An assistive method and system based on brain stimulation

Abstract

This invention relates to an auxiliary method and system based on brain stimulation. The method includes: dividing the head region into multiple sub-regions; determining a current set of sub-regions based on the multiple sub-regions, placing stimulation devices and applying stimulation; receiving neurophysiological signals from the brain and determining whether a cutoff condition is met in real-time stimulation response, adjusting the current set of sub-regions before the cutoff condition is met; using the current set of sub-regions as the final determined set of sub-regions; and placing stimulation devices in each sub-region of the set of sub-regions. This invention, based on a refined brain network setup, forms customized intelligent closed-loop control for differentiated target regions, thus improving the auxiliary capability of brain stimulation.

Description

[Technical Field]

[0001] This invention belongs to the field of smart medical technology, and in particular relates to an auxiliary method and system based on brain stimulation. [Background Technology]

[0003] Smart healthcare is a comprehensive concept that integrates medical services and data using advanced information technology and artificial intelligence to improve the quality, efficiency, and effectiveness of healthcare. It is applied across multiple stages, including diagnosis, treatment, rehabilitation, payment, and health management. Based on high-tech technologies such as the Internet of Things (IoT), cloud computing, big data, and artificial intelligence, it constructs a complete, cross-departmental, patient-centered medical information management and service system. The goal of smart healthcare is to achieve interconnected, shared, and collaborative medical information, clinical innovation, and scientific diagnosis. Key technologies in smart healthcare include IoT, cloud computing, and artificial intelligence. In smart healthcare, IoT technology is used for resource management, disease monitoring, and health management, enabling real-time monitoring and early warning of patients' vital signs through sensors and other devices.

[0004] From traditional magnetoencephalography (MEG) and electroencephalography (EEG) to novel photoelectric synchronous brain activity detection technologies, more comprehensive brain activity detection has been achieved by integrating electrical signals and blood oxygenation signals. Brain stimulation-based auxiliary methods and systems are an important research direction in neuroscience and medicine. They directly or indirectly stimulate brain neurons through physical factors (such as magnetism, electricity, light, and sound) to change the communication patterns between nerve cells and regulate abnormal neural circuits, thereby being used to treat neurological diseases. A key technology is transcranial electrical stimulation (tES), which includes several technical forms: 1) tDCS (transcranial direct current stimulation): This involves continuously applying weak direct current to the cerebral cortex to change the resting state of neuronal membrane potentials. 1) Anodic stimulation can enhance excitability, while cathodic stimulation inhibits neuronal activity; 2) tACS (transcranial alternating current stimulation): through sinusoidal waves and / or other forms of alternating current stimulation, it interacts with the brain's intrinsic oscillation frequency to regulate the synchronicity and rhythm of neural oscillations. High-frequency carrier current stimulation (>7mA) can directly stimulate deep brain nuclei; 3) tRNS (transcranial random noise stimulation): stimulation with random frequency current increases neural plasticity and synaptic activity, which helps improve learning and memory functions; 4) TIS (time-interference stimulation): through the interaction of two sets of high-frequency sinusoidal alternating current fields with small differences, due to the certain frequency difference between the two sinusoidal alternating current fields, a superimposed electric field is generated, forming a low-frequency envelope wave. The low-frequency envelope wave is sufficient to drive neural activity, and neurons can be activated at the selected focal point without driving adjacent or covered areas. This allows for stimulation of specific locations without affecting other unrelated brain regions, providing the possibility for precise brain regulation. Transcranial magnetic stimulation (TMS) has been widely applied in neurorehabilitation, mental health, and brain function enhancement. TMS uses time-varying magnetic fields to stimulate the cerebral cortex, generating induced currents that alter the action potentials of cortical nerve cells, thereby affecting brain metabolism and neural electrical activity. Repetitive magnetic stimulation (rTMS) can alter the stimulation frequency, duration, and intervals to produce excitatory or inhibitory effects on the brain. Deep brain stimulation (DBS) involves continuous stimulation via a pair of electrodes implanted in specific brain regions, used to treat conditions such as Parkinson's disease, essential tremor, dystonia, epilepsy, depression, tic disorders, and obsessive-compulsive disorder. Vagus nerve stimulation (VNS) delivers electrical impulses to the left vagus nerve via a subcutaneous device. Closed-loop brain stimulation systems use brain-computer interface (BCI) technology. By monitoring brain activity and decoding motor intentions, and using machine learning, these systems allow users to control external devices, such as robotic arms, through thought. Closed-loop BCI systems can adjust stimulation based on the user's brain signals to improve its effectiveness.Neural prostheses (NPs): When a region of the brain is damaged due to injury or disease, neural prostheses can be used to restore the function of the affected area, whether motor, sensory, or cognitive. For example, cochlear implants are among the most widely used neural prostheses; they directly stimulate the auditory nerve, bypassing damaged cochlear hair cells, providing significantly encoded information for better speech perception. Wireless neurostimulation technology is constantly being improved and developed, and will undoubtedly play an important role in future neuromodulation medicine, benefiting a wide range of patients. Brain network mapping can provide direction for modulation, helping to achieve a shift from extensive to precise modulation. Meanwhile, future device development must achieve closed-loop modulation, with real-time feedback to ensure the effectiveness of modulation, which is currently a research hotspot.

