Brain-computer interface generation method and apparatus giving consideration to fairness, and storage medium

Abstract

The method in the present application comprises: determining a target interface, and presenting the target interface; for any visual stimulation region, collecting an electroencephalogram signal corresponding to the visual stimulation region, determining a target operation on the basis of the electroencephalogram signal, and matching the target operation with a reference operation, so as to acquire a matching result corresponding to the visual stimulation region; on the basis of the matching result corresponding to each visual stimulation region, determining a total matching result corresponding to the target interface; if the total matching result indicates that the target interface is not applicable, adjusting the target interface; and on the basis of the current target interface, building a brain-computer interface.

Description

Brain-computer interface generation method, device and storage medium taking fairness into consideration

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese patent application filed with the China Patent Office on September 1, 2023, with application number 202311119120.9 and application name “Brain-computer interface generation method, device and storage medium taking fairness into consideration”, the full text of which is hereby incorporated by reference. Technical Field

[0003] The present application relates to the field of brain-computer interface technology, and in particular to a method, device and storage medium for generating a brain-computer interface that takes fairness into consideration. Background Art

[0004] With the gradual development of brain-computer interface technology, it has also begun to be applied in the field of medical rehabilitation. Through the brain-computer interface system, users can type, make phone calls, control wheelchairs, and other operations, allowing users with limited mobility to use external tools normally.

[0005] However, given that different users have different adaptability to different brain-computer interface systems, some users may not be able to adapt to the standard brain-computer interface, resulting in the inability to successfully use the standard brain-computer interface to complete the intended operation. As a result, the standard brain-computer interface cannot fairly enable all users to use the brain-computer interface technology. Therefore, it is necessary to personalize the standard brain-computer interface according to the characteristics of different users to improve the adaptability of the brain-computer interface system to different users.

[0006] Summary of the Invention

[0007] Based on this, it is necessary to provide a brain-computer interface generation method, device, computer equipment and storage medium that take fairness into account in order to address the above technical problems.

[0008] In a first aspect, the present application provides a method for generating a brain-computer interface that takes fairness into account. The method comprises:

[0009] Determining a target interface and displaying the target interface, wherein the target interface includes at least one visual stimulation area, and each visual stimulation area corresponds to a different benchmark operation;

[0010] For any of the visual stimulation areas, collecting an electroencephalogram (EEG) signal corresponding to the visual stimulation area, determining a target operation based on the EEG signal, and matching the target operation with the reference operation to obtain a matching result corresponding to the visual stimulation area;

[0011] Determining a total matching result corresponding to the target interface according to the matching results corresponding to each of the visual stimulation areas;

[0012] When the total matching result indicates that the target interface is not compatible, adjusting the target interface, wherein the number of visual stimulation areas of the target interface after the adjustment is less than the number of visual stimulation areas of the target interface before the adjustment;

[0013] Based on the current target interface, a brain-computer interface is constructed.

[0014] In one embodiment, constructing a brain-computer interface based on the current target interface includes:

[0015] Obtain target operation tasks;

[0016] Determining the number of target tasks corresponding to the target operation task;

[0017] When the number of single-interface tasks corresponding to the current target interface is less than the number of target tasks, a multi-level brain-computer interface is constructed based on the number of target tasks and the target interface; the number of single-interface tasks is the number of visual stimulation areas of the current target interface.

[0018] In one embodiment, constructing a brain-computer interface based on the current target interface further includes:

[0019] When the number of the single-interface tasks is equal to the number of the target tasks, the current target interface is used as the brain-computer interface interface, and a visual stimulation area is allocated to each target task in the brain-computer interface.

[0020] In one embodiment, constructing a brain-computer interface based on the current target interface further includes:

[0021] When the number of tasks in the single interface is greater than the number of target tasks, target visual stimulation areas equal to the number of target tasks are selected in the current target interface, and a brain-computer interface is constructed based on each target visual stimulation area; the interval between each target visual stimulation area in the brain-computer interface is larger than the interval between each target visual stimulation area in the current target interface.

[0022] In one embodiment, constructing a multi-level brain-computer interface based on the number of target tasks and the target interface includes:

[0023] Treat all the target tasks as tasks to be grouped;

[0024] Constructing the number of first-level groups of the single-interface tasks, and assigning each of the tasks to be grouped to each of the first-level groups;

[0025] Based on the target interface, constructing a first-level brain-computer interface interface, and assigning a visual stimulation area to each of the first-level groups in the first-level brain-computer interface interface;

[0026] For any of the first-level groups, construct a second-level brain-computer interface interface corresponding to the first-level group according to the target interface, and when the number of the target tasks in the first-level group is less than or equal to the number of the single-interface tasks, allocate a visual stimulation area in the second-level brain-computer interface interface to each of the target tasks in the first-level group;

[0027] Among them, triggering the visual stimulation area corresponding to the first-level group in the first-level brain-computer interface interface can jump to the second-level brain-computer interface corresponding to the first-level group, and triggering the visual stimulation area corresponding to the target task in the second-level brain-computer interface interface can trigger the target task.

[0028] In one embodiment, the constructing of a multi-level brain-computer interface based on the target number of tasks and the target interface further includes:

[0029] When the number of the target tasks in the first-level group is greater than the number of the single-interface tasks, the target tasks in the first-level group are used as the tasks to be grouped;

[0030] Constructing the number of secondary groups of the single-interface tasks, allocating each of the tasks to be grouped to each of the secondary groups, and allocating a visual stimulation area to each of the secondary groups in the secondary brain-computer interface interface;

[0031] For any of the secondary groups, constructing a third-level brain-computer interface corresponding to the secondary group according to the target interface, and when the number of the target tasks in the secondary group is less than or equal to the number of the single-interface tasks, allocating a visual stimulation area to each of the target tasks in the secondary group in the third-level brain-computer interface;

[0032] Among them, triggering the visual stimulation area corresponding to the group in the secondary brain-computer interface interface can jump to the tertiary brain-computer interface corresponding to the secondary group, and triggering the visual stimulation area corresponding to the target task in the tertiary brain-computer interface interface can trigger the target task.

[0033] In one embodiment, allocating each of the tasks to be grouped to each of the first-level groups includes:

[0034] Determining the usage frequency of each of the tasks to be grouped;

[0035] Dividing the single-interface tasks into frequency intervals according to the usage frequency of each task to be grouped;

[0036] Determine the first-level groups corresponding to the frequency intervals respectively;

[0037] For any of the tasks to be grouped, a target frequency interval corresponding to the task to be grouped is determined based on the usage frequency of the task to be grouped, and the task to be grouped is allocated to the first-level group corresponding to the target frequency interval.

[0038] In one embodiment, the number of tasks to be grouped corresponding to each frequency interval is the same.

[0039] In one embodiment, the target interface before adjustment and the target interface after adjustment meet at least one of the first condition, the second condition and the third condition;

[0040] The first condition is that the frequency difference between the visual stimulation areas in the target interface after adjustment is greater than the frequency difference between the visual stimulation areas in the target interface before adjustment;

[0041] The second condition is that the area of ​​each visual stimulation region in the target interface after adjustment is larger than the area of ​​each visual stimulation region in the target interface before adjustment;

[0042] The third condition is that the distance between the visual stimulation areas in the target interface after adjustment is larger than the distance between the visual stimulation areas in the target interface before adjustment.

[0043] In one embodiment, constructing a brain-computer interface based on the current target interface includes:

[0044] Display the current target interface and the interface parameters corresponding to the current target interface;

[0045] In response to an input operation on a target interface parameter, obtaining the target interface parameter;

[0046] According to the target interface parameters, the current target interface is updated, and a brain-computer interface is constructed based on the updated target interface.

[0047] In one embodiment, the interface parameters include at least one of the frequency, area, spacing, and shape of each of the visual stimulation areas.

[0048] In a second aspect, the present application also provides a device for generating a brain-computer interface that takes fairness into consideration. The device comprises:

[0049] a display module, configured to determine a target interface and display the target interface, wherein the target interface includes at least one visual stimulation area, and each visual stimulation area corresponds to a different benchmark operation;

[0050] a matching module, configured to collect an electroencephalogram (EEG) signal corresponding to any of the visual stimulation areas, determine a target operation based on the EEG signal, and match the target operation with the reference operation to obtain a matching result corresponding to the visual stimulation area;

[0051] an adjustment module, configured to determine a total matching result corresponding to the target interface based on the matching results corresponding to each of the visual stimulation areas, and adjust the target interface if the total matching result indicates that the target interface is not matched, wherein the number of visual stimulation areas of the target interface after the adjustment is less than the number of visual stimulation areas of the target interface before the adjustment;

[0052] A construction module is used to construct a brain-computer interface based on the current target interface.

