Particle effect presentation method and system for cognitive training task and cognitive training system

Abstract

The invention discloses a particle effect presentation method and system for a cognitive training task and a cognitive training system. The particle effect presentation method comprises the following steps: obtaining a brain neuron point cloud model based on a 3D coordinate system; performing brain region division on the brain neuron point cloud model based on the standardized brain region division template; obtaining a corresponding brain neural network conduction path based on a preset cognitive training task; based on the brain neural network conduction path, generating a visual conduction path according to a preset algorithm; based on a visual conduction path, a neuron-transmitted particle effect is presented on the brain neuron point cloud model. According to the particle effect presentation method, on the basis of quoting an open source brain model, standardized template partitions are mapped to a brain neuron model, standardized nerve conduction partitions and paths are provided for all cognitive training tasks on the basis of a brain point cloud model for the first time, and particle description and dynamic effect display are carried out.

Description

Technical Field

[0001] The present invention relates to a particle special effect presentation method for a cognitive training task, and also relates to a corresponding particle special effect presentation system and a cognitive training system using the particle special effect presentation method, belonging to the technical field of cognitive training. Background Art

[0002] Cognitive ability refers to the brain's ability to process, store and retrieve information. It is the basis for humans to understand the objective world and acquire various knowledge. Cognitive training is an activity aimed at improving individual cognitive abilities. Through training, people's reasoning, problem-solving and learning abilities can be enhanced. This training usually targets basic cognitive skills such as attention, working memory and executive function. The audience of cognitive training is very wide, including all age groups from young children to the elderly, and each age group has specific training methods and focuses. In addition to ordinary people, cognitive training is also suitable for children with attention deficit hyperactivity disorder (ADHD), Alzheimer's patients and patients with brain trauma.

[0003] There are many methods of cognitive training, including but not limited to color vision training, overall perception and partial perception training, shape and perception training, size perception training, orientation perception training, memory training, life memory training, thinking training, and cognitive understanding training. These training methods are based on the theory of neuroplasticity and enhance cognitive functions such as attention, memory, thinking, and problem-solving ability by stimulating and training specific functions of the brain.

[0004] Most existing cognitive training methods provide cognitive training to patients based on cognitive training tasks. However, patients do not have an intuitive understanding of the connection between such cognitive training tasks and brain function, and cannot truly understand what role cognitive training can play in overall cognition. Therefore, it is necessary to provide a method that can help patients intuitively understand the relationship between cognitive training and cognitive improvement. Summary of the invention

[0005] The primary technical problem to be solved by the present invention is to provide a particle special effects presentation method for cognitive training tasks.

[0006] Another technical problem to be solved by the present invention is to provide a particle special effects presentation system for cognitive training tasks.

[0007] Another technical problem to be solved by the present invention is to provide a cognitive training system using the particle special effects presentation method.

[0008] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0009] According to a first aspect of an embodiment of the present invention, a particle special effect presentation method for a cognitive training task is provided, comprising the following steps:

[0010] Based on the 3D coordinate system, obtain the brain neuron point cloud model;

[0011] Based on a standardized brain partition template, the brain neuron point cloud model is divided into brain regions; wherein each brain region corresponds to a different functional network;

[0012] Based on the preset cognitive training tasks, the brain regions involved in the training process and the functional connections of each brain region are obtained, so as to obtain the brain neural network conduction pathway corresponding to the cognitive training task;

[0013] Based on the brain neural network conduction path, the involved brain regions are mapped to the brain neuron point cloud model to obtain the three-dimensional coordinates of the relevant brain regions, and a visualized conduction path is generated based on a preset algorithm;

[0014] Based on the visualized conduction path, the transmission delay time, disappearance time and regional neuron lighting effect are set, so as to present the particle effects of neuron transmission on the brain neuron point cloud model.

[0015] Preferably, the brain region division of the point cloud model based on a standardized brain partition template includes:

[0016] Determine the sulci and gyri of the brain neuron point cloud model and the standardized brain partition template, and align the sulci and gyri of the two;

[0017] The partitioning results of the standardized brain partitioning template are mapped one by one to the brain neuron point cloud model, so as to divide the brain neuron point cloud model into multiple brain regions to correspond to different functional networks respectively.