[0005] Currently used international 10-20 EEG systems have relatively fixed and simple brain network maps, which are inconsistent with the current development of sophisticated sensors. Therefore, they lack corresponding intelligent closed-loop control methods. How to achieve flexible stimulation control and form customized intelligent closed-loop control for differentiated target regions is a technical problem to be solved. To address the problems in the existing technology, this invention proposes an auxiliary method and system based on brain stimulation. Based on a refined brain network setup, it forms customized intelligent closed-loop control for differentiated target regions, thus improving the auxiliary capabilities of brain stimulation. [Summary of the Invention]

[0006] To address the aforementioned problems in the prior art, this invention proposes an auxiliary method and system based on brain stimulation, the method comprising:

[0007] Step S1: Divide the head region into N sub-regions;

[0008] Step S2: Obtain the current sub-region set A, which contains M sub-regions, based on the N sub-regions. M ={a m}; in each sub-region a m Place the stimulation device and apply stimulation so that the stimulation signal is dispersed and acts on the target area; where: m = 1 to M;

[0009] Step S3: Receive neurophysiological signals from the brain; specifically: receive neurophysiological signals from the target region; process the received neurophysiological signals to identify real-time EEG patterns and / or real-time neural activity characteristics as real-time stimulus responses;

[0010] Step S4: Determine whether the cutoff condition is met based on the real-time stimulus response. If yes, proceed to step S5; otherwise, start from the current sub-region set A. M ={a m Remove one subregion a from the first subregion in the first subregion. m1 Then we obtain a set of M subregions that differ by one. Reapply the stimulus and obtain its corresponding response to the missing stimulus. Determine the similarity distance d between the real-time stimulus response and each difference stimulus response. m，m-m1 ;The similarity distance d m，m-m1 Set as sub-region a m1 The difference in weights; determine the centroid subregion a of the region covered by the M subregions. c Remove the subregion with the smallest difference in weight from the current subregion set A. M Remove from the middle and add the centroid subregion a. c Update the current sub-region set; return to step S2;

[0011] The determination of whether the cutoff condition is met based on the real-time stimulus response is specifically as follows: when the following sub-condition 1 and / or sub-condition 2 are met, it is determined that the cutoff condition is met.