[0053] In one embodiment, the building block is further configured to:

[0054] Obtain target operation tasks;

[0055] Determining the number of target tasks corresponding to the target operation task;

[0056] When the number of single-interface tasks corresponding to the current target interface is less than the number of target tasks, a multi-level brain-computer interface is constructed based on the number of target tasks and the target interface; the number of single-interface tasks is the number of visual stimulation areas of the current target interface.

[0057] In one embodiment, the building block is further configured to:

[0058] When the number of the single-interface tasks is equal to the number of the target tasks, the current target interface is used as the brain-computer interface interface, and a visual stimulation area is allocated to each target task in the brain-computer interface.

[0059] In one embodiment, the building block is further configured to:

[0060] When the number of tasks in the single interface is greater than the number of target tasks, target visual stimulation areas equal to the number of target tasks are selected in the current target interface, and a brain-computer interface is constructed based on each target visual stimulation area; the interval between each target visual stimulation area in the brain-computer interface is larger than the interval between each target visual stimulation area in the current target interface.

[0061] In one embodiment, the building block is further configured to:

[0062] Treat all the target tasks as tasks to be grouped;

[0063] Constructing the number of first-level groups of the single-interface tasks, and assigning each of the tasks to be grouped to each of the first-level groups;

[0064] Based on the target interface, constructing a first-level brain-computer interface interface, and assigning a visual stimulation area to each of the first-level groups in the first-level brain-computer interface interface;

[0065] For any of the first-level groups, construct a second-level brain-computer interface interface corresponding to the first-level group according to the target interface, and when the number of the target tasks in the first-level group is less than or equal to the number of the single-interface tasks, allocate a visual stimulation area in the second-level brain-computer interface interface to each of the target tasks in the first-level group;

[0066] Among them, triggering the visual stimulation area corresponding to the first-level group in the first-level brain-computer interface interface can jump to the second-level brain-computer interface corresponding to the first-level group, and triggering the visual stimulation area corresponding to the target task in the second-level brain-computer interface interface can trigger the target task.

[0067] In one embodiment, the building block is further configured to:

[0068] When the number of the target tasks in the first-level group is greater than the number of the single-interface tasks, the target tasks in the first-level group are used as the tasks to be grouped;

[0069] Constructing the number of secondary groups of the single-interface tasks, allocating each of the tasks to be grouped to each of the secondary groups, and allocating a visual stimulation area to each of the secondary groups in the secondary brain-computer interface interface;

[0070] For any of the secondary groups, constructing a third-level brain-computer interface corresponding to the secondary group according to the target interface, and when the number of the target tasks in the secondary group is less than or equal to the number of the single-interface tasks, allocating a visual stimulation area to each of the target tasks in the secondary group in the third-level brain-computer interface;

[0071] Among them, triggering the visual stimulation area corresponding to the group in the secondary brain-computer interface interface can jump to the tertiary brain-computer interface corresponding to the secondary group, and triggering the visual stimulation area corresponding to the target task in the tertiary brain-computer interface interface can trigger the target task.

[0072] In one embodiment, the building block is further configured to:

[0073] Determining the usage frequency of each of the tasks to be grouped;

[0074] Dividing the single-interface tasks into frequency intervals according to the usage frequency of each task to be grouped;

[0075] Determine the first-level groups corresponding to the frequency intervals respectively;

[0076] For any of the tasks to be grouped, a target frequency interval corresponding to the task to be grouped is determined based on the usage frequency of the task to be grouped, and the task to be grouped is allocated to the first-level group corresponding to the target frequency interval.

[0077] In one embodiment, the number of tasks to be grouped corresponding to each frequency interval is the same.

[0078] In one embodiment, the target interface before adjustment and the target interface after adjustment meet at least one of the first condition, the second condition and the third condition;

[0079] The first condition is that the frequency difference between the visual stimulation areas in the target interface after adjustment is greater than the frequency difference between the visual stimulation areas in the target interface before adjustment;

[0080] The second condition is that the area of ​​each visual stimulation region in the target interface after adjustment is larger than the area of ​​each visual stimulation region in the target interface before adjustment;

[0081] The third condition is that the distance between the visual stimulation areas in the target interface after adjustment is larger than the distance between the visual stimulation areas in the target interface before adjustment.

[0082] In one embodiment, the building block is further configured to:

[0083] Display the current target interface and the interface parameters corresponding to the current target interface;

[0084] In response to an input operation on a target interface parameter, obtaining the target interface parameter;

[0085] According to the target interface parameters, the current target interface is updated, and a brain-computer interface is constructed based on the updated target interface.

[0086] In one embodiment, the interface parameters include at least one of the frequency, area, spacing, and shape of each of the visual stimulation areas.

[0087] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements any of the above methods when executing the computer program.

[0088] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements any of the above methods when executed by a processor.

[0089] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which implements any of the above methods when executed by a processor.

[0090] The details of one or more embodiments of the present application are set forth in the following drawings and description. Other features, objects, and advantages of the present application will become apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] In order to better describe and illustrate the embodiments of the present application disclosed herein, reference may be made to one or more of the accompanying drawings. The additional details or examples used to describe the accompanying drawings should not be considered to limit the scope of the disclosed invention, the presently described embodiments, and any of the best modes of these inventions currently understood.

[0092] FIG1 is a schematic diagram of a flow chart of a method for generating a brain-computer interface that takes fairness into consideration in one embodiment;

[0093] FIG2 is a schematic diagram of adjusting a target interface in one embodiment;

[0094] FIG3 is a schematic diagram of constructing a brain-computer interface in one embodiment;

[0095] FIG4 is a schematic diagram of step 108 in one embodiment;

[0096] FIG5 is a schematic diagram of constructing a brain-computer interface in one embodiment;

[0097] FIG6 is a schematic diagram of constructing a multi-level brain-computer interface in one embodiment;

[0098] FIG7 is a schematic diagram of step 404 in one embodiment;

[0099] FIG8 is a schematic diagram of step 404 in one embodiment;

[0100] FIG9 is a schematic diagram of constructing a multi-level brain-computer interface in one embodiment;

[0101] FIG10 is a schematic diagram of step 704 in one embodiment;

[0102] FIG11 is a schematic diagram of constructing a multi-level brain-computer interface according to usage frequency in one embodiment;

[0103] FIG12 is a schematic diagram of adjusting a target interface in one embodiment;

[0104] FIG13 is a schematic diagram of step 108 in one embodiment;

[0105] FIG14 is a schematic diagram of a brain-computer interface editing system according to one embodiment;

[0106] FIG15 is a schematic diagram of a process for storing EEG data in one embodiment;

[0107] FIG16 is a schematic diagram of a storage structure of EEG data in one embodiment;

[0108] FIG17 is a structural block diagram of a device for generating a brain-computer interface that takes fairness into consideration in one embodiment;

[0109] FIG18 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0110] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0111] In one embodiment, as shown in FIG1 , a method for generating a brain-computer interface that takes fairness into account is provided. This embodiment uses the method applied to a terminal as an example. It is understood that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0112] Step 102 : determining a target interface and displaying the target interface. The target interface includes at least one visual stimulation area, and each visual stimulation area corresponds to a different reference operation.

[0113] In the embodiment of the present application, the target interface is a brain-computer interface that includes at least one visual stimulation area. The number of visual stimulation areas in the target interface, the frequency of the visual stimulation coding sequence corresponding to each visual stimulation area, and the shape and size of each visual stimulation area can all be set by those skilled in the art according to actual needs. For example, when the terminal first displays the target interface to the user, it can select a standard brain-computer interface as the target interface. Subsequently, the target interface can be adjusted based on the compatibility between the user and the target interface.

[0114] Each visual stimulation area in the target interface corresponds to a different baseline operation. A baseline operation is an operation that can be performed on a device connected to the brain-computer interface after the visual operation area is triggered, such as controlling a wheelchair to move in a certain direction or typing a letter or number on the screen. The baseline operation corresponding to each visual stimulation area can also be pre-set by those skilled in the art and is not specifically limited in this embodiment of the present application.