[0018] Preferably, the brain neural network conduction pathway is obtained by:

[0019] Based on the preset cognitive training tasks, the brain area related to perception at the beginning of the training is used as the starting brain area;

[0020] Based on the preset cognitive training tasks, the brain areas corresponding to the advanced cognitive functions involved in the training process are used as process brain areas;

[0021] Based on the preset cognitive training tasks, the brain area corresponding to the advanced cognitive functions involved at the end of the training is used as the termination brain area;

[0022] A brain neural network conduction path is formed from the starting brain area through the process brain area to the ending brain area for the preset cognitive training task.

[0023] Preferably, the visualized conduction path is obtained by:

[0024] For each relevant brain region, a preset proportion of neurons are randomly selected, and neurons that are connected to brain regions outside the conduction pathway according to a preset algorithm are filtered out;

[0025] The remaining neurons in each relevant brain region after filtering are connected to a random number of connections, where the maximum number of connections between each neuron and the nearest neurons around it is 5;

[0026] Based on the principle of shortest straight-line distance, a three-dimensional Bezier curve path from the starting brain region to the ending brain region is generated according to the preset algorithm;

[0027] According to the three-dimensional Bezier curve path, a set of three-dimensional space coordinate points of the curve is obtained;

[0028] Randomly selecting a preset number of points from the three-dimensional space coordinate point set and connecting them to form a bifurcation for neuronal transmission from the surface of the brain;

[0029] Obtaining the coordinates of the neurons in the brain neuron point cloud model that are closest to the straight line of the three-dimensional Bezier curve path, and adding them to the process of generating the three-dimensional Bezier curve path for performing neuron transmission from the inside of the brain;

[0030] The control points, length, number of nodes, diameter and random time difference of the triggering sequence of the three-dimensional Bezier curve path are set to generate a visualized conduction path of neuron conduction.

[0031] Preferably, for the visualized conduction path of the cognitive training task, labels of brain regions involved in the cognitive training task are added;

[0032] During the cognitive training task training process, as the visualized conduction pathway is conducted, the cognitive function strength corresponding to each brain region label is dynamically displayed.

[0033] Preferably, the particle special effects presentation method further comprises:

[0034] Generate different particle effects according to different cognitive training tasks;

[0035] According to the brain neuron point cloud model and the particle special effects based on a variety of cognitive training tasks, a neural network conduction model for particle special effects presentation is jointly formed.

[0036] Preferably, for a new cognitive training task, the new cognitive training task is matched with a plurality of cognitive training tasks for which particle special effects have been generated to obtain a matching result; wherein the matching result at least includes: the task type, the paradigm corresponding to the task, and the specific brain area involved in the task;

[0037] If the matching result is the same or similar, the particle special effects corresponding to the cognitive training task that is the same or similar to the new cognitive training task are directly used as the particle special effects of the new cognitive training task; if the matching result is not the same or similar, the particle special effects for the new cognitive training task are regenerated.

[0038] According to a second aspect of an embodiment of the present invention, a particle special effects rendering system for cognitive training tasks is provided, comprising a processor and a memory, wherein the processor reads a computer program in the memory to execute the above-mentioned particle special effects rendering method.

[0039] According to a third aspect of an embodiment of the present invention, there is provided a cognitive training system, comprising:

[0040] Login unit, used for user registration and system login;

[0041] a cognitive assessment unit, connected to the login unit, for performing cognitive assessment on the user;

[0042] A task configuration unit, connected to the cognitive assessment unit and pre-set with a variety of cognitive training tasks, so as to push a personalized cognitive training program to the user based on the cognitive assessment result of the user;

[0043] A cognitive training unit, connected to the task configuration unit, to perform interactive training with the user based on the personalized cognitive training program;

[0044] A particle special effect display unit is connected to the cognitive training unit to present the particle special effects transmitted by neurons to the user based on the above-mentioned particle special effect presentation method after each cognitive training task is completed;

[0045] A training feedback unit is connected to the cognitive training unit to feed back the final training result to the user based on the user's interactive training result.