[0012] Sub-condition 1: The similarity distance between the real-time response stimulus and the target response stimulus is less than or equal to the similarity distance threshold;

[0013] Subcondition 2: Determine the similarity distance d between the real-time stimulus response and each difference stimulus response. m，m-m1 The deviation value ds is calculated based on the following formula (1); if the deviation value ds is less than or equal to the deviation value threshold, then sub-condition 2 is satisfied; where: It is the mean of the similarity distance;

[0014]

[0015] The determination of the centroid sub-regions covered by the M sub-regions specifically involves: Let the current sub-region set A... M Neutron region a m1 The two-dimensional expansion coordinates are (x m1 y m1 The corresponding difference weight is d. m，m-m1 The two-dimensional unfolded coordinates (x, y) of the centroid of the central sub-region are calculated using the following formulas (2) and (3). c y c ); Expand the centroid in two dimensions to coordinates (x c y c The sub-region into which the centroid falls is taken as the centroid sub-region;

[0016]

[0017] Step S5: Use the current set of sub-regions as the final set of sub-regions.

[0018] Furthermore, the areas of the N sub-regions may be the same or different.

[0019] Furthermore, the shapes of the N sub-regions may be the same or different.

[0020] Furthermore, M is a preset value, and M <N。

[0021] Furthermore, M = 3 to 16.

[0022] Furthermore, the coordinates of a specific location within the sub-region are used as its two-dimensional unfolded coordinates.

[0023] Furthermore, in step S1, in the initial state, N sub-regions of a specific shape are formed by random division.

[0024] A brain stimulation-based assistive device is used to implement the aforementioned brain stimulation-based assistive method.

[0025] A brain stimulation-based assistive system is provided for implementing the aforementioned brain stimulation-based assistive method.

[0026] A brain stimulation-based assistive device is used to implement the aforementioned brain stimulation-based assistive method.

[0027] The beneficial effects of this invention include:

[0028] (1) Based on the flexible division of the head region, the current sub-region set can be quickly optimized through the difference operation, realize intelligent customization settings for the target region, and improve stimulation efficiency by combining with the stimulation scheme and the area of ​​action.

[0029] (2) A quantitative correspondence is formed between sub-region settings and response stimuli, thereby supporting customized intelligent closed-loop control for differentiated target regions, which can provide intelligent assistance on the basis of the current manual setting method; optimization convergence can be performed quickly through two-dimensional projection calculation, with low requirements for the computing resources of the setting terminal; furthermore, the centroid sub-region is determined according to the three-dimensional spatial projection and / or the spatial position of the target region, and random factors are introduced on the basis of accurate calculation, which reduces the possibility of falling into local optima rather than global optima in the case of multiple peaks in the brain. [Attached Image Description]

[0030] The accompanying drawings, which are provided to further illustrate the invention and form part of this application, are not intended to unduly limit the invention. In the drawings:

[0031] Figure 1 This is a schematic diagram of the placement method for a traditional 10-20 system stimulation device.

[0032] Figure 2This is a schematic diagram of the refined sub-region division method provided by the present invention.

[0033] Figure 3 This is a schematic diagram of the brain stimulation-based auxiliary method provided by the present invention.

Detailed Implementation Methods

[0034] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0035] The currently commonly used international 10-20 EEG system is used to determine the electrode locations for specific brain regions on the scalp; the distance from the midpoint of the frontal pole to the root of the nose and the distance from the occipital point to the external occipital protuberance each account for 10% of the total length of this line, and all other points are spaced 20% of the total length of this line, as shown in the attached figure. Figure 1 As shown in the attached diagram. Eight electrodes are placed on each side of the brain, plus electrodes at the frontal midpoint (Fz), central point (Cz), vertex (Pz) in the anterior-posterior position, and two ear electrodes, for a total of 21 electrodes. However, this method uses a fixed division of brain regions, while the actual area of ​​action varies greatly during operation. Adapting the two methods will reduce stimulation efficiency. (See attached diagram.) Figure 2 As shown, by flexibly dividing the head region, it can be combined with the stimulation plan and the target area to improve stimulation efficiency.

