An ADHD Neurofeedback Training System and Method Based on EEG-Based Multi-Person Collaboration

Abstract

This invention belongs to the field of mixed reality technology. It provides an ADHD neurofeedback training system and method based on EEG-based multi-user collaboration. Using an intelligent car off-road adventure as a backdrop, it constructs rich and personalized MR scenes. Neurofeedback training is conducted by controlling the intelligent car along a self-planned walking route through attention control. Users can play together individually or in groups, choosing different control modes. Low-cost EEG equipment is used to acquire users' brainwave signals, calculate each user's attention level to control the intelligent car's speed, and provide corresponding feedback using MR effects based on each user's attention level, making the neurofeedback training process more effective.

Description

Technical Field

[0001] This invention relates to the field of mixed reality technology, and in particular to an ADHD neurofeedback training system and method based on EEG multi-person collaboration. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Mixed Reality (MR) is a technology that combines virtual information with the real world, and it is widely used, especially on mobile devices. Through the cameras, sensors, and screens of smartphones or tablets, MR technology can overlay virtual elements onto the user's real-world field of vision, enhancing their perception of their surroundings and interactive experience. Users can see real-time virtual images blended with real-world scenes on their mobile devices, resulting in a more intuitive and richer experience. This technology is widely used in fields such as gaming, navigation, shopping, and education, not only providing users with innovative ways to interact but also greatly enhancing the immersion and emotional connection of the user experience.

[0004] EEG (electroencephalography) is a method that records brain activity using electrophysiological indicators. During brain activity, the postsynaptic potentials generated synchronously by a large number of neurons are summed to form the EEG signal. EEG records the changes in electrical waves during brain activity, providing a comprehensive reflection of the electrophysiological activity of brain nerve cells on the cerebral cortex or scalp surface. An EEG device is a bio-amplifier that measures brain signals. It collects physiological signals from the human head through EEG sensors, amplifies and filters them, processes and analyzes the signals, and records, displaying, recording, and transmitting the results. In recent years, with continuous advancements in microelectronics, medical signal processing, and electrophysiological techniques, the stability, reliability, and portability of EEG devices have been significantly improved.

[0005] Neurofeedback training is a non-invasive therapy commonly used to help patients with Attention Deficit Hyperactivity Disorder (ADHD) improve their symptoms. By monitoring and receiving feedback on the brain's electrical activity in real time, patients can learn how to self-regulate specific brainwave patterns to enhance focus and self-control. During training, patients typically receive immediate feedback through on-screen games or tasks, helping them understand when they have reached an ideal brainwave state. Over time, this enhanced self-regulation can significantly improve attention, impulse control, and behavioral management in patients with ADHD. Neurofeedback training provides a drug-free intervention, offering additional options for patients and families seeking alternative treatments.

[0006] The inventors discovered that while some EEG neurofeedback training technologies exist that use various interactive methods to assist children with ADHD in their rehabilitation training, the main problems with current ADHD rehabilitation training systems are as follows:

[0007] (1) The training methods in the existing neurofeedback training system are very boring. They simply give pictures or prompts to make users focus their attention and give feedback. (2) The existing neurofeedback training system has complicated equipment connections. It usually requires sitting in front of a computer for a long time. Users will have many inconveniences when using the system. It cannot be ported to mobile device clients, making it difficult to make rehabilitation training at home. (3) The existing neurofeedback training system can only be trained by one person. Parents cannot participate when their children are training. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides an ADHD neurofeedback training system and method based on EEG-based multi-user collaboration. Using an intelligent car off-road adventure as a backdrop, it constructs rich and personalized MR scenes. Neurofeedback training is conducted by controlling the intelligent car along a self-planned walking route through attention control. Users can choose different control modes to play together, either individually or in groups. Low-cost EEG equipment is used to acquire users' brainwave signals, and the attention levels of each user are calculated to control the speed of the intelligent car. Based on each user's attention level, corresponding feedback is provided using MR effects, making the neurofeedback training process more effective.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] In a first aspect, the present invention provides an ADHD neurofeedback training system based on EEG multi-person collaboration.

[0011] An ADHD neurofeedback training system based on EEG multi-person collaboration includes: a mobile terminal, a server, a first EEG signal acquisition device, a second EEG signal acquisition device, and an intelligent vehicle. The first and second EEG signal acquisition devices are both connected to the server. The server is communicatively connected to the mobile terminal and the intelligent vehicle, respectively. The mobile terminal is communicatively connected to the intelligent vehicle.