[0046] Preferably, the particle special effects display unit also includes a brain region label display module, and the brain region label display module is connected to the cognitive training unit to dynamically display the cognitive function intensity corresponding to each brain region label as the visualized conduction pathway is conducted during the cognitive training task training process.

[0047] Compared with the prior art, the present invention has the following technical effects:

[0048] (1) Based on the reference of the open source brain model, the standardized template partitions used in scientific research are mapped to the brain neuron model, and the algorithm and effect are optimized. For the first time, based on the brain point cloud model, this invention provides standardized neural conduction partitions and paths for various cognitive training tasks, and performs particle characterization and dynamic effect display.

[0049] (2) For the visualized conduction pathway of cognitive training tasks, labels of brain regions involved in cognitive training tasks are added; and during cognitive training, as the visualized conduction pathway is conducted, the cognitive function strength corresponding to each brain region label is dynamically displayed, thereby helping users to more intuitively understand which cognitive functions are currently playing a role in cognitive training, allowing users to have a deeper understanding of their own situation and improve their compliance with cognitive training. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flowchart of a particle special effects presentation method for cognitive training tasks provided by the first embodiment of the present invention;

[0051] Figure 2 A schematic diagram of a brain neuron point cloud model in the first embodiment of the present invention;

[0052] Figure 3 This is a schematic diagram of dividing the brain neuron point cloud model into multiple brain regions in the first embodiment of the present invention;

[0053] Figure 4 This is a schematic diagram of obtaining the brain neural network conduction path in the first embodiment of the present invention;

[0054] Figure 5 A schematic diagram of generating a visualized conduction path in the first embodiment of the present invention;

[0055] Figure 6 It is a schematic diagram of particle effects transmitted by neurons in the first embodiment of the present invention;

[0056] Figure 7 This is a schematic diagram of the cognitive function strength corresponding to each brain region label at the beginning of training in the first embodiment of the present invention;

[0057] Figure 8 This is a schematic diagram of the cognitive function strength corresponding to each brain region label during the training process in the first embodiment of the present invention;

[0058] Fig. 9 A structural diagram of a particle special effect presentation system for cognitive training tasks provided by the second embodiment of the present invention;

[0059] Fig.10This is a structural diagram of a cognitive training system provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0060] The technical content of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0061] An embodiment of the present invention provides a particle special effects presentation method embedded in a cognitive training system, which is specifically used to demonstrate the standardized training effect of a single cognitive training task. The method is intended to help patients understand the theoretical and scientific change path of the brain after cognitive training, thereby enhancing their understanding and recognition of the training. After completing each cognitive training task every day, the cognitive training system will display the neuron transmission particle special effects related to the task. These special effects can programmatically generate dynamic neuron conduction effects by establishing the relationship between tasks, brain activation maps, and neural network conduction pathways.

[0062] First embodiment

[0063] like Figure 1 As shown, a particle special effect presentation method for cognitive training tasks provided by the first embodiment of the present invention specifically includes steps S1 to S5:

[0064] S1: Based on the 3D coordinate system, obtain the brain neuron point cloud model.

[0065] In this embodiment, based on the 3D modeling space, a biological brain model containing 61863 neurons is imported. The brain model includes four regions: cerebellum, brain stem, left hemisphere of the brain, and right hemisphere of the brain. A basic brain neuron point cloud model based on the coordinate system is obtained (such as Figure 2 shown).

[0066] S2: Based on the standardized brain partition template, the brain neuron point cloud model is divided into brain regions.

[0067] In order to facilitate the subsequent precise positioning of brain regions according to tasks, it is necessary to first perform a basic division of brain regions. In this embodiment, the specific method is to first determine the sulci and gyri (hereinafter referred to as sulci and gyri) of the brain neuron point cloud model and the standardized brain partition template according to anatomical characteristics, and align the sulci and gyri of the two as feature points (landmarks). Then, the standardized brain partition template used for scientific research is mapped one by one to the modeling space of the brain neuron point cloud model according to the sulci and gyri marks, thereby dividing the brain neuron point cloud model into multiple brain regions (such as Figure 3 Each brain region corresponds to a different functional network. In the brain neuron point cloud model, the brain is divided into basic brain regions such as the visual area, dorsal attention network, ventral attention network, and language area.