[0036] This invention proposes an auxiliary method and system based on brain stimulation, as shown in the appendix. Figure 1 As shown, the method includes the following steps:

[0037] Step S1: Divide the head region into N sub-regions; specifically: form N sub-regions by random division; the N sub-regions form a dense grid;

[0038] Preferably, the areas of the N sub-regions are the same or different, and the shapes of the N sub-regions are the same or different;

[0039] Alternative: Divide the scalp area into N sub-regions in a neat division manner, where each sub-region has the same or approximately the same shape or size;

[0040] Preferably, the sub-region is one of a square, rectangle, and triangle, or approximately one of a square, rectangle, and triangle; using these three shapes facilitates surface simulation and shape fitting; (Attached) Figure 2The diagram shows a triangular structure; this structure facilitates surface simulation and allows for the creation of other common shapes, such as circles, squares, and strips, through various combinations. Furthermore, triangular electrodes can be used to stimulate specific neural tissues, aiding in more precise positioning and focusing of current while avoiding stimulation of normal sensory nerves. By selecting appropriate rise times and stimulation intensities, triangular electrodes can stimulate diseased muscles while avoiding stimulation of normal muscles and sensory nerves.

[0041] Preferably, the triangle can be of any shape, including obtuse and acute triangles;

[0042] Step S2: Obtain the current sub-region set A, which contains M sub-regions, based on the N sub-regions. M ={a m}; in each sub-region a m Place the stimulation device and apply stimulation so that the stimulation signal is dispersed and acts on the target area; where: m = 1 to M;

[0043] Preferred: M is a preset value; for example: 3 to 16;

[0044] Preferably, the target region is a two-dimensional or three-dimensional region; the target region is located in the brain;

[0045] Preferably, in the initial state, M sub-regions are randomly selected from the N sub-regions as the current sub-region set A. M ={a m The sum of the areas of the M sub-regions satisfies the area constraint condition; in addition, interference judgment can be made on the distribution of the M sub-regions to ensure that there is no interference between the electrodes of the sub-regions.

[0046] Alternatively: In the initial state, select M regions from the N sub-regions as the current sub-region set A. M ={a m};

[0047] Alternatively: In the initial state, the step of selecting M sub-regions from the N sub-regions as the current sub-region set A. M ={a m Specifically, this involves selecting M sub-regions based on the stimulus target attributes, target region shape, and / or patient attributes of the target region; a stimulus trial can be conducted beforehand to obtain the correspondence between the stimulus target attributes, target region shape, and / or patient attributes and the M sub-region settings; or the M sub-regions can be selected based on human experience.

[0048] Preferably, the stimulation device is an electrode, and the stimulation signal is a continuous and low-intensity current, so that the current is dispersed and acts on the target area; the target area is the area where the stimulation target is located; the target area is a region different from the N sub-regions; the sub-regions are located on the scalp, while the target area is located in the brain;

[0049] Alternative: A time-varying magnetic field device is set in each of the N sub-regions to stimulate the cerebral cortex to generate an induced current, thereby affecting the neural electrical activity of the target area in the brain by changing the action potential of cortical nerve cells.

[0050] Step S3: Receive neurophysiological signals from the brain; specifically: receive neurophysiological signals from the target region; process the received neurophysiological signals to identify real-time EEG patterns or real-time neural activity characteristics as real-time stimulus responses; wherein: the real-time EEG patterns or real-time neural activity characteristics are used to indicate specific brain functional states or brain (or target region) response signals to brain stimulation;

[0051] Preferred method: Real-time stimulation and acquisition of real-time stimulus response are achieved by directly placing electrodes or time-varying magnetic field devices at M sub-region locations, or by acquiring real-time stimulus response through in vitro simulation; in the case of in vitro simulation, a simulation device or simulation model needs to be constructed for the stimulation target; when setting up electrodes or time-varying magnetic field devices, ensure that the electrode surface is clean, and clean the electrode surface with pure water or an appropriate solution to remove any residue; avoid touching the electrode surface, and use dust-free gloves to prevent contamination that may interfere with the electrode performance; avoid mechanical damage to the electrodes, handle the electrodes carefully, and avoid scratching or bending the electrodes;

[0052] Preferably, the brain's electrical activity or metabolic changes are captured in real time using electroencephalography (EEG), functional near-infrared spectroscopy, or other neuroimaging techniques; the real-time neural activity features include delta waves, theta waves, alpha waves, beta waves, gamma waves, EEG microstates, event-related potentials (ERPs), event-related synchronization (ERS) and event-related desynchronization (ERD), spectral entropy, and / or neural oscillations; the EEG patterns are real-time neural activity features obtained through combinations of specific patterns.