[0115] Step 104 : For any visual stimulation area, collect the EEG signal corresponding to the visual stimulation area, determine the target operation based on the EEG signal, and match the target operation with the reference operation to obtain a matching result corresponding to the visual stimulation area.

[0116] In an embodiment of the present application, the visual stimulation area flashes according to the visual stimulation coding sequence. When the user looks at the visual stimulation area, the EEG signal will show a regular pattern due to the flashing of the visual stimulation area. By matching the user's EEG signal with the standard EEG signal corresponding to each visual stimulation sequence, the visual stimulation coding sequence that induces the EEG signal can be obtained, and then the visual stimulation area that the user is currently looking at can be determined, and the target operation corresponding to the visual stimulation area can be obtained.

[0117] However, if the user is disturbed by other visual stimulation areas, the user's EEG signal may be affected by other visual stimulation areas, causing the user's EEG signal to match the standard EEG signal of other visual stimulation coding sequences. This results in the visual stimulation area determined based on the user's EEG signal not being the visual stimulation area the user is currently looking at, and the target operation indicated by the EEG signal does not match the reference operation. If there are too many visual stimulation areas where the target operation and the reference operation do not match, it means that the target interface is not compatible with the user and needs to be adjusted accordingly.

[0118] Exemplarily, the terminal can prompt the user to look at a specific visual stimulation area on the target interface in the form of text or voice broadcast while displaying the target interface to the user. For example, when the target interface is a 40-target keyboard interface, the user can first be prompted to look at the visual stimulation area corresponding to the letter A, and record that the current benchmark operation is to input the letter A. After collecting the EEG signal generated when the user looks at the visual stimulation area and parsing the EEG signal, if the target operation parsed is also to input the letter A, it indicates that the benchmark operation and the target operation match; if the target operation parsed is to input the letter B, or no target operation is parsed, it indicates that the benchmark operation and the target operation do not match. By analogy, matching results corresponding to multiple visual stimulation areas can be obtained.

[0119] It should be noted that a preset number of required matching results can be set in advance, and after obtaining the preset number of matching results, the above-mentioned test process of making the user gaze at the visual stimulation area is terminated to avoid the situation where there are too many visual stimulation areas on the target interface, resulting in an excessively long test process. At the same time, preset rules can also be set in advance, and the visual stimulation area for the user to gaze at each time can be selected according to the preset rules. For example, according to the position of the visual stimulation area on the target interface, a preset number of visual stimulation areas with different positions can be selected and displayed to the user, so as to obtain the matching results of the user's visual stimulation areas at different positions with as few tests as possible. The different positions may refer to the different positions of the visual stimulation area on the target interface, for example, the visual stimulation area located on the left side of the target interface and the visual stimulation area located on the right side of the target interface are two visual stimulation areas in different positions; or it may also refer to the different numbers or arrangements of the visual stimulation areas around the visual stimulation area. For example, when the visual stimulation areas are arranged in a square array, the visual stimulation area located in the upper left corner of the array (surrounded by 3 visual stimulation areas, arranged to the right, lower right and below the visual stimulation area), and the visual stimulation area located at the left boundary of the array (surrounded by 5 visual stimulation areas, arranged above, upper right, right, lower right and below the visual stimulation area) are two visual stimulation areas in different positions. The embodiments of the present application do not specifically limit this.

[0120] Step 106, determining a total matching result corresponding to the target interface based on the matching results corresponding to each visual stimulation area;

[0121] In an embodiment of the present application, the total matching result corresponding to the target interface can be determined based on the matching results corresponding to each visual stimulation area, and the total matching result is used to characterize the overall adaptation of the user and the target interface. For example, a ratio threshold can be pre-set, and the matching results representing the matching of the target operation and the benchmark operation can be calculated as the proportion of all matching results. When the proportion is greater than the ratio threshold, the total matching result is determined as the target interface adaptation; or each visual stimulation area can be grouped according to different positions, and the matching results representing the matching of the target operation and the benchmark operation in each group can be determined as the proportion of all matching results. When the proportions corresponding to a preset number of groups are all greater than the ratio threshold, the total matching result is determined as the target interface adaptation. This embodiment of the present application does not specifically limit this.

[0122] Step 108 , when the total matching result indicates that the target interface is not compatible, the target interface is adjusted, wherein the number of visual stimulation areas of the target interface after adjustment is smaller than the number of visual stimulation areas of the target interface before adjustment.

[0123] In embodiments of the present application, if the overall matching result indicates that the target interface is not compatible, the target interface needs to be adjusted for the user to improve the user's success rate in performing tasks using the target interface. Since mismatches between the target operation and the baseline operation are generally caused by interference from other visual stimulation areas, the number of visual stimulation areas in the target interface can be reduced to reduce interference sources.

[0124] The adjusted target interface can be obtained through a pre-trained neural network. For example, the neural network can be trained based on the user's total matching results for the target interface and the brain-computer interface adapted to the user after the target interface is adjusted. Testing can be conducted in advance for multiple users to obtain the total matching results for each user for the target interface, and then the number of visual stimulation areas in the target interface can be adjusted for each user until the user's total matching results for the adjusted brain-computer interface interface are characterized, the user is adapted to the adjusted brain-computer interface interface, and the number of visual stimulation areas in the brain-computer interface at this time is recorded. The neural network can then be trained using training samples consisting of the total matching results and the number of visual stimulation areas. In the application, the number of visual stimulation areas adapted to the user can be predicted by the user's total matching results for the target interface, and the adjusted target interface can be obtained based on the number of visual stimulation areas.

[0125] Alternatively, the target interface can be adjusted through a preset strategy, and the adjusted target interface can be repeatedly displayed to the user to obtain an adjusted target interface that is ultimately adapted to the user. The preset strategy refers to a strategy for adjusting the target interface, such as how many visual stimulation areas to reduce each time the adjustment is made, and how to arrange the remaining visual stimulation areas after reducing the visual stimulation areas. For example, the preset strategy can be set to reduce 5 visual stimulation areas each time, while making the visual stimulation areas still arranged in the manner before the adjustment (such as an array arrangement before, an array arrangement after adjustment; a circular arrangement before, a circular arrangement after adjustment); or the preset strategy can be set so that the number of visual stimulation areas reduced each time is twice the number of visual stimulation areas reduced last time, and the arrangement of the visual stimulation areas is changed each time the adjustment is made. The embodiments of the present application do not make specific restrictions on this.

[0126] Exemplarily, the target interface can also be adjusted in a targeted manner based on the user's matching results for visual stimulation areas at different positions. For example, the matching success rates of the user's visual stimulation areas at different positions can be calculated separately (the matching success rate of each position is determined based on the matching results that characterize the matching of the target operation and the benchmark operation at that position, and the proportion of all matching results at that position), and when adjusting the target interface, the number of visual stimulation areas at positions where the matching success rate is lower than the ratio threshold is reduced, and the number of visual stimulation areas at positions where the matching success rate is higher than the ratio threshold is correspondingly increased. Referring to Figure 2, if the matching success rates of the visual stimulation area C at the upper right corner and the visual stimulation area B at the lower left corner are lower, and the matching success rates of the visual stimulation area A at the upper left corner and the visual stimulation area D at the lower right corner are higher, then only the visual stimulation area at the upper left corner and the visual stimulation area at the lower right corner can be retained in the adjusted target interface.

[0127] After repeating the above process several times, if the total matching result represents the adapted target interface, the testing process can be stopped and a brain-computer interface for the user can be built based on the target interface.

[0128] Step 108: construct a brain-computer interface based on the current target interface.

[0129] In the embodiments of the present application, a brain-computer interface that enables the user to perform the task currently being performed can be constructed based on a target interface adapted to the user. The currently available tasks can be displayed to the user, and the user can select the task they want to perform through buttons, text input, voice instructions, or other methods. The terminal can then construct a brain-computer interface based on the number of visual stimulation areas and target interface required for the task.