[0068] In this embodiment, in order to match the scientific research results with the existing brain template, brain partition templates such as AAL, AAL2, and FS2009s are first downloaded; then, the extracted brain regions are registered to the brain neuron point cloud model using a registration method, and converted to the cortical surface for a match through the mri_vol2surf tool (a tool in the Free Surfer software package for mapping volume data to the brain surface); finally, the specific brain region is extracted from the template, thereby obtaining the specific brain region of the brain neuron point cloud model.

[0069] S3: Based on the preset cognitive training tasks, obtain the corresponding brain neural network conduction pathway.

[0070] In this embodiment, the activation coordinates related to the task or paradigm are searched through the meta-analysis website Neurosynth, and the activation of brain areas involved in the specific paradigm is summarized and aggregated.

[0071] Among them, the starting brain area is mainly summarized by referring to the pathways related to perception, while the process and end brain areas are summarized according to the high-level cognitive functions involved. Further, based on the selected relevant brain areas and combined with the brain network concept map that has been explored and clarified, the brain functional network and brain connections of the corresponding functions are summarized, thus forming a scientific and rigorous brain neural network conduction pathway.

[0072] Specifically, Figure 4 As shown, steps S31 to S34 are included:

[0073] S31: Based on the preset cognitive training task, the brain area related to perception at the beginning of the training is used as the starting brain area;

[0074] S32: Based on the preset cognitive training tasks, the brain areas corresponding to the advanced cognitive functions involved in the training process are used as process brain areas;

[0075] S33: Based on the preset cognitive training task, the brain area corresponding to the high-level cognitive function involved at the end of the training is used as the termination brain area;

[0076] S34: From the starting brain area through the process brain area to the ending brain area, a brain neural network conduction path for the preset cognitive training task is formed.

[0077] Taking language tasks as an example, first, visual information will reach the visual perception-related area. As the starting point for the brain to receive information, the language task requires the recognition of Chinese characters. The visual information related to cognitive recognition will be transmitted through the ventral side of the brain to the higher cognitive area (forward), reaching the language brain area related to language recognition. Language recognition information will be transmitted to the brain area related to language construction, and the grammatical order and logical relationship of the sentence will be processed to understand the sentence. This information will be transmitted to the higher-level cognitive control-related brain area, which will process the actions required for the next step of the task through the received language information. This information will finally be transmitted to the motor perception brain area related to the action, so as to generate action based on the information processed by the higher-level cognitive brain area. The brain areas activated at the starting point, process and end point of the path are ultimately determined by the guidance of the task paradigm, the results of literature research and the needs of brain function to determine the conduction route of the brain neural network.

[0078] It is understandable that for different cognitive training tasks, since different brain areas are involved in the training, the brain neural network conduction pathways formed are also different.

[0079] S4: Generate a visualized conduction pathway based on a preset algorithm.

[0080] like Figure 5 As shown, in this embodiment, after the brain neural network conduction path for a specific cognitive training task is formed based on step S3, the brain areas involved are mapped to the brain neuron point cloud model to obtain the three-dimensional coordinates of the relevant brain areas, and a visualized conduction path is generated based on a preset algorithm.

[0081] The process of generating a visualized conduction path by a preset algorithm specifically includes steps S41 to S47:

[0082] S41: For each relevant brain region, randomly select a preset proportion of neurons, and filter out neurons that will be connected to brain regions outside the conduction pathway according to a preset algorithm;

[0083] S42: connect the remaining neurons in each relevant brain region after filtering to a random number, wherein the maximum number of connections between each neuron and the nearest neurons around it is 5;

[0084] S43: Based on the principle of shortest straight-line distance, a three-dimensional Bezier curve path is generated from the starting brain area to the ending brain area according to a preset algorithm;

[0085] S44: According to the three-dimensional Bezier curve path, a set of three-dimensional space coordinate points of the curve is obtained;

[0086] S45: randomly selecting a preset number of points from the set of three-dimensional space coordinate points and connecting them to form a bifurcation, thereby being used for neuronal transmission from the surface layer of the brain;

[0087] S46: Obtain the coordinates of the neurons in the brain neuron point cloud model that are closest to the three-dimensional Bezier curve path straight line, and add them to the process of generating the three-dimensional Bezier curve path for neuron transmission from the inside of the brain;

[0088] S47: Setting the control points, length, number of nodes, diameter and random time difference of the triggering sequence of the three-dimensional Bezier curve path to generate a visualized conduction path of neuron conduction.