[0053] Step S4: Determine whether the cutoff condition is met based on the real-time stimulus response. If yes, proceed to step S5; otherwise, start from the current sub-region set A. M ={a m Remove one subregion a from the first subregion in the first subregion. m1 Then we obtain a set of M subregions that differ by one. Reapply the stimulus and obtain its corresponding response to the missing stimulus. Determine the similarity distance d between the real-time stimulus response and each difference stimulus response.m，m-m1 ;The similarity distance d m，m-m1 Set as sub-region a m1 The difference in weights; determine the centroid subregion a of the region covered by the M subregions. c Remove the subregion with the smallest difference in weight from the current subregion set A. M Remove from the middle and add the centroid subregion a. c Update the current sub-region set; return to step S2;

[0054] The determination of whether the cutoff condition is met based on the real-time stimulus response is as follows: if the similarity distance between the real-time response stimulus and the target response stimulus is less than or equal to the similarity distance threshold, then the cutoff condition is determined to be met; the target stimulus response corresponds to the target EEG pattern or target neural activity feature to be achieved; the similarity distance threshold is a preset value.

[0055] Alternatively: The determination of whether the cutoff condition is met based on the real-time stimulus response is specifically: the cutoff condition is determined to be met when the following sub-conditions 1 and 2 are met;

[0056] Sub-condition 1: The similarity distance between the real-time response stimulus and the target response stimulus is less than or equal to the similarity distance threshold;

[0057] Subcondition 2: Determine the similarity distance d between the real-time stimulus response and each difference stimulus response. m，m-m1 ; Calculate the mean deviation of similar distances. When the mean deviation is small, sub-condition 2 is satisfied; Specifically: Calculate the deviation value ds based on the following formula (1); If the deviation value ds is less than or equal to the deviation value threshold, then sub-condition 2 is satisfied; Where: It is the mean of the similarity distance;

[0058]

[0059] Preferred setting: The deviation threshold is a preset value;

[0060] Alternative: The deviation threshold is equal to the similarity distance between the real-time response stimulus and the target response stimulus;

[0061] The similarity distance d between the real-time stimulus response and each difference stimulus response is determined. m，m-m1Specifically, the similarity distance is defined as the degree of similarity between the real-time stimulus response and each stimulus response with a difference of one. For example, when the stimulus response is a real-time neural activity feature, the similarity between the two is determined by the similarity of the real-time neural activity feature values; when the real-time neural activity feature is a discrete value, it is determined by Euclidean distance; when the real-time neural activity feature is a continuous value, it is determined by Pearson correlation coefficient; when the stimulus response is an EEG pattern, pattern matching is used to determine the degree of similarity between the two, and this is used as the similarity distance. The smaller the similarity distance, the higher the degree of similarity, and vice versa. Of course, the degree of similarity and its corresponding similarity distance can also be determined by creating a correspondence table.

[0062] The determination of the centroid sub-regions covered by the M sub-regions specifically involves: Let the current sub-region set A... M Neutron region a m1 The two-dimensional expansion coordinates are (x m1 y m1 The corresponding difference weight is d. m，m-m1 The two-dimensional unfolded coordinates (x, y) of the centroid of the central sub-region are calculated using the following formulas (2) and (3). c y c ); Expand the centroid in two dimensions to coordinates (x c y c The sub-region into which the centroid falls is taken as the centroid sub-region;

[0063]

[0064] Preferred method: Use the coordinates of a specific location within the sub-region as its two-dimensional unfolded coordinates; for example: the center location of the sub-region;

[0065] Preferred: When the two-dimensional unfolded coordinates of the centroid (x) c y c If it does not fall into any sub-region, the sub-region closest to the two-dimensional unfolded coordinates of the centroid is taken as the centroid sub-region;

[0066] Alternative: Expand the centroid in two dimensions to its coordinates (x) c y c The sub-region into which the centroid falls is taken as the centroid sub-region; specifically, from the sub-region into which the two-dimensional unfolded coordinates of the centroid fall and its adjacent sub-regions, a sub-region is randomly selected as the centroid sub-region.