[0130] As shown in Figure 3, a typing task requiring 40 visual stimulation areas is used for illustration. After testing User 1, User 2, and User 3 in Figure 3, it was determined that Target Interface 1, adapted for User 1, has 40 visual stimulation areas; Target Interface 2, adapted for User 2, has 12 visual stimulation areas; and Target Interface 3, adapted for User 3, has 6 visual stimulation areas. Therefore, for User 1, a BCI interface tailored to the typing task can be constructed using Target Interface 1. For Users 2 and 3, the typing task needs to be broken down into several subtasks that can be completed using Target Interface 2 or Target Interface 3. For each subtask, an interface using Target Interface 2 or Target Interface 3 as a template is constructed, with each visual stimulation area on the interface assigned an operation from the subtask as the baseline operation. For User 2, for example, the subtasks could be typing the letters AE, FJ, KO, etc., as well as typing 9, a space, a comma, a period, and a delete (the number of visual stimulation areas in the final BCI interface can be less than or equal to that of the target interface). To facilitate jumping to the interface corresponding to each subtask, a general interface can be constructed based on the target interface with the goal of jumping to the interface corresponding to each subtask. For other tasks, the brain-computer interface can also be constructed by splitting subtasks. The embodiments of this application will not be repeated here.

[0131] The fairness-oriented brain-computer interface generation method provided by the embodiment of the present application, by displaying the target interface to the user and collecting the user's EEG signals for each visual stimulation area on the target interface, can parse out whether the target operation corresponding to the user's EEG signal matches the reference operation corresponding to the visual stimulation area, and then determine whether the user is compatible with the current target interface based on the matching results of each visual stimulation area; if not, the number of visual stimulation areas on the target interface is reduced, that is, the interference sources are reduced, until a target interface compatible with the user is obtained, and a brain-computer interface is constructed based on the target interface. The embodiment of the present application determines a brain-computer interface suitable for each user, so that users who are not adapted to the standard brain-computer interface can also use the brain-computer interface system normally, thereby improving the adaptability of the brain-computer interface system to different users.

[0132] In one embodiment, as shown in FIG4 , in step 108 , a brain-computer interface is constructed based on the current target interface, including:

[0133] Step 402: Obtain target operation task;

[0134] Step 404: Determine the number of target tasks corresponding to the target operation task.

[0135] Step 406: When the number of single-interface tasks in the visual stimulation area corresponding to the current target interface is less than the number of target tasks, a multi-level brain-computer interface is constructed based on the number of target tasks and the target interface.

[0136] In the embodiment of the present application, the target operation task is the task that the user needs to perform, and the number of target tasks is the number of visual stimulation areas required for the target operation task. For example, when the target operation task is digital dialing, since digital dialing requires the numbers 0-9 and two special symbols * and #, each symbol requires a visual stimulation area to input, so the number of target tasks is 12. As shown in Figure 5, when the number of target tasks is greater than or equal to the number of single-interface tasks in the visual stimulation area of ​​the target interface, constructing a brain-computer interface based on the target interface can enable the user to perform the target operation task.

[0137] When the number of target tasks is less than the number of tasks in a single interface, it is necessary to divide the target tasks into multiple task groups and construct a brain-computer interface for each task group according to the target interface, thereby forming a multi-level brain-computer interface. Referring to Figure 6, there are three exemplary implementation forms of a multi-level brain-computer interface (in fact, it can also be implemented in other ways, which are not specifically limited in this embodiment of the application).

[0138] The specific steps of constructing a multi-level brain-computer interface are illustrated by the first implementation form in the figure. In the first implementation form, the brain-computer interface interfaces at each level are connected in the form of turning pages, so it is necessary to set a visual stimulation area for going to the next interface in the first brain-computer interface interface, set a visual stimulation area for going to the previous interface in the last brain-computer interface interface, and set a visual stimulation area for going to the previous interface and a visual stimulation area for going to the next interface in the middle brain-computer interface interface. That is, when grouping the target tasks, two task groups containing a maximum of 11 target tasks and multiple task groups containing a maximum of 10 target tasks can be divided.

[0139] The target tasks can be assigned to each task group in sequence according to the arrangement order of each target task in the manner shown in Figure 6, that is, the target tasks arranged from 1 to 11 are assigned to the first task group, the target tasks arranged from 12 to 21 are assigned to the second task group, the target tasks arranged from 22 to 31 are assigned to the third task group, and the target tasks arranged from 32 to 40 are assigned to the last task group, and then the brain-computer interface corresponding to each task group is constructed according to the target interface (the last task group can contain 11 target tasks, but currently there are only 9 target tasks, so when constructing the brain-computer interface according to the task group, you can refer to the aforementioned method of constructing the brain-computer interface when the number of target tasks is greater than the number of single-interface tasks, which will not be repeated in the embodiments of this application).

[0140] Alternatively, each target task can also be grouped according to the frequency of use of each target task. For example, in the Chinese typing task, the keys that are generally used most frequently are a, e, i, n, u, comma, period, etc., so these target tasks with high usage frequency can be placed in the first brain-computer interface, so that users can input these symbols without turning pages, thereby improving the efficiency of user typing. For example, each target task can be sorted according to the frequency of use (the frequency of use can be obtained based on the data statistics of all users) to obtain a frequency queue, and then the target tasks in the frequency queue are sequentially assigned to each task group, and then the brain-computer interface corresponding to each task group is constructed according to the target interface.

[0141] It should be noted that the above are only two examples of how to group target tasks and construct brain-computer interface interfaces. In fact, target tasks can also be grouped according to other standards, and the embodiments of this application do not make specific limitations on this.

[0142] The fairness-taking brain-computer interface generation method provided in the embodiment of the present application constructs a multi-level brain-computer interface when the number of target tasks corresponding to the target operation task is greater than the number of single-interface tasks. Each brain-computer interface uses the target interface as a template, so that the user can complete target operation tasks with a large number of target tasks through the multi-level brain-computer interface.

[0143] In one embodiment, the method further includes:

[0144] When the number of the single-interface tasks is equal to the number of the target tasks, the current target interface is used as the brain-computer interface interface, and a visual stimulation area is allocated to each target task in the brain-computer interface.

[0145] In an embodiment of the present application, when the number of target tasks is equal to the number of single-interface tasks, the construction of the brain-computer interface can be completed by using each target task as the benchmark operation corresponding to each visual stimulation area, as shown in the lower left side of Figure 5.

[0146] In one embodiment, the method further includes:

[0147] When the number of tasks in the single interface is greater than the number of target tasks, target visual stimulation areas equal to the number of target tasks are selected in the current target interface, and a brain-computer interface is constructed based on each target visual stimulation area; the interval between each target visual stimulation area in the brain-computer interface is larger than the interval between each target visual stimulation area in the current target interface.

[0148] In an embodiment of the present application, when the number of target tasks is greater than the number of single-interface tasks, only the number of visual stimulation areas of the target tasks can be retained when generating a brain-computer interface based on the target interface, and the remaining visual stimulation areas can be readjusted and arranged to increase the intervals between the visual stimulation areas and further reduce the interference between the visual stimulation areas, as shown in the lower right side of Figure 5.

[0149] Alternatively, the brain-computer interface can still be generated using the target interface as a template, but only the number of visual stimulation areas of the target task in the brain-computer interface is the flickering effective visual stimulation area, as shown in the upper right corner of Figure 5. When selecting the flickering visual stimulation area, you can choose randomly; you can also choose a visual stimulation area whose position conforms to the user's usage habits. For example, when the target operation task is direction control, you can select a visual stimulation area from the top, right, bottom and left of the target interface; or you can select visual stimulation areas with a large spacing between each other to reduce interference between the visual stimulation areas. This embodiment of the application does not specifically limit this.

[0150] In one embodiment, as shown in FIG7 , in step 404 , a multi-level brain-computer interface is constructed based on the target number of tasks and the target interface, including:

[0151] Step 702: All target tasks are taken as tasks to be grouped.

[0152] Step 704: construct the number of first-level groups of single-interface tasks, and assign each task to be grouped to each first-level group.

[0153] Step 706: construct a first-level brain-computer interface based on the target interface, and allocate a visual stimulation area to each first-level group in the first-level brain-computer interface.

[0154] Step 708: For any first-level group, a second-level brain-computer interface corresponding to the first-level group is constructed according to the target interface. When the number of target tasks in the first-level group is less than or equal to the number of single-interface tasks, a visual stimulation area is allocated to each target task in the first-level group in the second-level brain-computer interface.