[0089] S5: Particle effects of neuron transmission are presented on the brain neuron point cloud model.

[0090] Specifically, Figure 6 As shown, based on the visualized conduction path, the transmission delay time, disappearance time and regional neuron lighting effect are set, thereby presenting the particle effects of neuron transmission on the brain neuron point cloud model.

[0091] In the above embodiment, preferably, the particle special effect presentation method further includes:

[0092] S6: Dynamically display the cognitive function intensity corresponding to each brain region label.

[0093] Specifically, first, for the visualized conduction pathway of the cognitive training task, add the brain region labels involved in the cognitive training task; then, during the cognitive training task training process, as the visualized conduction pathway is conducted, dynamically display the cognitive function strength corresponding to each brain region label (such as Figure 7 and Figure 8 shown).

[0094] It is understandable that during the user's cognitive training, dynamically displaying the cognitive function strength corresponding to each brain area label can help users more intuitively understand which cognitive functions are currently playing a role in cognitive training, allowing users to have a deeper understanding of their own situation and improve their compliance with cognitive training.

[0095] In addition, in the above embodiment, since the particle effects of a specific cognitive training task can be formed through steps S1 to S5, repeating the above steps S1 to S5 can generate different particle effects according to different cognitive training tasks. Thus, according to the brain neuron point cloud model and the particle effects based on a variety of cognitive training tasks, a neural network conduction model for the presentation of particle effects can be formed together. It can be understood that the use of this neural network conduction model can realize the display of particle effects for a variety of cognitive training tasks.

[0096] In addition, for new cognitive training tasks, the new cognitive training tasks can be input into the neural network conduction model to match the new cognitive training tasks with a variety of cognitive training tasks for which particle effects have been generated, thereby obtaining a matching result. The matching result includes at least: the task type, the paradigm corresponding to the task, and the specific brain area involved in the task. If the matching result is the same or similar, the particle effects corresponding to the cognitive training tasks that are the same or similar to the new cognitive training tasks are directly used as the particle effects of the new cognitive training tasks. If the matching result is not the same or similar, the particle effects for the new cognitive training tasks are regenerated.

[0097] Second embodiment

[0098] like Fig. 9 As shown, based on the above first embodiment, the second embodiment of the present invention provides a particle special effect rendering system for cognitive training tasks, and the particle special effect rendering system includes a processor 21 and a memory 22. Among them, the memory 22 is coupled to the processor 21, and is used to store one or more programs. When the program is executed by the processor 21, the processor 21 implements the particle special effect rendering method in the above embodiment.

[0099] Among them, the processor 21 is used to control the overall operation of the particle special effect rendering system to complete all or part of the steps of the above-mentioned particle special effect rendering method. The processor 21 can be a central processing unit (CPU), a graphics processing unit (GPU), a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a digital signal processing (DSP) chip, etc. The memory 22 is used to store various types of data to support the operation of the particle special effect rendering system, and these data may include, for example, instructions for any application or method used to operate on the particle special effect rendering system, and application-related data. The memory 22 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, etc.

[0100] In an exemplary embodiment, the particle special effect rendering system can be implemented by a computer chip or entity, or by a product with a certain function, for executing the above-mentioned particle special effect rendering method and achieving the same technical effect as the above-mentioned method. A typical embodiment is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0101] In another exemplary embodiment, the present invention further provides a computer-readable storage medium including program instructions, which, when executed by a processor, implements the steps of the particle special effect rendering method in any of the above embodiments. For example, the computer-readable storage medium may be the above-mentioned memory including program instructions, and the above-mentioned program instructions may be executed by a processor of a particle special effect rendering system to complete the above-mentioned particle special effect rendering method and achieve the same technical effect as the above-mentioned method.