[0067] Alternatively: Determining the centroid sub-regions covered by the M sub-regions specifically involves: determining the centroid sub-regions based on three-dimensional spatial projection and / or the spatial position of the target region; specifically including the following steps:

[0068] Step S4A1: Let the brain be a sphere, and the equation of the horizontal cross section at the center of the sphere be ax + by + cz = d, where a, b, c, and d are constants; the center of the sphere is O = (0, 0, 0); the radius of the sphere is r.

[0069] Step S4A2: Project the M sub-regions onto the central horizontal section in a two-dimensional plane to obtain the projected coordinates of the M sub-regions; calculate the centroid projection coordinates (x, y) of the M projected coordinates using the above formulas (1) and (2). c y c ,0);

[0070] Step S4A3: The coordinates of the projected center of gravity on the sphere are called the barycentric spherical coordinates (x, y, y). q y q , z q Based on the distance between the projected coordinates of the center of gravity, the projected coordinates of the center of gravity, and the coordinates of the center of gravity sphere, and the equation of the horizontal section of the center of the sphere, the coordinates of the center of gravity sphere are calculated; specifically, the coordinates of the center of gravity sphere are calculated using the following formulas (4)(5)(6);

[0071] ax c +bx c +cx c =d(4);

[0072]

[0073] (x c y c ,0)=(x q -ta, y q -tb, z q -tc)(6);

[0074] By using the formula above and performing a simple transformation, we can obtain...

[0075] Step S4A4: Take the sub-region where the coordinates of the centroid sphere fall as the centroid sub-region;

[0076] Alternatively, step S4A4 specifically involves: determining the centroid sub-region based on the positional relationship between the centroid sphere coordinates and the target region; specifically including the following steps:

[0077] Step S4A41: Let the vector between the coordinates of the centroid sphere and the coordinates of the center of the target region be the center vector.

[0078] Step S4A42: Determine the center vector A cylinder H with its central axis;

[0079] Step S4A43: Determine the intersection line between the cylinder and the sphere; take the sub-region within the intersection line as the centroid sub-region; when there are multiple sub-regions within the intersection line, randomly select one as the centroid sub-region.

[0080] Preferably, the step further includes judging the interference of the distribution of the M sub-regions. If the distance between the M sub-regions will cause inter-electrode interference, then return to step S2 and redetermine the current set of sub-regions; for example, by re-randomizing, or in step S4A43.

[0081] Step S5: Use the current set of sub-regions as the final set of sub-regions; use each sub-region in the set of sub-regions as auxiliary information for setting up the stimulation device;

[0082] Preferred method: Merge adjacent sub-regions to fit the stimulation device; select a stimulation device of suitable shape to fit the merged sub-regions;

[0083] Preferred method: Analyze the frequency, amplitude, phase and other characteristics of the stimulus response, and adjust the stimulation intensity of the stimulation device distributed in each sub-region accordingly; for example: adjust the current intensity from micro-current (≤2mA) stimulation to high current (≤100mA, which is the safe limit current) range.

[0084] Based on the same inventive concept, the present invention also provides a brain stimulation-based assistive system, which is used to implement the above-mentioned brain stimulation-based assistive method;

[0085] Based on the same inventive concept, the present invention also provides a brain stimulation-based auxiliary server, the system being used to implement the aforementioned brain stimulation-based auxiliary method;

[0086] Based on the same inventive concept, the present invention also provides an auxiliary device based on brain stimulation, the device being used to implement the above-mentioned auxiliary method based on brain stimulation.