[0012] The server is used to process the scene images uploaded by the mobile terminal to generate relevant walking routes and feed them back to the mobile terminal, so that the mobile terminal can build a model based on the walking route. The modeled virtual vehicle model is bound to the real location of the smart car collected by the mobile terminal in real time.

[0013] The server is used to calculate attention based on the EEG signals from the first and second EEG signal acquisition devices, and to control the speed of the smart car in conjunction with the task mode selected by the user.

[0014] As a further limitation of the first aspect of the present invention, the mobile terminal includes: a topic selection and model generation module, a scene arrangement module, an EEG data interaction feedback module, and a communication module;

[0015] The theme selection and model generation module includes: user-selected theme scenarios, user-uploaded photos of pasted routes, and user-corrected generated models.

[0016] The scene layout module generates a corresponding scene model based on the scene theme selected by the user, and allows the user to place the theme-related model by aligning the coordinates and clicking the button.

[0017] The EEG data interaction feedback module provides visual feedback on the attention of the two training participants.

[0018] The communication module is used to establish communication between the mobile terminal and the server.

[0019] As a further limitation of the first aspect of the present invention, the task mode includes: a collaborative mode and a division of labor mode;

[0020] In the cooperative mode, the speed of the smart car is: (average of each user's attention / 100) * V, where V is the maximum speed of the smart car, in cm / s;

[0021] In the division of labor mode, the first user's attention is used to control the start and stop of the smart car. If the first user's attention is less than the set threshold, the smart car will not start regardless of the second user's attention. If the first user's attention is greater than or equal to the set threshold, the smart car will start. The speed of the smart car is controlled by the second user's attention, specifically: (second user's attention value / 100) * V.

[0022] As a further limitation of the first aspect of the present invention, the base image for generating the walking route is derived from hand-drawn or tape arrangement, including a scene route map that the user arbitrarily draws on paper to construct, or a scene route map that is pasted with tape in an actual venue.

[0023] The mobile terminal uploads the actual route map captured by the camera to the server. The server supports manual or automatic image distortion correction to correct the distortion of the actual route map. After processing the corrected image, the walking route is extracted, encoded, and sent back to the mobile terminal.

[0024] As a further limitation of the first aspect of the present invention, the mobile terminal supports users in selecting the corresponding scene theme, and the mobile terminal performs MR scene modeling in the virtual space according to the returned encoding. The mobile terminal supports users in placing MR scene models at specific locations. After the MR scene model is built, the mobile device client supports the selection of spatial coordinates and the placement of small models related to the selected scene theme at the specified location.

[0025] As a further limitation of the first aspect of the present invention, the mobile terminal uses the camera to identify the location of the smart car. After successful identification, the virtual vehicle model in the corresponding MR scene model is bound to the identified smart car. The virtual vehicle model can be changed according to preference.

[0026] As a further limitation of the first aspect of the invention, feedback on the attention of the two training participants is provided in a visual form, including: UI feedback and MR feedback;

[0027] UI feedback is achieved through an energy bar. In training scenarios, when the user's attention is low, the corresponding energy bar changes to a set color to alert the user that their attention is low.

[0028] MR feedback is displayed through different effects of the speed ring. When the user's attention value is greater than or equal to threshold A and less than or equal to threshold B, the transmission speed is 0. When the smart car decelerates, the speed ring displays the first set effect. When the user's attention value is greater than threshold B and less than or equal to threshold C, a medium speed is transmitted. When the smart car is running at a medium speed, the speed ring displays the second set effect. When the user's attention value is greater than threshold C, a high speed is transmitted. When the smart car is traveling at high speed, the speed ring displays the third set effect.

[0029] As a further limitation of the first aspect of the present invention, during the training process, each user is supported in adjusting their own attention and coordinating team communication based on the feedback results of the energy bar and the smart car; after the training is completed, users are supported in viewing the results of this training, including the average attention value, the time distribution of attention level, the total mileage of the smart car and the number of items placed.

[0030] Secondly, this invention provides an ADHD neurofeedback training method based on EEG multi-person collaboration.