[0102] Third embodiment

[0103] like Fig.10 As shown, a cognitive training system provided by the third embodiment of the present invention includes a login unit 1, a cognitive evaluation unit 2, a task configuration unit 3, a cognitive training unit 4, a particle special effect display unit 5 and a training feedback unit 6.

[0104] Among them, the login unit 1 is used for user registration and system login. The cognitive assessment unit 2 is connected to the login unit 1 for performing cognitive assessment on the user. The task configuration unit 3 is connected to the cognitive assessment unit 2 and is preset with a variety of cognitive training tasks to push personalized cognitive training programs to the user based on the user's cognitive assessment results. The cognitive training unit 4 is connected to the task configuration unit 3 to perform interactive training with the user based on the personalized cognitive training program. The particle special effect display unit 5 is connected to the cognitive training unit 4 to present the particle special effects transmitted by neurons to the user based on the particle special effect presentation method in the above-mentioned first embodiment after each cognitive training task is completed. The training feedback unit 6 is connected to the cognitive training unit 4 to feedback the final training results to the user based on the user's interactive training results.

[0105] In addition, in this embodiment, preferably, the particle special effect display unit 5 also includes a brain region label display module 51. The brain region label display module 51 is connected to the cognitive training unit 4 to dynamically display the cognitive function strength corresponding to each brain region label as the visual conduction path is conducted during the cognitive training task training process, thereby helping the user to more intuitively understand which cognitive functions are currently playing a role in cognitive training.

[0106] In summary, the particle special effects presentation method, system and cognitive training system for cognitive training tasks provided by the embodiments of the present invention have the following beneficial effects:

[0107] (1) Based on the reference of the open source brain model, the standardized template partitions used in scientific research are mapped to the brain neuron model, and the algorithm and effect are optimized. For the first time, based on the brain point cloud model, this invention provides standardized neural conduction partitions and paths for various cognitive training tasks, and performs particle characterization and dynamic effect display.

[0108] (2) For the visualized conduction pathway of cognitive training tasks, labels of brain regions involved in cognitive training tasks are added; and during cognitive training, as the visualized conduction pathway is conducted, the cognitive function strength corresponding to each brain region label is dynamically displayed, thereby helping users to more intuitively understand which cognitive functions are currently playing a role in cognitive training, allowing users to have a deeper understanding of their own situation and improve their compliance with cognitive training.

[0109] It should be noted that the above embodiments are only examples, and the technical solutions of the various embodiments can be combined, all within the protection scope of the present invention.

[0110] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0111] The particle special effects presentation method, system and cognitive training system for cognitive training tasks provided by the present invention are described in detail above. For a person skilled in the art, any obvious changes made to it without departing from the essence of the present invention will constitute an infringement of the patent right of the present invention and will bear corresponding legal liability.

Claims

1. A particle special effects presentation method for cognitive training tasks, characterized in that The steps include: Based on the 3D coordinate system, obtain the brain neuron point cloud model; Based on a standardized brain partition template, the brain neuron point cloud model is divided into brain regions; wherein each brain region corresponds to a different functional network; Based on the preset cognitive training tasks, the brain regions involved in the training process and the functional connections of each brain region are obtained, so as to obtain the brain neural network conduction pathway corresponding to the cognitive training task; Based on the brain neural network conduction path, the involved brain regions are mapped to the brain neuron point cloud model to obtain the three-dimensional coordinates of the relevant brain regions, and a visualized conduction path is generated based on a preset algorithm; Based on the visualized conduction path, the transmission delay time, disappearance time and regional neuron lighting effect are set, so as to present the particle effects of neuron transmission on the brain neuron point cloud model.

2. The particle special effects presentation method according to claim 1, characterized in that Based on the standardized brain partition template, the point cloud model is divided into brain regions, specifically including: Determine the sulci and gyri of the brain neuron point cloud model and the standardized brain partition template, and align the sulci and gyri of the two; The partitioning results of the standardized brain partitioning template are mapped one by one to the brain neuron point cloud model, so as to divide the brain neuron point cloud model into multiple brain regions to correspond to different functional networks respectively.

