CN117409946A - Individualized brain stimulation instrument regulation and control method, device, terminal and storage medium - Google Patents
Individualized brain stimulation instrument regulation and control method, device, terminal and storage medium Download PDFInfo
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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
Technical Field
The invention relates to the technical field of intelligent regulation and control, in particular to a personalized brain stimulation instrument regulation and control method, device, terminal and storage medium.
Background
With advances in neuroscience and biomedical engineering, the development of non-invasive neuromodulation is also increasing. The transcranial electrical stimulation technology has the characteristics of high safety, small side effect and convenient operation, and is a research hotspot of brain neurologists in recent years. At present, because of individual differences among different users, the transcranial electric stimulation instrument needs to be regulated and controlled according to subjective judgment and experience of professionals, so that the labor cost and the time cost required for regulating and controlling the transcranial electric stimulation instrument are high.
Accordingly, there is a need for improvement and development in the art.
Disclosure of Invention
The invention aims to solve the technical problems that in the prior art, the personalized brain stimulation instrument regulation and control method, device, terminal and storage medium are provided, so that the problems that in the prior art, because individual differences exist among different users, the transcranial electrical stimulation instrument needs to be regulated and controlled according to subjective judgment and experience of professionals, and the labor cost and the time cost required for regulating and controlling the transcranial electrical stimulation instrument are high are solved.
The technical scheme adopted by the invention for solving the problems is as follows:
in a first aspect, an embodiment of the present invention provides a personalized brain stimulation device modulation method, 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.
In one embodiment, the calculating the signal cooperativity between each brain region according to each brain wave data includes:
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.
In one embodiment, 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.
In one embodiment, the screening the brain region combinations of the plurality of cooperative abnormalities from the brain regions according to the signal cooperative properties 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.
In one embodiment, the stimulation parameter combination includes a stimulation site and a stimulation waveform, the determining the stimulation parameter combination according to the target user category includes:
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.
In one embodiment, the determining the stimulus waveforms corresponding to the target brain regions according to the brain wave data of the target brain regions includes:
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.
In one embodiment, the determining, according to the local brain wave data of each target brain region in the target waveform interval, the expected brain wave data corresponding to each target brain region in the target waveform interval includes:
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.
In a second aspect, an embodiment of the present invention further provides a personalized brain stimulation device regulation and control apparatus, the apparatus including:
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.
In a third aspect, an embodiment of the present invention further provides a terminal, where the terminal includes a memory and more than one processor; the memory stores more than one program; the program comprising instructions for performing the personalized brain stimulation instrument modulation method of any one of the above; the processor is configured to execute the program.
In a fourth aspect, embodiments of the present invention also provide a computer readable storage medium having stored thereon a plurality of instructions adapted to be loaded and executed by a processor to implement the steps of the personalized brain stimulation instrument adjustment method described in any of the above.
The invention has the beneficial effects that: according to the embodiment of the invention, the category of the user is judged by acquiring the brain wave data of different brain regions of the user and analyzing the 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.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to the drawings without inventive effort to those skilled in the art.
Fig. 1 is a schematic flow chart of a personalized brain stimulation instrument regulation method according to an embodiment of the present invention.
Fig. 2 is a schematic block diagram of a personalized brain stimulation instrument control device according to an embodiment of the present invention.
Fig. 3 is a schematic block diagram of a terminal according to an embodiment of the present invention.
Detailed Description
The invention discloses a personalized brain stimulation instrument regulation and control method, a device, a terminal and a storage medium, and in order to make the purposes, the technical scheme and the effects of the invention clearer and more clear, the invention is further described in detail below by referring to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude 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 an element is referred to as 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. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein includes all or any element and all combination of one or more of the associated listed items.
It will be understood by those skilled in the art that all terms (including technical 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 unless defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
In view of the above-mentioned drawbacks of the prior art, the present invention provides a personalized brain stimulation device regulation method 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; 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. 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.
As shown in fig. 1, the method includes:
step S100, 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.