[0031] An ADHD neurofeedback training method based on EEG multi-person collaboration, utilizing the ADHD neurofeedback training system based on EEG multi-person collaboration described in the first aspect of this invention, includes the following process:

[0032] Users select roles and scene themes;

[0033] Users can freely design the smart car's route within a given space according to their own ideas. After the route design is completed, the mobile terminal takes a picture of the route and uploads it to the server for analysis and processing. The server analyzes the image, extracts the route, and returns it to the mobile terminal. After receiving the data, the mobile terminal generates a corresponding MR scene model based on the previously selected scene theme, and the user places it in the real world, placing the smart car on the real route. At this time, the mobile terminal automatically captures the position of the smart car and generates a virtual vehicle model in the virtual space to overlay on the real smart car. The virtual vehicle model can be freely selected by the user.

[0034] The virtual vehicle model has a halo underneath. During training, the halo's effects are adjusted in real time according to the speed of the smart car. Users can freely place small models related to the theme in the virtual space to enhance the vividness of the training scene.

[0035] Two users wear the first and second EEG signal acquisition devices respectively, turn on the server to enter formal training, and after the "Start Training" button is clicked, the countdown will start automatically and the training will officially begin.

[0036] During training, raw EEG data is transmitted to a computer server. After receiving the raw EEG data, the server calculates the real-time attention value of each user and controls the speed of the smart car according to the task mode selected by the user.

[0037] As a further limitation of the second aspect of the invention, the intelligent vehicle uses a front-facing camera to achieve line-following function, and adjusts the wheel speed in real time according to the received speed, thereby using attention control to control the speed of the intelligent vehicle.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] 1. This invention innovatively proposes an ADHD neurofeedback training method based on EEG multi-person collaboration. Users do not need to wear heavy and expensive head-mounted displays, but only need a portable terminal device (such as a mobile phone, tablet, etc.) to participate in the training process. It adopts a multi-person collaborative mixed reality interaction mode, with the background of inviting participants to plan and participate in the competition. The game has a shared collective task goal, and measures the collective game status for real-time game adjustment, which helps to stimulate the motivation and behavior of multi-person collaboration, and enhance team cohesion and individual empathy.

[0040] 2. This invention improves upon existing neurofeedback training, moving beyond simple images on a computer screen to organically combine the virtual and real worlds through the camera of a mobile terminal device. This not only makes the entire training process more personalized but also helps enhance the sustainability of user training.

[0041] 3. During the task, each team member not only needs to consider their own attention and relaxation level, but also needs to observe the attention and relaxation level of other team members. During the game, the team members who are not in a good state can be reminded to improve the team's collective attention. Through mutual communication and cooperation among members, the team members' cooperation efficiency can be improved.

[0042] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0043] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0044] Figure 1 Hardware architecture diagram provided for embodiments of the present invention;

[0045] Figure 2 This is a schematic diagram of the mobile platform APP workflow provided in an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of the BrainLink Pro brainwave intelligent hardware device worn by a user, provided in an embodiment of the present invention;

[0047] Figure 4 This is an example image showing how users paste paths with help.

[0048] Figure 5 Example image of a user capturing a pasted path image using an application;

[0049] Figure 6 Example diagrams of how users can generate and deploy the main model of the track with assistance;

[0050] Figure 7 An example diagram illustrating how users experience a scenario by running an application on a mobile device client;

[0051] Figure 8(a) is an example diagram of scene theme selection provided by the embodiments of the present invention in a specific embodiment;

[0052] Figure 8(b) is an example of a photograph of an actual race track taken according to a specific embodiment of the present invention;

[0053] Figure 8(c) is an example diagram of the scene generation effect applied to a specific embodiment of the present invention;

[0054] Figure 8(d) is an example diagram of scene theme model placement applied to a specific embodiment of the present invention;

[0055] Figure 8(e) is an example diagram of an MR car model applied to a specific embodiment of the present invention;

[0056] Figure 8(f) is an example diagram of the attention level feedback UI applied to the execution practice scenario of a specific embodiment of the present invention;

[0057] Figure 8(g) is an example of the corresponding special effects of the MR car model applied to the execution training scenario of a specific embodiment of the present invention. Detailed Implementation

[0058] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0059] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all 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 pertains.

