to the signal cooperativity and the signal cooperativity threshold value.

5. The personalized brain stimulation instrument modulation method of claim 1, wherein the stimulation parameter combination comprises a stimulation site and a stimulation waveform, the determining the stimulation parameter combination according to the target user category comprises:

determining the expected stimulation effect of the target user according to the target user category;

obtaining stimulation effect labels corresponding to the brain regions respectively, wherein the stimulation effect label of each brain region is used for reflecting a plurality of stimulation effects associated with the brain region;

matching the expected stimulation effect with the stimulation effect labels of the brain areas, and determining a plurality of target brain areas according to the matching result;

and taking each target brain area as the stimulation site, and determining the stimulation waveforms corresponding to each target brain area according to the brain wave data of each target brain area.

6. The method according to claim 5, wherein determining the stimulation waveforms corresponding to the target brain regions according to the brain wave data of the target brain regions comprises:

determining a target waveform interval to be stimulated according to the brain wave data of each target brain region;

determining expected brain wave data corresponding to each target brain region in the target waveform region according to the local brain wave data of each target brain region in the target waveform region;

determining phase adjustment information and amplitude adjustment information respectively corresponding to each target brain region according to each local brain wave data and each expected brain wave data;

and constructing the stimulation waveforms corresponding to the target brain regions respectively according to the phase adjustment information and the amplitude adjustment information of the target brain regions.

7. The method for adjusting and controlling a personalized brain stimulation instrument according to claim 6, wherein determining the expected brain wave data corresponding to each target brain region in the target waveform region according to the local brain wave data of each target brain region in the target waveform region, respectively, comprises:

inputting each local brain wave data into a preset reinforcement learning model to obtain the expected brain wave data corresponding to each target brain region;

calculating update signal cooperativity between every two brain areas according to the expected brain wave data;

calculating a reward value according to the cooperativity of each updating signal, and judging whether the reward value reaches a preset reward threshold value or not;

if not, updating the reinforcement learning model according to the reward value, and continuing to execute the step of inputting the local brain wave data into the preset reinforcement learning model after updating until the reward value reaches the reward threshold value, so as to obtain the expected brain wave data corresponding to each target brain region.

8. A personalized brain stimulation apparatus modulation device, the device comprising:

the data analysis module is used for acquiring brain wave data corresponding to a plurality of brain regions of a target user, and calculating signal cooperativity between every two brain regions according to the brain wave data;

the category analysis module is used for determining a target user category according to the signal cooperativity and determining a stimulation parameter combination according to the target user category;

and the intelligent regulation and control module is used for regulating and controlling the brain stimulation instrument used by the target user according to the stimulation parameter combination.

9. A terminal comprising a memory and one or more processors; the memory stores more than one program; the program comprising instructions for performing the personalized brain stimulation instrument modulation method of any one of claims 1-7; the processor is configured to execute the program.

10. A computer readable storage medium having stored thereon a plurality of instructions adapted to be loaded and executed by a processor to carry out the steps of the personalized brain stimulation instrument modulation method according to any one of the preceding claims 1-7.