Specifically, the target user in this embodiment may be any user who has a need for using the brain stimulation apparatus. The brain states of different users are different, the brain states of the same user in different scenes/times are also different, and the accurate regulation and control of the brain stimulation instrument needs to take the current actual brain state of the user as a guide. Therefore, in this embodiment, brain wave data of different brain regions of the target user are acquired, and signal cooperativity between the different brain regions is calculated by combining all the acquired brain wave data. The signal cooperativity can reflect the communication condition between brain areas, and further reflect the current brain state of the target user. Generally, the better the brain state, the higher the signal synergy between brain regions and vice versa.
In one implementation, the calculating signal cooperativity between each brain region according to each brain wave data includes:
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.
The present example illustrates the calculation of signal cooperativity using two brain regions as an example. Specifically, brain wave data of two brain regions are acquired first, and a target frequency band of interest can be selected for specific analysis. And extracting phase information and amplitude information of a target frequency band in the two brain wave data, calculating phase amplitude coupling degree according to the extracted phase information and amplitude information, wherein the higher the phase amplitude coupling degree is, the more normal the signal communication of the two brain regions is, the higher the signal cooperativity is, and the lower the signal cooperativity is otherwise.
In one implementation, by the phase information and the amplitude information of the two brain wave data, the frequency of occurrence of the case where the amplitude of the high-frequency time series oscillates at a low frequency (for example, the frequency at which the amplitude of the high-frequency time series of a oscillates at a low frequency of B corresponding to a time point or the frequency at which the amplitude of the high-frequency time series of B oscillates at a low frequency of a corresponding time point for the two brain wave data A, B) is detected, the higher the frequency, the higher the phase amplitude coupling degree, the higher the signal synergy. In short, if there is a phase amplitude coupling of the two brain wave data, the amplitude of the high frequency time series will oscillate at a low frequency.
In another implementation, the consistency of the amplitude values respectively corresponding to the different phase values is calculated through the phase information and the amplitude information of the two brain wave data. For example, for the two brain wave data A, B, the phase is extracted from the low frequency signal of a, the amplitude is extracted from the high frequency signal of B, the correspondence between the phase and the amplitude is determined based on the time point, and the correspondence between the amplitudes corresponding to the different phase values is calculated. The lower the coherence, the higher the phase magnitude coupling and vice versa. In short, if two brain wave data have phase amplitude coupling, the amplitude which is obviously higher than other phase values exists in a specific phase value; otherwise, if there is no phase amplitude coupling, the amplitudes corresponding to the different phase values are relatively similar.
In another implementation, the calculating the phase-amplitude coupling degree according to the brain wave data of the two brain regions includes:
obtaining standard fusion oscillation characteristic data corresponding to two brain regions, wherein the method for generating the standard fusion oscillation characteristic data comprises the following steps: obtaining standard brain wave data corresponding to the two brain regions respectively, wherein the standard brain wave data are acquired by a user with a normal brain state; standard oscillation characteristic data corresponding to the two pieces of standard brain wave data are obtained, and standard fusion oscillation characteristic data are generated according to the two pieces of standard oscillation characteristic data;
acquiring oscillation characteristic data corresponding to the brain wave data of the two brain areas respectively, and generating fusion oscillation characteristic data according to the two oscillation characteristic data;
calculating oscillation similarity according to the standard fusion oscillation characteristic data and the fusion oscillation characteristic data;
and calculating the phase amplitude coupling degree according to the oscillation similarity.
Specifically, in this embodiment, brain wave data of the two brain regions under the condition of normal communication is collected in advance by a user with a normal brain state, and two standard brain wave data are obtained. And then carrying out data fusion on the oscillation characteristic data of the two standard brain wave data to obtain standard fusion oscillation characteristic data. In an actual application scene, the current fusion oscillation characteristic data of the two brain areas are calculated, the phase amplitude coupling degree of the standard fusion oscillation characteristic data is used as a full value, the oscillation similarity of the fusion oscillation characteristic data and the standard fusion oscillation characteristic data is calculated, and the phase amplitude coupling degree of the brain wave data of the current two brain areas can be converted.
The embodiment provides three calculation modes of the phase amplitude coupling degree, and one calculation mode can be selected to be used in an actual application scene, or a plurality of calculation modes can be selected to be used for averaging.
As shown in fig. 1, the method further includes:
step 200, determining a target user category according to the signal cooperativity, and determining a stimulation parameter combination according to the target user category.