[0060] This implementation proposes an ADHD neurofeedback training system based on EEG devices, constructing a virtual-real hybrid training environment. This system not only provides users with a rich physical interaction experience using an intelligent vehicle, but also leverages mixed reality technology to offer a virtual space where users can freely create. This significantly optimizes the user's training experience and helps improve the sustainability of training. During the arrangement of the virtual space, users can communicate and negotiate to create a shared and enjoyable environment, which is beneficial for improving users' comprehensive abilities, such as social skills, rather than focusing solely on improving attention span. During training, each user needs to assess not only their own attention level but also the attention level of their partner. The system adjusts the speed of the intelligent vehicle according to a pre-set pattern, thus requiring collaboration among users to better complete the task.

[0061] The system includes: one computer server (i.e., server), one mobile device client (i.e., mobile terminal), one TurboPi AI smart car, and two EEG signal acquisition devices (BrainLink, i.e., the first EEG signal acquisition device and the second EEG signal acquisition device). The computer server is communicatively connected to the smart car, the EEG signal acquisition devices and the mobile device client, respectively, and the mobile device client is communicatively connected to the smart car.

[0062] In this implementation, all devices are on the same local area network (LAN), which is enabled by the smart car to provide a data transmission channel. The two sets of BrainLink EEG smart hardware devices are connected to the computer server via Bluetooth and interact with each other.

[0063] Specifically, the mobile device client includes a topic selection and model generation module, a scene setup module, an EEG data interaction feedback module, and a communication module.

[0064] The topic selection and model generation module includes: a user selects a topic scenario, a user takes a picture of the pasted route and uploads it, and a user corrects the generated model. In these three scenarios, the users participating in the training complete the virtual modeling of the path pasted in the actual scenario with the help of relevant personnel.

[0065] The scene layout module is configured to: based on the Vuforia architecture, generate a corresponding scene model in the mobile device client APP according to the scene theme selected by the test user, and allow the user to place the theme-related model by aligning the coordinates and clicking the button.

[0066] The EEG data interaction feedback module is configured to provide feedback on the attention of two training participants in a visual form. The feedback is divided into UI feedback and MR feedback. The UI feedback is implemented through the "energy bar" shown in Figure 8(f). In the training scenario, if user 1's attention is low, the corresponding "energy bar" will turn red to indicate that the user's attention is low.

[0067] MR feedback is demonstrated through different effects of the "speed ring" as shown in Figure 8(g). When the user's attention value is greater than or equal to threshold A and less than or equal to threshold B, the transmission speed is 0. When the smart car decelerates, the speed ring displays an "icing" effect. When the user's attention value is greater than threshold B and less than or equal to threshold C, a medium speed is transmitted. When the smart car is running at a medium speed, the speed ring displays a "flickering" effect. When the user's attention value is greater than threshold C, a higher speed is transmitted. When the smart car is traveling at high speed, the speed ring displays a "star halo" effect.

[0068] The communication module is used to connect the APP and the server, including establishing a connection, sending data, and parsing data. The server has data processing and forwarding functions, enabling direct communication and message transmission with the APP and the smart car. Users of this system do not need to worry about distance between team members; they can control the smart car within a local area network using the communication module.

[0069] In this implementation, the APP is installed on any mobile device client and needs to access the device's camera and WIFI functions. When playing and experiencing this training system, users need to wear an EEG signal acquisition device to collect and transmit the user's EEG information to the server for attention assessment.

[0070] The EEG signal detection device uses two EEG devices that communicate with the server. These EEG devices provide the user's brainwave data. When the human brain is functioning normally, neurons release neurotransmitters to transmit information. The process of neurons releasing and transmitting neurotransmitters is an electrochemical reaction that generates weak brain currents called brainwaves. The EEG devices acquire the user's THETA distraction waves, SMR attention waves, and Hi-beta tension waves. Then, the built-in eSense algorithm analyzes the length of the wavebands to analyze the user's attention and relaxation level.

[0071] The EEG device first amplifies the raw EEG signal and filters out environmental noise and interference from muscle movement. Then, it applies the eSense algorithm to the processed signal to obtain quantified eSense parameter values. The eSense algorithm represents the analysis results in quantified numerical form. Its working principle involves performing a Fast Fourier Transform on the acquired EEG data, then filtering the frequencies to obtain alpha and beta waves, and finally performing power spectrum analysis to select a basic reference value and complete normalization.