[0087] A computer program (also referred to as a program, software, software application, script, or code) can be written in any form of programming language, including assembly or interpreted languages, declarative or procedural languages, and can be deployed in any form, including as a standalone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but does not necessarily, correspond to a file in a file system. A program can be stored as part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to said program, or in multiple co-located files (e.g., a file storing one or more modules, subroutines, or code portions). A computer program can be deployed to execute on a single computer or on multiple computers located at a single site or distributed across multiple sites and interconnected by a communications network.

[0088] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0090] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0091] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An auxiliary method based on brain stimulation, characterized in that, The method includes: Step S1: Divide the head region into N sub-regions; Step S2: Obtain the current sub-region set A, which contains M sub-regions, based on the N sub-regions. M ={a m }; in each sub-region a m Place the stimulation device and apply stimulation so that the stimulation signal is dispersed and acts on the target area; where: m = 1 to M; Step S3: Receive neurophysiological signals from the brain; specifically: receive neurophysiological signals from the target region; process the received neurophysiological signals to identify real-time EEG patterns and / or real-time neural activity characteristics as real-time stimulus responses; Step S4: Determine whether the cutoff condition is met based on the real-time stimulus response. If yes, proceed to step S5; otherwise, start from the current sub-region set A. M ={a m Remove one subregion a from the first subregion in the first subregion. m1 Then we obtain a set of M subregions that differ by one. Reapply the stimulus and obtain its corresponding response to the missing stimulus. Determine the similarity distance d between the real-time stimulus response and each difference stimulus response. m，m-m1 ;The similarity distance d m，m-m1 Set as sub-region a m1 The difference in weights; determine the centroid subregion a of the region covered by the M subregions. c Remove the subregion with the smallest difference in weight from the current subregion set A. M Remove from the middle and add the centroid subregion a. c Update the current sub-region set; return to step S2; The determination of whether the cutoff condition is met based on the real-time stimulus response is specifically as follows: when the following sub-condition 1 and / or sub-condition 2 are met, it is determined that the cutoff condition is met. Sub-condition 1: The similarity distance between the real-time response stimulus and the target response stimulus is less than or equal to the similarity distance threshold; Subcondition 2: Determine the similarity distance d between the real-time stimulus response and each difference stimulus response. m，m-m1 The deviation value ds is calculated based on the following formula (1); if the deviation value ds is less than or equal to the deviation value threshold, then sub-condition 2 is satisfied; where: It is the mean of the similarity distance; The determination of the centroid sub-regions covered by the M sub-regions specifically involves: Let the current sub-region set A... M Neutron region a m1 The two-dimensional expansion coordinates are (x m1 y m1 The corresponding difference weight is d. m，m-m1 The two-dimensional unfolded coordinates (x, y) of the centroid of the central sub-region are calculated using the following formulas (2) and (3). c y c ); Expand the centroid in two dimensions to coordinates (x c y c The sub-region into which the centroid falls is taken as the centroid sub-region; Step S5: Use the current set of sub-regions as the final set of sub-regions.

2. The brain stimulation-based auxiliary method according to claim 1, characterized in that, The areas of the N sub-regions are either the same or different.

3. The brain stimulation-based auxiliary method according to claim 2, characterized in that, The N sub-regions may have the same or different shapes.

4. The brain stimulation-based auxiliary method according to claim 3, characterized in that, M is a preset value, and M <N。 5. The brain stimulation-based auxiliary method according to claim 4, characterized in that, M＝3～16。 6. The brain stimulation-based auxiliary method according to claim 5, characterized in that, Use the coordinates of a specific location within a subregion as its two-dimensional expanded coordinates.

7. A brain stimulation-based assistive system, characterized in that, In step S1, in the initial state, N sub-regions of a specific shape are formed by random division.

8. An assistive device based on brain stimulation, characterized in that, The brain stimulation-based assistive device is used to implement the brain stimulation-based assistive method according to any one of claims 1-7.

9. A brain stimulation-based assistive system, characterized in that, The brain stimulation-based assistive system is used to implement the brain stimulation-based assistive method according to any one of claims 1-7.

10. An assistive device based on brain stimulation, characterized in that, The brain stimulation-based assistive device is used to implement the brain stimulation-based assistive method according to any one of claims 1-7.

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