[0155] Among them, triggering the visual stimulation area corresponding to the first-level group in the first-level brain-computer interface interface can jump to the second-level brain-computer interface interface corresponding to the first-level group, and triggering the visual stimulation area corresponding to the target task in the second-level brain-computer interface interface can trigger the target task.

[0156] In an embodiment of the present application, brain-computer interface interfaces at various levels are connected in the form of a parent interface and a child interface, and the parent interface includes a visual stimulation area for going to each child interface, as shown in implementation method ② in Figure 6.

[0157] Each target task can be divided into a first-level grouping of the number of single-interface tasks, and based on the target interface, a first-level brain-computer interface interface can be constructed, and then each visual stimulation area in the first-level brain-computer interface interface can be respectively assigned to each first-level grouping, which is equivalent to setting the benchmark operation corresponding to each visual stimulation area to jump to the second-level brain-computer interface interface corresponding to each first-level grouping. Since the second-level brain-computer interface interface does not exist at this time, a blank brain-computer interface interface can be first constructed as the second-level brain-computer interface interface, and the benchmark operation can be set to jump to each blank brain-computer interface interface. After determining the specific content of the second-level brain-computer interface interface, the second-level brain-computer interface interface can be updated based on the determined content; or after constructing the second-level brain-computer interface interface, the benchmark operation corresponding to each visual stimulation area in the first-level brain-computer interface interface can be set. The embodiment of the present application does not make specific restrictions on this.

[0158] For any first-level grouping, a second-level brain-computer interface corresponding to the first-level grouping can be constructed based on the target interface, and when the number of target tasks in the first-level grouping is less than or equal to the number of single-interface tasks, each visual stimulation area in the second-level brain-computer interface interface is assigned to each target task, that is, the benchmark operation corresponding to the visual stimulation area is set to execute the target task. It should be noted that since the brain-computer interface may not be exactly the same as the target interface when the number of target tasks is less than the number of single-interface tasks (see the description of the aforementioned embodiment), the blank brain-computer interface can first be set as the second-level brain-computer interface corresponding to the first-level grouping. After determining the number of target tasks in the first-level grouping, the specific content in the second-level brain-computer interface interface is determined, and the second-level brain-computer interface is updated according to the determined specific content.

[0159] It should be noted that, as shown in implementation method ② in Figure 6, when assigning the tasks to be grouped to each first-level group, it is possible that there is only one target task in the first-level group. In this case, the baseline operation corresponding to the visual stimulation area of ​​the first-level group can be directly set to execute the target task in the first-level group, without the need to construct a second-level brain-computer interface for the first-level group.

[0160] The fairness-taking brain-computer interface generation method provided in the embodiment of the present application constructs a multi-level brain-computer interface by grouping target tasks, and the brain-computer interface interfaces at each level are connected in the form of parent interface and child interface, so that users can complete target operation tasks with a large number of target tasks through the multi-level brain-computer interface.

[0161] In one embodiment, as shown in FIG8 , in step 404 , a multi-level brain-computer interface is constructed based on the target number of tasks and the target interface, further comprising:

[0162] Step 802: When the number of target tasks in the first-level group is greater than the number of single-interface tasks, the target tasks in the first-level group are used as tasks to be grouped.

[0163] Step 804: construct the number of secondary groups of the single-interface tasks, assign each task to be grouped to each secondary group, and assign a visual stimulation area to each secondary group in the secondary brain-computer interface interface.

[0164] Step 806: For any secondary group, a third-level brain-computer interface corresponding to the secondary group is constructed according to the target interface. When the number of target tasks in the secondary group is less than or equal to the number of single-interface tasks, a visual stimulation area is allocated to each target task in the secondary group in the third-level brain-computer interface.

[0165] Among them, triggering the visual stimulation area corresponding to the group in the secondary brain-computer interface interface can jump to the third-level brain-computer interface corresponding to the secondary group, and triggering the visual stimulation area corresponding to the target task in the third-level brain-computer interface interface can trigger the target task.

[0166] In an embodiment of the present application, if the number of target tasks in the first-level group is still greater than the number of single-interface tasks, the target tasks in the first-level group need to be grouped again. After constructing each secondary group and assigning the target tasks to each secondary group, the benchmark operations corresponding to each visual stimulation area in the secondary brain-computer interface can be set to jump to the third-level brain-computer interface corresponding to each secondary group. The process can refer to the relevant description of the first-level brain-computer interface and the second-level brain-computer interface in the aforementioned embodiment, and the embodiment of the present application will not be repeated here.

[0167] It should be noted that the number of target tasks in the second-level grouping may still be greater than the number of single-interface tasks. In this case, the target tasks in the second-level grouping need to be grouped again to obtain third-level groupings, and each fourth-level brain-computer interface corresponding to each third-level grouping is constructed... and so on, until all target tasks have corresponding visual stimulation areas. That is, for any N-level grouping constructed, the N+1-level brain-computer interface corresponding to the N-level grouping is constructed based on the target interface. If the number of target tasks in the N-level grouping is less than or equal to the number of single-interface tasks, then each visual stimulation area in the N+1-level brain-computer interface is respectively assigned to each target task in the N-level grouping; if the number of target tasks is greater than the number of single-interface tasks, then the target tasks in the N-level grouping are used as tasks to be grouped, and N+1-level groups with the number of single-interface tasks are constructed, and each task to be grouped is assigned to each N+1-level grouping... Repeat the above process until all target tasks have corresponding visual stimulation areas.

[0168] To illustrate with a specific example, as shown in Figure 9, if the target tasks are respectively the tasks of inputting abcdefgh, the number of target tasks is 8. Since the number of single-interface tasks is 2, it is necessary to enter the process of building a multi-level brain-computer interface interface. First, abcdefgh is used as the task to be grouped, and the tasks to be grouped are divided into 2 groups (at this time, a first-level grouping). The number of target tasks in each group can be the same (as shown in Figure 9, that is, 4 target tasks in one group), or the number of target tasks in each group can be different, and the embodiment of the present application does not specifically limit this.

[0169] Assume that the target task in the first first-level group is abcd, and the target task in the second first-level group is efgh. After obtaining the target tasks in each group, a first-level brain-computer interface interface can be constructed based on the target interface, in which the benchmark operations corresponding to the two visual stimulation areas are respectively jumping to the second-level brain-computer interface interface of the first first-level group and the second-level brain-computer interface interface of the second first-level group. Two blank brain-computer interface interfaces can be pre-constructed as the second-level brain-computer interface interfaces of the first and second groups, so that the benchmark operation can be set to jump to these two blank brain-computer interface interfaces. When the first-level brain-computer interface interface is constructed, the setting of the benchmark operation is completed.

[0170] Since the number of target tasks in the first and second level groups is greater than 2, it is necessary to group the target tasks in the first and second level groups again. For the first level group, abcd can be used as tasks to be grouped, and the tasks to be grouped can be divided into 2 groups (secondary groups at this time), for example, ab group and cd group. After determining the grouping, the previously pre-built blank brain-computer interface interface can be updated to mark which secondary group each visual stimulation area corresponds to. Similar operations are performed on the second level group to complete the update of the secondary brain-computer interface.

[0171] For the secondary grouping group ab, since the number of target tasks in group ab is 2, which is equal to the number of single-interface tasks, there is no need to group group ab again. Instead, refer to the method in the aforementioned embodiment to construct a three-level brain-computer interface interface corresponding to group ab, and assign the two visual stimulation areas in the three-level brain-computer interface interface to target task a and target task b respectively. Similar operations are performed for other groups to obtain four third-level brain-computer interface interfaces, completing the construction of the multi-level brain-computer interface interface. If the user needs to input the letter a through the multi-level brain-computer interface, he can jump to the second-level brain-computer interface corresponding to abcd by gazing at the visual stimulation area marked with ab in the first-level brain-computer interface interface, and then jump to the third-level brain-computer interface corresponding to ab by gazing at the visual stimulation area marked with ab, and complete the input of a by gazing at the visual stimulation area marked with a.

[0172] It should be noted that, in addition to the first-level BCI interface, a visual stimulation area can be set in other levels of BCI interfaces for returning to the previous level. After the first-level BCI interface is constructed, the number of single-interface tasks can be reduced by 1 to reserve the above-mentioned visual stimulation area in the BCI interface.

[0173] The fairness-taking brain-computer interface generation method provided in the embodiment of the present application constructs a multi-level brain-computer interface by grouping target tasks, and the brain-computer interface interfaces at each level are connected in the form of parent interface and child interface, so that users can complete target operation tasks with a large number of target tasks through the multi-level brain-computer interface.