[0072] Before formal training begins, users can design their own training scenarios. Within a given space, users can paste intelligent walking routes according to their ideas. After the walking routes are pasted, the system generates a corresponding virtual vehicle model based on the real-world walking routes. The virtual vehicle model supports a high degree of customization; users can choose the model theme and the virtual vehicle model of the intelligent car, and can also freely place smaller models related to the theme on top of the virtual vehicle model.

[0073] After the training scenario is set up, formal training begins. Each user, wearing their EEG device, can start training. The system will then process each user's attention level in real-time according to the pre-defined task mode and convert it into the speed of the intelligent car—a process completely transparent to the user. During training, the user's attention level will be fed back to them in real-time as an energy bar, allowing them to adjust their strategies accordingly. Throughout the training, the user's sole task is to maximize their own and their team's attention and make the intelligent car travel as far as possible within the given 3 minutes.

[0074] After training, the system will automatically record various data during the training process, including the time distribution of each user's attention and the mileage traveled by the smart car, so that users and researchers can conduct further analysis.

[0075] Specifically, such as Figure 2 As shown, it includes the following steps:

[0076] (1) System introduction and theme selection: In this step, users will first have an overall understanding of the background and functions of the system. Users can select the role and scene theme in the system, as shown in Figure 8(a).

[0077] (2) Training Scenario Design: First, users can freely design intelligent walking routes within a given spatial range according to their own ideas. These routes can be quadrilaterals, pentagons, or hearts, etc. (e.g.) Figure 4 As shown in Figure 8(b), after the walking route is designed, the user takes photos of the walking route using a mobile device client, and uploads them to the computer server for analysis (e.g., ...). Figure 5 As shown in Figure 8(c), the computer server analyzes the image using techniques such as perspective transformation and threshold recognition to extract model data, which is then returned to the mobile device client. Upon receiving the data, the mobile device client generates a corresponding MR scene model based on the previously selected scene theme, which is then projected into the real world by the user (e.g., ...). Figure 6 (As shown in Figure 8(e)); Next, place the smart car on the real-world walking route. At this time, the system will automatically capture the position of the smart car and generate a virtual vehicle model in the virtual space to overlay on the real smart car, as shown in Figure 8(e). The user can freely choose this virtual vehicle model, which can be a pickup truck, police car, excavator, etc. There is a halo under the virtual vehicle model. During the training process, the system will adjust the halo effect in real time according to the speed of the smart car, as shown in Figure 8(g), which corresponds to low speed, medium speed and high speed from left to right; Afterwards, the user can freely place small models related to the theme in the virtual space, as shown in Figure 8(d), so as to make the training scene more vivid and interesting.

[0078] (3) Preparation before formal training: The user correctly wears the EEG signal acquisition device, such as Figure 3 As shown, the server for the smart car is turned on to perform connection and function tests. After the tests are successful, formal training begins.

[0079] (4) Formal training: Click the “Start training” button and the system will automatically start a three-minute countdown. The training will officially begin. During the training, the BrainLink device will collect the user’s raw EEG data and transmit it to the computer server. After receiving the raw EEG data, the server will calculate the real-time attention value of each user and convert the attention value into the corresponding speed of the smart car according to the preset conversion formula, and send it to the smart car.

[0080] The relationship between the speed of the smart car and the user's attention level is as follows:

[0081] If it is in the combined force mode, the speed of the smart car is equal to: (average of each user's attention / 100) * V, where V is the maximum speed of the smart car, in cm / s;

[0082] In the division of labor mode, user 1's (i.e., the first user) attention is used to control the start and stop of the smart car. If user 1's attention is less than the set threshold, the smart car will not start regardless of user 2's (i.e., the second user's) attention. If user 1's attention is greater than or equal to the set threshold, the smart car will start, and the speed will be controlled by user 2's attention, specifically: (user 2's attention value / 100) * V.

[0083] The intelligent car uses a front-facing camera to achieve line-following function, and adjusts the speed of the intelligent car's wheels in real time according to the received speed, thus realizing the function of controlling the speed of the intelligent car with attention. Throughout the training process, the interface on the mobile device client will provide users with rich feedback in real time, including using the length and color of the energy bar to indicate the level of user attention (as shown in Figure 8(f)) and using halo effects to indicate the status of the intelligent car.

[0084] (5) Training ends: After the system countdown ends in three minutes, training stops and the system automatically records user data during the training process, including raw EEG data, real-time attention values ​​and total mileage of the smart car.