Specifically, the connection conditions of the brain nerves of different user groups are different, so that the signal communication conditions between brain regions can be different, and the signal communication conditions can be quantitatively presented through signal cooperativity between the brain regions. The embodiment can determine the user group to which the target user belongs through signal cooperativity among different brain areas of the target user, and then the target user category is obtained. By means of the identified target user category, a combination of stimulation parameters suitable for the target user is accurately selected for the target user.
In one implementation, the determining the target user category according to each signal cooperativity includes:
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.
Specifically, the signal cooperativity between two brain regions can reflect whether the signal communication between the two brain regions is normal or not, so that the brain region combination with abnormal cooperativity can be screened out through the signal cooperativity between each brain region. The brain region collaboration abnormal conditions of different user groups are different, so that the user group to which the target user should belong can be analyzed based on the screened brain region combination of the collaboration abnormal conditions, and the user category corresponding to the target user is further determined, and the target user category is obtained.
In one implementation, the screening the brain region combinations with abnormal synergy from the brain regions according to the signal synergy 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.
Specifically, in this embodiment, signal cooperativity thresholds between every two brain regions may be preset, where the signal cooperativity thresholds are equivalent to threshold values with normal cooperativity states. If the currently calculated signal cooperativity is lower than the corresponding signal cooperativity threshold, the situation that the brain wave data of the two corresponding brain areas have abnormal cooperativity is indicated, namely, the signal communication between the two brain areas is abnormal. Thereby screening out synergistic abnormal brain region combinations.
In one implementation, the stimulation parameter combination includes a stimulation site and a stimulation waveform, the determining the stimulation parameter combination according to the target user category includes:
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.
In particular, the motivation for different user groups to use the brain stimulation apparatus is different, e.g. there are user groups desiring to improve memory by the brain stimulation apparatus and user groups desiring to improve concentration by the brain stimulation apparatus, so that the desired stimulation effect achieved by the different user groups after use of the brain stimulation apparatus is different. Different brain regions can generate different stimulation effects as stimulation sites, and the expected stimulation effect is determined according to the target user category, so that the brain region capable of realizing the stimulation effect is screened out as a target brain region. When the brain stimulation instrument is used, each target brain region is used as a stimulation site, and proper stimulation waveforms are set by taking brain wave data acquired from each target brain region as guidance, so that the local brain state of each target brain region is optimized, and the expected stimulation effect of a target user is further realized.
In one implementation, the determining the stimulus waveforms corresponding to the target brain regions according to the brain wave data of the target brain regions includes:
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.
Specifically, firstly, an optimal synchronization interval is determined by analyzing brain wave data of each target brain region, namely, a target waveform interval to be stimulated is obtained, and for example, an interval with the weakest phase amplitude coupling degree of each brain wave data can be found out to serve as the target waveform interval. And then intercepting out the local wave bands corresponding to the target waveform interval to obtain a plurality of local brain wave data. And determining respective corresponding adjustment targets by comprehensively analyzing the current waveforms of the local brain wave data, and obtaining expected brain wave data of the local brain wave data. For each target brain region, phase adjustment information and amplitude adjustment information of the target brain region are calculated by comparing local brain wave data and corresponding expected brain wave data of the target brain region, and then the stimulation waveform corresponding to the target brain region is constructed by taking the phase adjustment information and the amplitude adjustment information as guidance.
In one implementation manner, the determining, according to the local brain wave data of each target brain region in the target waveform interval, the expected brain wave data corresponding to each target brain region in the target waveform interval respectively includes:
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.
Specifically, in this embodiment, one reinforcement learning model is constructed in advance, input data of the reinforcement learning model is all local brain wave data, and output data is desired brain wave data corresponding to each local brain wave data one by one. The reinforcement learning model is provided with a reward mechanism for automatically updating parameters, update signal cooperativity among target brain areas is calculated according to expected brain wave data output by each round, and a reward value is calculated through all update signal cooperativity, wherein the reward value can be used for evaluating the optimization action of the reinforcement learning model on each local brain wave data of the round, and further guiding the reinforcement learning model to perform self-updating. If the corresponding rewarding value of the round does not reach the rewarding threshold value, which means that the effect of electrically stimulating each target brain area by taking each expected brain wave data of the round as a reference cannot reach the expectation, the expected brain wave data cannot be used as the final expected brain wave data of each target brain area, and the reinforcement learning model also needs to optimize the strategy for executing the action; if the corresponding reward value of the round reaches the reward threshold, the expected effect of the electric stimulation on each target brain area by taking each expected brain wave data of the round as a reference can be expected, and the expected brain wave data are taken as final expected brain wave data of each target brain area.