[0174] In one embodiment, as shown in FIG10 , in step 704 , allocating each to-be-grouped task to each first-level group includes:

[0175] Step 1002: Determine the usage frequency of each task to be grouped.

[0176] Step 1004 : Divide the single-interface tasks into frequency intervals according to the usage frequency of each task to be grouped.

[0177] Step 1006: Determine the first-level groups corresponding to each frequency interval.

[0178] Step 1008 : For any task to be grouped, determine a target frequency interval corresponding to the task to be grouped based on the usage frequency of the task to be grouped, and assign the task to be grouped to a first-level group corresponding to the target frequency interval.

[0179] In an embodiment of the present application, tasks to be grouped that are used more frequently can be divided into a group, so that the user does not need to return to the top-level brain-computer interface interface all the time to continuously perform tasks to be grouped that are used more frequently, thereby improving the efficiency of the user in executing tasks to be grouped.

[0180] The tasks to be grouped can be sorted according to their usage frequencies, and frequency intervals can be divided according to the usage frequencies of the tasks to be grouped. For example, several preset frequency intervals can be set in advance. If there are tasks to be grouped whose usage frequencies are within the preset frequency intervals, the preset frequency intervals will be retained; if the final number of preset frequency intervals is greater than the number of tasks in a single interface, the preset frequency intervals will be merged to obtain the number of frequency intervals for single-interface tasks. Alternatively, the frequency intervals can be divided based on the number of tasks to be grouped corresponding to the frequency intervals, so that the number of tasks to be grouped corresponding to each frequency interval finally obtained meets the preset strategy.

[0181] The preset strategy can be pre-set by a person skilled in the art. For example, each frequency interval can contain the same number of tasks to be grouped, that is, the number of tasks to be grouped corresponding to each frequency interval is the same. Alternatively, the usage frequency corresponding to the frequency interval can be inversely proportional to the number of tasks to be grouped in the frequency interval; the usage frequency corresponding to the frequency interval can also be inversely proportional to the number of times (number of advances) required to enter the next level brain-computer interface interface when performing the tasks to be grouped belonging to the frequency interval, and then the number of tasks to be grouped in each frequency interval can be determined according to the power of the number of advances of the number of single-interface tasks. For example, the frequency interval with the highest usage frequency can correspond to entering the next level brain-computer interface interface 0 times, that is, the frequency interval can only contain 1 task to be grouped; the frequency interval with the second highest usage frequency can correspond to entering the next level brain-computer interface interface 1 time, that is, the frequency interval can contain the number of single-interface tasks to be grouped, and so on, until each task to be grouped has a corresponding frequency interval.

[0182] The following description still uses the tasks to be grouped as abcdefgh. As shown in Figure 11, by sorting the tasks to be grouped from high to low according to their usage frequency, the queue aeghbdcf can be obtained. Since the number of tasks in a single interface is 2, it is necessary to divide it into two frequency intervals. The number of advances corresponding to the first frequency interval can be 1 (corresponding to the number of tasks to be grouped is 2), and the number of advances corresponding to the second frequency interval can be 3 (corresponding to the number of tasks to be grouped is 8). In this way, under the premise that the usage frequency and the number of advances corresponding to the frequency interval are inversely proportional, the sum of the number of tasks to be grouped corresponding to each frequency interval can be greater than or equal to the actual number of tasks to be grouped.

[0183] After determining the number of tasks to be grouped corresponding to each frequency interval, the two most frequently used tasks to be grouped can be divided into the first frequency interval, and the remaining tasks to be grouped can be divided into the second frequency interval, that is, the grouping at this time is ae as one group, and bcdfgh as another group. After constructing the first-level brain-computer interface according to the two groups and the target interface, further grouping is required for the bcdfgh group. The two frequency intervals can be divided again, and the number of advances corresponding to the first frequency interval is 1 (corresponding to the number of tasks to be grouped is 2), and the number of advances corresponding to the second frequency interval is 2 (corresponding to the number of tasks to be grouped is 4), to obtain two groups, and then repeat the above process until the multi-level brain-computer interface is constructed. The final multi-level brain-computer interface can be shown in Figure 11.

[0184] The fairness-conscious brain-computer interface generation method provided in the embodiment of the present application determines the frequency intervals according to the usage frequency of each target task when grouping target tasks, so that each frequency interval corresponds to a group, so that target tasks with similar usage frequencies can be grouped into the same group. This allows users to continuously perform target tasks with higher usage frequencies without having to return to the top-level brain-computer interface, thereby improving the efficiency of users in executing target tasks.

[0185] In one embodiment, the target interface before adjustment and the target interface after adjustment meet at least one of the first condition, the second condition, and the third condition;

[0186] The first condition is that the frequency difference between the visual stimulation areas in the adjusted target interface is greater than the frequency difference between the visual stimulation areas in the target interface before adjustment;

[0187] The second condition is that the area of ​​each visual stimulation region in the target interface after adjustment is larger than the area of ​​each visual stimulation region in the target interface before adjustment;

[0188] The third condition is that the distance between each visual stimulation area in the target interface after adjustment is larger than the distance between each visual stimulation area in the target interface before adjustment.

[0189] In an embodiment of the present application, when adjusting the target interface, in addition to adjusting the number of visual stimulation areas, the frequency difference (referring to the frequency difference of the visual stimulation coding sequence corresponding to the visual stimulation area), area and spacing of each visual stimulation area can also be adjusted to further reduce the interference between the visual stimulation areas.

[0190] Referring to FIG12 , an example of adjusting the area and spacing is shown. When adjusting the area of ​​each visual stimulation area, the original shape of each visual stimulation area can be maintained (upper left side of FIG12 ), or the shape of each visual stimulation area can be adjusted (lower left side of FIG12 ). When adjusting the spacing of each visual stimulation area, the spacing can be adjusted while maintaining the original arrangement of each visual stimulation area (upper right side of FIG12 ), or the arrangement of each visual stimulation area can be adjusted while adjusting the spacing (lower right side of FIG12 ). This is not specifically limited in the embodiments of the present application.

[0191] The fairness-conscious brain-computer interface generation method provided in the embodiment of the present application can, in addition to adjusting the number of visual stimulation areas when adjusting the target interface, also make the frequency difference between the visual stimulation areas larger, the area of ​​the visual stimulation areas larger, or the spacing between the visual stimulation areas larger, thereby further reducing the interference between the visual stimulation areas and speeding up the test to obtain the target interface that matches the user.

[0192] In one embodiment, as shown in FIG13 , in step 108 , a brain-computer interface is constructed based on the current target interface, including:

[0193] Step 1302: Display the current target interface and the interface parameters corresponding to the current target interface.

[0194] Step 1304 : Determine the target interface parameters in response to the input operation on the target interface parameters.

[0195] Step 1306: Update the current target interface according to the target interface parameters, and construct a brain-computer interface based on the updated target interface.

[0196] In the embodiment of the present application, after testing and obtaining a target interface that is adapted to the user, the doctor or other operator can also manually modify the target interface. The terminal can display the target interface and the interface parameters corresponding to the target interface (which may include the frequency, area, spacing, shape, etc. of each visual stimulation area), and prompt the operator to replace the interface parameters in the target interface by inputting the target interface parameters.

[0197] Exemplarily, the operator may input target interface parameters through text or voice input instructions. The correspondence between keywords and operation instructions for modifying interface parameters may be pre-set, for example, setting "area" to correspond to an operation instruction for modifying the area of ​​each visual stimulation area, "spacing" to correspond to an operation instruction for modifying the spacing of each visual stimulation area, and the like, and parsing the instructions input by the operator through text or voice through a natural language processing method. When the keywords and the specific values ​​(target interface parameters) input by the operator are parsed, the interface parameters corresponding to the target interface are modified through the operation instructions corresponding to the keywords and the target interface parameters, and the target interface is regenerated based on the modified interface parameters, and the current target interface and the interface parameters corresponding to the target interface are continuously displayed until the operator instructs to terminate the process of adjusting the target interface.

[0198] Alternatively, an array of template target interface parameters may be pre-set, and a template target interface generated based on these template target interface parameters. The operator may drag the template target interface onto the current target interface to replace the interface parameters in the target interface with the template target interface parameters. Other methods may also be used to allow the operator to input target interface parameters, which are not specifically limited in this embodiment of the present application.