3. The particle special effects presentation method according to claim 1, characterized in that The brain neural network conduction pathway is obtained by: Based on the preset cognitive training tasks, the brain area related to perception at the beginning of the training is used as the starting brain area; Based on the preset cognitive training tasks, the brain areas corresponding to the advanced cognitive functions involved in the training process are used as process brain areas; Based on the preset cognitive training tasks, the brain area corresponding to the advanced cognitive functions involved at the end of the training is used as the termination brain area; A brain neural network conduction path is formed from the starting brain area through the process brain area to the ending brain area for the preset cognitive training task.

4. The particle special effect presentation method according to claim 1, characterized in that The visualized conduction pathway is obtained by: For each relevant brain region, a preset proportion of neurons are randomly selected, and neurons that are connected to brain regions outside the conduction pathway according to a preset algorithm are filtered out; The remaining neurons in each relevant brain region after filtering are connected to a random number of connections, where the maximum number of connections between each neuron and the nearest neurons around it is 5; Based on the principle of shortest straight-line distance, a three-dimensional Bezier curve path from the starting brain region to the ending brain region is generated according to the preset algorithm; According to the three-dimensional Bezier curve path, a set of three-dimensional space coordinate points of the curve is obtained; Randomly selecting a preset number of points from the three-dimensional space coordinate point set and connecting them to form a bifurcation for neuronal transmission from the surface of the brain; Obtaining the coordinates of the neurons in the brain neuron point cloud model that are closest to the straight line of the three-dimensional Bezier curve path, and adding them to the process of generating the three-dimensional Bezier curve path for performing neuron transmission from the inside of the brain; The control points, length, number of nodes, diameter and random time difference of the triggering sequence of the three-dimensional Bezier curve path are set to generate a visualized conduction path of neuron conduction.

5. The particle special effect presentation method according to claim 1, characterized in that: For the visualized conduction pathway of the cognitive training task, adding brain region labels involved in the cognitive training task; During the cognitive training task training process, as the visualized conduction pathway is conducted, the cognitive function strength corresponding to each brain region label is dynamically displayed.

6. The particle special effect presentation method according to claim 1, characterized in that Also includes: Generate different particle effects according to different cognitive training tasks; According to the brain neuron point cloud model and the particle special effects based on a variety of cognitive training tasks, a neural network conduction model for particle special effects presentation is jointly formed.

7. The particle special effect presentation method according to claim 6, characterized in that: For a new cognitive training task, the new cognitive training task is matched with a plurality of cognitive training tasks for which particle special effects have been generated to obtain a matching result; wherein the matching result at least includes: a task type, a paradigm corresponding to the task, and a specific brain area involved in the task; If the matching result is the same or similar, the particle special effects corresponding to the cognitive training task that is the same or similar to the new cognitive training task are directly used as the particle special effects of the new cognitive training task; if the matching result is not the same or similar, the particle special effects for the new cognitive training task are regenerated.

8. A particle special effects presentation system for cognitive training tasks, characterized by It comprises a processor and a memory, wherein the processor reads a computer program in the memory to execute the particle special effect presentation method according to any one of claims 1 to 7.

9. A cognitive training system, characterized in that include: Login unit, used for user registration and system login; a cognitive assessment unit, connected to the login unit, for performing cognitive assessment on the user; A task configuration unit, connected to the cognitive assessment unit and pre-set with a variety of cognitive training tasks, so as to push a personalized cognitive training program to the user based on the cognitive assessment result of the user; A cognitive training unit, connected to the task configuration unit, to perform interactive training with the user based on the personalized cognitive training program; A particle special effect display unit connected to the cognitive training unit to present the particle special effect transmitted by neurons to the user based on the particle special effect presentation method according to any one of claims 1 to 7 after each cognitive training task is completed; A training feedback unit is connected to the cognitive training unit to feed back the final training result to the user based on the interactive training result of the user.

10. The cognitive training system according to claim 9, characterized in that: The particle special effects display unit also includes a brain region label display module, which is connected to the cognitive training unit to dynamically display the cognitive function intensity corresponding to each brain region label as the visualized conduction path is conducted during the cognitive training task training process.

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