As shown in fig. 1, the method further includes:
and step 300, regulating and controlling the brain stimulation instrument used by the target user according to the stimulation parameter combination.
Specifically, the stimulation parameter combination is obtained through brain wave data analysis of the target user, so that the stimulation parameter combination is matched with the current brain state of the target user. The brain stimulation instrument used by the target user is regulated and controlled through the stimulation parameter combination, so that the current brain state of the target user can be effectively improved, and better experience is brought to the target user.
Based on the above embodiment, the present invention further provides a personalized brain stimulation device regulation and control apparatus, as shown in fig. 2, where the apparatus includes:
the data analysis module 01 is used for acquiring brain wave data corresponding to a plurality of brain regions of a target user respectively, and calculating signal cooperativity between every two brain regions according to the brain wave data;
a category analysis module 02, configured to determine a target user category according to each signal cooperativity, and determine a stimulation parameter combination according to the target user category;
and the intelligent regulation and control module 03 is used for regulating and controlling the brain stimulation instrument used by the target user according to the stimulation parameter combination.
In one implementation, the data analysis module 01 includes:
the coupling analysis unit is used for calculating the phase amplitude coupling degree according to the brain wave data of the two brain areas;
and the cooperativity calculation unit is used for calculating the signal cooperativity between the two brain regions according to the phase amplitude coupling degree.
In one implementation, the category analysis module 02 includes:
the combination screening unit is used for screening a plurality of brain region combinations with abnormal synergy from the brain regions according to the signal cooperativity;
and the category determining unit is used for determining the target user category according to each brain area combination with abnormal coordination.
In one implementation, the combinatorial screening unit includes:
the threshold value acquisition unit is used for acquiring signal cooperativity threshold values between every two brain regions;
and the numerical comparison unit is used for screening out a plurality of brain region combinations with abnormal synergy from the brain regions according to the signal cooperativity and the signal cooperativity threshold value.
In one implementation, the stimulation parameter combination includes a stimulation site and a stimulation waveform, and the category analysis module 02 further includes:
an effect determining unit, configured to determine an expected stimulation effect of the target user according to the target user category;
the device comprises a label acquisition unit, a display unit and a display unit, wherein the label acquisition unit is used for acquiring stimulation effect labels corresponding to each brain region respectively, and the stimulation effect label of each brain region is used for reflecting a plurality of stimulation effects associated with the brain region;
the effect matching unit is used for 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 the waveform determining unit is used for determining the corresponding stimulation waveforms of the target brain areas according to the brain wave data of the target brain areas by taking the target brain areas as the stimulation sites.
In one implementation, the waveform determining unit includes:
the interval determining unit is used for determining a target waveform interval to be stimulated according to the brain wave data of each target brain region;
the local analysis unit is used for 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;
an information determining unit configured to determine phase adjustment information and amplitude adjustment information corresponding to each of the target brain regions, respectively, based on each of the local brain wave data and each of the desired brain wave data;
and the waveform construction unit is used for 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.
In one implementation, the local analysis unit includes:
the model calling unit is used for 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;
the cooperativity updating unit is used for calculating the cooperativity of the updating signals between every two brain areas according to the expected brain wave data;
the reward value calculation unit is used for 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;
and the iteration updating unit is used for updating the reinforcement learning model according to the reward value if not, and continuously executing 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.
Based on the above embodiment, the present invention also provides a terminal, and a functional block diagram thereof may be shown in fig. 3. The terminal comprises a processor, a memory, a network interface and a display screen which are connected through a system bus. Wherein the processor of the terminal is adapted to provide computing and control capabilities. The memory of the terminal includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the terminal is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a personalized brain stimulation instrument regulation method. The display screen of the terminal may be a liquid crystal display screen or an electronic ink display screen.