[0199] As shown in FIG14 , the above method can be implemented using the BCI interface editing system in FIG14 . The operator can also specify a target parsing algorithm and a target statistical algorithm through the BCI interface editing system, so that the EEG signals generated by the user when looking at the BCI interface can be parsed using the target parsing algorithm, and the data obtained during user testing can also be statistically analyzed using the target statistical algorithm. The BCI interface editing system can display array element components for the target interface, target parsing algorithm, and target statistical algorithm, respectively. For example, the element component for the target interface can be the template target interface for SSVEP (Steady-State Visual Evoked Potentials), or it can be the video data or image data required to implement other BCI interfaces (such as emotion induction and motor imagery). The element component for the target parsing algorithm can be an analysis algorithm for the EEG signals generated by various BCI interfaces, and the element component for the target statistical algorithm can be various mathematical statistical methods. By dragging and dropping the element components, the BCI interface, target parsing algorithm, and target statistical algorithm can be obtained. In addition, operators can also customize other content, such as whether the data obtained during the test needs to be encrypted during transmission.

[0200] The human-computer interaction interface is the triggering source for generating brain-computer data. Constructing a stable stimulation interface for visually induced brain-computer interfaces is key to ensuring data quality. Improving the interface's scalability is also a way to enhance data reusability. Therefore, it's necessary to construct a brain-computer database with online recording and offline query capabilities. Specifically, generating structured information from unstructured EEG data online, and then enabling data reuse based on structured information queries, is key to implementing a data-centric brain-computer interface. Figure 15 shows a schematic diagram of the process for storing collected user EEG data. The user's EEG data can be recorded in a data table, which also includes information such as the stimulation frequency, characters used, and the number of experimental groups. The tester responsible for testing the user (subject) can register with the BCI interface editing system to use the system. After logging in, the tester can register the subject's information so that the EEG data generated during the test, the target operation analyzed based on the EEG data, and the corresponding benchmark operation are all recorded in a structured data table. Figure 16 shows an example of a data table. The data table records the current experimental group number, the stimulation sequence within the current experimental group, the stimulation time, and the character corresponding to the visual stimulation area during the stimulation. The data table then analyzes the subject's EEG data to determine the target action performed while gazing at the stimulus source and whether the target action matches the character. This data table allows for successful database access in the system, enabling structured output of unstructured information and, in turn, storage and query of brain-computer data using structured information.

[0201] The fairness-conscious brain-computer interface generation method provided in the embodiment of the present application displays the target interface and the parameters of the target interface after obtaining the target interface, so that the operator can update the target interface by inputting the target parameters, and can allow the operator to modify the target interface.

[0202] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0203] Based on the same inventive concept, the embodiments of the present application also provide a fairness-based brain-computer interface generation device for implementing the fairness-based brain-computer interface generation method mentioned above. The implementation solution provided by this device is similar to the implementation solution described in the above method. Therefore, the specific limitations in the embodiments of one or more fairness-based brain-computer interface generation devices provided below can be found in the limitations of the fairness-based brain-computer interface generation method mentioned above, and will not be repeated here.

[0204] In one embodiment, as shown in FIG17 , a device for generating a brain-computer interface that takes fairness into consideration is provided, including: a display module 1702 , a matching module 1704 , an adjustment module 1706 , and a construction module 1708 , wherein:

[0205] A display module 1702 is configured to determine a target interface and display the target interface, wherein the target interface includes at least one visual stimulation area, and each visual stimulation area corresponds to a different reference operation;

[0206] A matching module 1704 is configured to collect an electroencephalogram (EEG) signal corresponding to any of the visual stimulation areas, determine a target operation based on the EEG signal, and match the target operation with the reference operation to obtain a matching result corresponding to the visual stimulation area.

[0207] an adjustment module 1706, configured to determine a total matching result corresponding to the target interface based on the matching results corresponding to the visual stimulation areas, and adjust the target interface if the total matching result indicates that the target interface is not compatible, wherein the number of visual stimulation areas of the target interface after the adjustment is less than the number of visual stimulation areas of the target interface before the adjustment;

[0208] Construction module 1708 is used to construct a brain-computer interface based on the current target interface.

[0209] The fairness-oriented brain-computer interface generation device provided by the embodiment of the present application can analyze whether the target operation corresponding to the user's brain-computer interface signal matches the reference operation corresponding to the visual stimulation area by displaying the target interface to the user and collecting the user's brain wave signals for each visual stimulation area on the target interface, and then determine whether the user is compatible with the current target interface based on the matching results of each visual stimulation area; if not, the number of visual stimulation areas on the target interface is reduced, that is, the interference sources are reduced, until a target interface compatible with the user is obtained, and a brain-computer interface is constructed based on the target interface. The embodiment of the present application determines the brain-computer interface suitable for each user, so that users who are not adapted to the standard brain-computer interface interface can also use the brain-computer interface system normally, thereby improving the adaptability of the brain-computer interface system to different users.

[0210] In one embodiment, the building block 1708 is further configured to:

[0211] Obtain target operation tasks;

[0212] Determining the number of target tasks corresponding to the target operation task;

[0213] When the number of single-interface tasks corresponding to the current target interface is less than the number of target tasks, a multi-level brain-computer interface is constructed based on the number of target tasks and the target interface; the number of single-interface tasks is the number of visual stimulation areas of the current target interface.

[0214] In one embodiment, the building block 1708 is further configured to:

[0215] When the number of the single-interface tasks is equal to the number of the target tasks, the current target interface is used as the brain-computer interface interface, and a visual stimulation area is allocated to each target task in the brain-computer interface.

[0216] In one embodiment, the building block 1708 is further configured to:

[0217] When the number of tasks in the single interface is greater than the number of target tasks, target visual stimulation areas equal to the number of target tasks are selected in the current target interface, and a brain-computer interface is constructed based on each target visual stimulation area; the interval between each target visual stimulation area in the brain-computer interface is larger than the interval between each target visual stimulation area in the current target interface.

[0218] In one embodiment, the building block 1708 is further configured to:

[0219] Treat all the target tasks as tasks to be grouped;

[0220] Constructing the number of first-level groups of the single-interface tasks, and assigning each of the tasks to be grouped to each of the first-level groups;

[0221] Based on the target interface, constructing a first-level brain-computer interface interface, and assigning a visual stimulation area to each of the first-level groups in the first-level brain-computer interface interface;

[0222] For any of the first-level groups, construct a second-level brain-computer interface interface corresponding to the first-level group according to the target interface, and when the number of the target tasks in the first-level group is less than or equal to the number of the single-interface tasks, allocate a visual stimulation area in the second-level brain-computer interface interface to each of the target tasks in the first-level group;

[0223] Among them, triggering the visual stimulation area corresponding to the first-level group in the first-level brain-computer interface interface can jump to the second-level brain-computer interface corresponding to the first-level group, and triggering the visual stimulation area corresponding to the target task in the second-level brain-computer interface interface can trigger the target task.

[0224] In one embodiment, the building block 1708 is further configured to:

[0225] When the number of the target tasks in the first-level group is greater than the number of the single-interface tasks, the target tasks in the first-level group are used as the tasks to be grouped;

[0226] Constructing the number of secondary groups of the single-interface tasks, allocating each of the tasks to be grouped to each of the secondary groups, and allocating a visual stimulation area to each of the secondary groups in the secondary brain-computer interface interface;

[0227] For any of the secondary groups, constructing a third-level brain-computer interface corresponding to the secondary group according to the target interface, and when the number of the target tasks in the secondary group is less than or equal to the number of the single-interface tasks, allocating a visual stimulation area to each of the target tasks in the secondary group in the third-level brain-computer interface;

[0228] Among them, triggering the visual stimulation area corresponding to the group in the secondary brain-computer interface interface can jump to the tertiary brain-computer interface corresponding to the secondary group, and triggering the visual stimulation area corresponding to the target task in the tertiary brain-computer interface interface can trigger the target task.

[0229] In one embodiment, the building block 1708 is further configured to:

[0230] Determining the usage frequency of each of the tasks to be grouped;

[0231] Dividing the single-interface tasks into frequency intervals according to the usage frequency of each task to be grouped;

[0232] Determine the first-level groups corresponding to the frequency intervals respectively;

[0233] For any of the tasks to be grouped, a target frequency interval corresponding to the task to be grouped is determined based on the usage frequency of the task to be grouped, and the task to be grouped is allocated to the first-level group corresponding to the target frequency interval.