[0085] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An ADHD neurofeedback training system based on EEG multi-person collaboration. Its features are, It includes: a mobile terminal, a server, a first EEG signal acquisition device, a second EEG signal acquisition device, and an intelligent vehicle. The first and second EEG signal acquisition devices are both connected to the server. The server is communicatively connected to the mobile terminal and the intelligent vehicle, respectively. The mobile terminal is communicatively connected to the intelligent vehicle. The server is used to process the scene images uploaded by the mobile terminal to generate relevant walking routes and feed them back to the mobile terminal, so that the mobile terminal can build a model based on the walking route. The modeled virtual vehicle model is bound to the real location of the smart car collected by the mobile terminal in real time. The server is used to calculate attention based on the EEG signals from the first and second EEG signal acquisition devices, and to control the speed of the smart car in combination with the task mode selected by the user. The task modes include: collaborative mode and division of labor mode; In the cooperative mode, the speed of the smart car is: (average of each user's attention / 100) * V, where V is the maximum speed of the smart car, in cm / s; In the division of labor mode, the first user's attention is used to control the start and stop of the smart car. If the first user's attention is less than the set threshold, the smart car will not start regardless of the second user's attention. If the first user's attention is greater than or equal to the set threshold, the smart car will start. The speed of the smart car is controlled by the second user's attention, specifically: (second user's attention value / 100) * V.

2. The ADHD neurofeedback training system based on EEG multi-person collaboration as described in claim 1, characterized in that, The mobile terminal includes: a topic selection and model generation module, a scene arrangement module, an EEG data interaction feedback module, and a communication module; The theme selection and model generation module includes: user-selected theme scenarios, user-uploaded photos of pasted routes, and user-corrected generated models. The scene layout module generates a corresponding scene model based on the scene theme selected by the user, and allows the user to place the theme-related model by aligning the coordinates and clicking the button. The EEG data interaction feedback module provides visual feedback on the attention of the two training participants. The communication module is used to establish communication between the mobile terminal and the server.

3. The ADHD neurofeedback training system based on EEG multi-person collaboration as described in claim 1, characterized in that, The base images for generating walking routes come from hand-drawn or tape arrangements, including scene route maps that users draw on paper to construct, or scene route maps that are pasted with tape in the actual venue. The mobile terminal uploads the actual route map captured by the camera to the server. The server supports manual or automatic image distortion correction to correct the distortion of the actual route map. After processing the corrected image, the walking route is extracted, encoded, and sent back to the mobile terminal.

4. The ADHD neurofeedback training system based on EEG multi-person collaboration as described in claim 1, characterized in that, The mobile terminal allows users to select the corresponding scene theme. The mobile terminal models the MR scene model in the virtual space according to the returned code. The mobile terminal allows users to place the MR scene model at a specific location. After the MR scene model is built, the mobile device client can select the spatial coordinates and place a small model related to the selected scene theme at the specified location.

5. The ADHD neurofeedback training system based on EEG multi-person collaboration as described in claim 1, characterized in that, The mobile terminal uses its camera to identify the location of the smart car. Once the identification is successful, the virtual vehicle model in the corresponding MR scene model is bound to the identified smart car. The virtual vehicle model can be changed according to preferences.

6. The ADHD neurofeedback training system based on EEG multi-person collaboration as described in claim 1, characterized in that, Feedback on the attention of the two training participants was provided in a visual form, including UI feedback and MR feedback; UI feedback is achieved through an energy bar. In training scenarios, when the user's attention is low, the corresponding energy bar changes to a set color to alert the user that their attention is low. MR feedback is displayed through different effects of the speed ring. When the user's attention value is greater than or equal to threshold A and less than or equal to threshold B, the transmission speed is 0. When the smart car decelerates, the speed ring displays the first set effect. When the user's attention value is greater than threshold B and less than or equal to threshold C, a medium speed is transmitted. When the smart car is running at a medium speed, the speed ring displays the second set effect. When the user's attention value is greater than threshold C, a high speed is transmitted. When the smart car is traveling at high speed, the speed ring displays the third set effect.

7. The ADHD neurofeedback training system based on EEG multi-person collaboration as described in claim 6, characterized in that, During training, it supports individual users in adjusting their attention and coordinating team communication based on feedback from the energy bar and the smart car. After training, users can view the results of the training, including the average attention score, the time distribution of attention levels, the total mileage of the smart car, and the number of items placed.

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