It will be appreciated by those skilled in the art that the functional block diagram shown in fig. 3 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the terminal to which the present inventive arrangements may be applied, and that a particular terminal may include more or less components than those shown, or may combine some of the components, or have a different arrangement of components.
In one implementation, the memory of the terminal has stored therein one or more programs, and the execution of the one or more programs by one or more processors includes instructions for performing a personalized brain stimulation instrument regulation method.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
In summary, the invention discloses a personalized brain stimulation instrument regulation and control method, a device, a terminal and a storage medium, wherein the method 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; 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. The invention judges the category of the user by acquiring the brain wave data of different brain regions of the user and analyzing the 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.
It is to be understood that the invention is not limited in its application to the examples described above, but is capable of modification and variation in light of the above teachings by those skilled in the art, and that all such modifications and variations are intended to be included within the scope of the appended claims.
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.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130197401A1 (en) * | 2011-12-30 | 2013-08-01 | Tomo Sato | Optimization of ultrasound waveform characteristics for transcranial ultrasound neuromodulation |
| CN110522983A (en) * | 2018-05-23 | 2019-12-03 | 深圳先进技术研究院 | Brain stimulation system, method, equipment and storage medium based on artificial intelligence |
| CN116090294A (en) * | 2022-12-21 | 2023-05-09 | 中国科学院自动化研究所 | Brain response method, device, equipment and medium for personalized transcranial direct current stimulation |
| CN116999701A (en) * | 2023-09-18 | 2023-11-07 | 深圳般意科技有限公司 | Micro-electro-stimulation scheme making method and device, terminal equipment and storage medium |
| CN117045967A (en) * | 2023-09-15 | 2023-11-14 | 杭州般意科技有限公司 | Intervention stimulation mode optimization method of transcranial direct current stimulation equipment |
| CN117093787A (en) * | 2023-08-24 | 2023-11-21 | 深圳般意科技有限公司 | Information pushing method applied to brain stimulation device and related equipment |
| CN117085251A (en) * | 2023-09-21 | 2023-11-21 | 杭州般意科技有限公司 | Transcranial direct current stimulation device for determining intervention stimulation mode based on scene |
-
2023
- 2023-12-15 CN CN202311726920.7A patent/CN117409946A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130197401A1 (en) * | 2011-12-30 | 2013-08-01 | Tomo Sato | Optimization of ultrasound waveform characteristics for transcranial ultrasound neuromodulation |
| CN110522983A (en) * | 2018-05-23 | 2019-12-03 | 深圳先进技术研究院 | Brain stimulation system, method, equipment and storage medium based on artificial intelligence |
| CN116090294A (en) * | 2022-12-21 | 2023-05-09 | 中国科学院自动化研究所 | Brain response method, device, equipment and medium for personalized transcranial direct current stimulation |
| CN117093787A (en) * | 2023-08-24 | 2023-11-21 | 深圳般意科技有限公司 | Information pushing method applied to brain stimulation device and related equipment |
| CN117045967A (en) * | 2023-09-15 | 2023-11-14 | 杭州般意科技有限公司 | Intervention stimulation mode optimization method of transcranial direct current stimulation equipment |
| CN116999701A (en) * | 2023-09-18 | 2023-11-07 | 深圳般意科技有限公司 | Micro-electro-stimulation scheme making method and device, terminal equipment and storage medium |
| CN117085251A (en) * | 2023-09-21 | 2023-11-21 | 杭州般意科技有限公司 | Transcranial direct current stimulation device for determining intervention stimulation mode based on scene |
Non-Patent Citations (2)
| Title |
|---|
| 关龙舟;魏云;李小俚;: "经颅电刺激―一项具有发展前景的脑刺激技术", 中国医疗设备, vol. 30, no. 11, 30 November 2015 (2015-11-30), pages 1 - 5 * |
| 杨虎;汪毓铎;陈新华;殷胜勇;巫彤宁;: "多参数可调经颅微电流刺激仪的研制和基于脑电的效果评测", 《中国生物医学工程学报》, vol. 34, no. 06, 31 December 2015 (2015-12-31), pages 714 - 719 * |
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