[0234] In one embodiment, the number of tasks to be grouped corresponding to each frequency interval is the same.

[0235] In one embodiment, the target interface before adjustment and the target interface after adjustment meet at least one of the first condition, the second condition and the third condition;

[0236] The first condition is that the frequency difference between the visual stimulation areas in the target interface after adjustment is greater than the frequency difference between the visual stimulation areas in the target interface before adjustment;

[0237] The second condition is that the area of ​​each visual stimulation region in the target interface after adjustment is larger than the area of ​​each visual stimulation region in the target interface before adjustment;

[0238] The third condition is that the distance between the visual stimulation areas in the target interface after adjustment is larger than the distance between the visual stimulation areas in the target interface before adjustment.

[0239] In one embodiment, the building block 1708 is further configured to:

[0240] Display the current target interface and the interface parameters corresponding to the current target interface;

[0241] In response to an input operation on a target interface parameter, obtaining the target interface parameter;

[0242] According to the target interface parameters, the current target interface is updated, and a brain-computer interface is constructed based on the updated target interface.

[0243] In one embodiment, the interface parameters include at least one of the frequency, area, spacing, and shape of each of the visual stimulation areas.

[0244] Each module in the fairness-conscious brain-computer interface generation device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a computer device's memory in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0245] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in FIG18 . The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for generating a brain-computer interface that takes fairness into account is implemented.

[0246] Those skilled in the art will understand that the structure shown in Figure 18 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0247] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0248] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0249] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0250] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0251] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0252] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0253] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for generating a brain-computer interface that takes fairness into consideration, characterized in that: The method comprises: Determining a target interface and displaying the target interface, wherein the target interface includes at least one visual stimulation area, and each of the visual stimulation areas corresponds to a different reference operation; For any of the visual stimulation areas, collecting an electroencephalogram signal corresponding to the visual stimulation area, determining a target operation based on the electroencephalogram signal, and matching the target operation with the reference operation to obtain a matching result corresponding to the visual stimulation area; Determining a total matching result corresponding to the target interface according to the matching results corresponding to each of the visual stimulation areas; When the total matching result indicates that the target interface is not compatible, adjusting the target interface, wherein the number of visual stimulation areas of the target interface after the adjustment is less than the number of visual stimulation areas of the target interface before the adjustment; Based on the current target interface, a brain-computer interface is constructed.

2. The method according to claim 1, characterized in that The step of constructing a brain-computer interface based on the current target interface includes: Get the target operation task; Determine the number of target tasks corresponding to the target operation task; When the number of single-interface tasks corresponding to the current target interface is less than the number of target tasks, a multi-level brain-computer interface is constructed based on the number of target tasks and the target interface; the number of single-interface tasks is the number of visual stimulation areas of the current target interface.

3. The method according to claim 2, characterized in that The constructing of the brain-computer interface based on the current target interface also includes: When the number of the single-interface tasks is equal to the number of the target tasks, the current target interface is used as the brain-computer interface interface, and a visual stimulation area is allocated to each target task in the brain-computer interface.

4. The method according to claim 2, characterized in that: The constructing of the brain-computer interface based on the current target interface also includes: When the number of tasks in the single interface is greater than the number of target tasks, target visual stimulation areas equal to the number of target tasks are selected in the current target interface, and a brain-computer interface is constructed based on each of the target visual stimulation areas; the interval between each of the target visual stimulation areas in the brain-computer interface is larger than the interval between each of the target visual stimulation areas in the current target interface.

5. The method according to claim 2, characterized in that: The constructing of a multi-level brain-computer interface based on the number of target tasks and the target interface includes: Treat all the target tasks as tasks to be grouped; Constructing the number of first-level groups of the single-interface tasks, and assigning each of the tasks to be grouped to each of the first-level groups; Based on the target interface, constructing a primary brain-computer interface interface, and assigning a visual stimulation area to each of the primary groups in the primary brain-computer interface interface; For any of the first-level groups, a second-level brain-computer interface corresponding to the first-level group is constructed according to the target interface, and when the number of the target tasks in the first-level group is less than or equal to the number of the single-interface tasks, a visual stimulation area is respectively allocated in the second-level brain-computer interface to each of the target tasks in the first-level group; Among them, triggering the visual stimulation area corresponding to the first-level group in the first-level brain-computer interface interface can jump to the second-level brain-computer interface corresponding to the first-level group, and triggering the visual stimulation area corresponding to the target task in the second-level brain-computer interface interface can trigger the target task.

6. The method according to claim 5, characterized in that The constructing of a multi-level brain-computer interface based on the target number of tasks and the target interface also includes: When the number of the target tasks in the first-level group is greater than the number of the single-interface tasks, the target tasks in the first-level group are used as the tasks to be grouped; Constructing the number of secondary groups of the single-interface tasks, allocating each of the tasks to be grouped to each of the secondary groups, and allocating a visual stimulation area to each of the secondary groups in the secondary brain-computer interface interface; For any of the secondary groups, construct a third-level brain-computer interface corresponding to the secondary group according to the target interface, and when the number of the target tasks in the secondary group is less than or equal to the number of the single-interface tasks, allocate a visual stimulation area in the third-level brain-computer interface to each of the target tasks in the secondary group; Among them, triggering the visual stimulation area corresponding to the group in the secondary brain-computer interface interface can jump to the tertiary brain-computer interface corresponding to the secondary group, and triggering the visual stimulation area corresponding to the target task in the tertiary brain-computer interface interface can trigger the target task.

7. The method according to claim 5 or 6, characterized in that: The step of respectively allocating the tasks to be grouped to the first-level groups comprises: Determining the usage frequency of each of the tasks to be grouped; According to the usage frequency of each of the tasks to be grouped, dividing the single-interface tasks into frequency intervals; Determine the primary groups corresponding to the frequency intervals respectively; For any of the tasks to be grouped, based on the usage frequency of the tasks to be grouped, a target frequency interval corresponding to the tasks to be grouped is determined, and the tasks to be grouped are allocated to the first-level group corresponding to the target frequency interval.

8. The method according to claim 7, characterized in that The number of the tasks to be grouped corresponding to each of the frequency intervals is the same.

9. The method according to claim 1, characterized in that: The target interface before adjustment and the target interface after adjustment satisfy at least one of the first condition, the second condition and the third condition; The first condition is that the frequency difference between the visual stimulation areas in the target interface after adjustment is greater than the frequency difference between the visual stimulation areas in the target interface before adjustment; The second condition is that the area of ​​each visual stimulation region in the target interface after adjustment is larger than the area of ​​each visual stimulation region in the target interface before adjustment; The third condition is that the distance between the visual stimulation areas in the target interface after adjustment is larger than the distance between the visual stimulation areas in the target interface before adjustment.

10. The method according to claim 1, characterized in that The step of constructing a brain-computer interface based on the current target interface includes: Display the current target interface and the interface parameters corresponding to the current target interface; In response to an input operation on a target interface parameter, acquiring the target interface parameter; According to the target interface parameters, the current target interface is updated, and a brain-computer interface is constructed based on the updated target interface.

11. The method according to claim 10, characterized in that The interface parameters include at least one of the frequency, area, spacing, and shape of each of the visual stimulation areas.

12. A brain-computer interface generation device that takes fairness into consideration, characterized in that: The device comprises: A display module, used for determining a target interface and displaying the target interface, wherein the target interface includes at least one visual stimulation area, and each of the visual stimulation areas corresponds to a different reference operation; A matching module, for collecting an electroencephalogram signal corresponding to any of the visual stimulation areas, determining a target operation based on the electroencephalogram signal, and matching the target operation with the reference operation to obtain a matching result corresponding to the visual stimulation area; An adjustment module, used to determine a total matching result corresponding to the target interface according to the matching results corresponding to each of the visual stimulation areas, and to adjust the target interface when the total matching result indicates that the target interface is not matched, wherein the number of visual stimulation areas of the target interface after adjustment is less than the number of visual stimulation areas of the target interface before adjustment; A construction module is used to construct a brain-computer interface based on the current target interface.

13. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 11 are implemented.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

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