Brain-computer interface testing method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a brain-computer interface testing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a current index value of each performance index in real time in a process of performing a function test on a brain-computer interface; based on the current index value, the first relative weight of each performance index relative to the corresponding performance, the second relative weight of each performance relative to each running state, the third relative weight of each running state relative to each tested function, and the fourth relative weight of each tested function relative to the brain-computer interface, the first relative weight of each performance index relative to the corresponding performance, the second relative weight of each performance relative to each running state, and the third relative weight of each tested function relative to the brain-computer interface are calculated; and obtaining an evaluation value corresponding to the brain-computer interface, and testing the brain-computer interface based on the evaluation value. According to the method provided by the invention, the brain-computer interface can be comprehensively tested from different dimensions, so that the test accuracy can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to a brain-computer interface testing method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] In response to the national call, the brain-computer interface (BCI) was applied to smart cars, namely brain-controlled vehicles (BCV). Since the brain-computer interface serves as an information exchange channel for the driver to control the vehicle, its performance plays a key role in the interaction of brain-controlled vehicles. Therefore, the testing and evaluation of the performance of the vehicle brain-computer interface system is the key to accelerating the development, application and deployment of brain-controlled vehicles. However, the existing testing methods often only test some functions of the vehicle brain-computer interface, resulting in low accuracy in testing the vehicle brain-computer interface. Summary of the invention

[0003] Based on this, it is necessary to provide a brain-computer interface testing method, device, computer equipment, computer-readable storage medium and computer program product that can improve the test accuracy in response to the above-mentioned technical problems.

[0004] In a first aspect, the present application provides a brain-computer interface testing method, the method comprising:

[0005] For a brain-computer interface of a brain-controlled vehicle, multiple measured functions of the brain-computer interface, multiple performances of the measured functions, multiple performance indicators of the performances, and multiple operating states of the brain-controlled vehicle are obtained, and a first relative weight of each performance indicator relative to the corresponding performance, a second relative weight of each performance relative to each operating state, a third relative weight of each operating state relative to each measured function, and a fourth relative weight of each measured function relative to the brain-computer interface is obtained;

[0006] In the process of functional testing the brain-computer interface, the current indicator value of each performance indicator is obtained in real time;

[0007] Based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, an evaluation value corresponding to the brain-computer interface is obtained, and the brain-computer interface is tested based on the evaluation value.

[0008] In one embodiment, obtaining a first relative weight of each performance indicator relative to the corresponding performance includes:

[0009] For each indicator combination formed by two performance indicators, based on the difference in importance of the two performance indicators in the indicator combination to the performance, determine the importance scale value corresponding to the indicator combination according to the first preset value assignment rule;

[0010] Constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix;

[0011] For the eigenvector corresponding to the maximum eigenvalue, a first relative weight of each performance indicator relative to the performance is determined based on vector elements in the eigenvector.

[0012] In one embodiment, obtaining a second relative weight of each performance relative to each operating state includes:

[0013] For each performance combination formed by two performances, when the brain-controlled vehicle is in each operating state, based on the difference in importance of the two performances in the performance combination in the evaluation process of the tested function, determine the importance scale value corresponding to the performance combination according to the second preset value assignment rule;

[0014] Constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix;

[0015] For the eigenvector corresponding to the maximum eigenvalue, a second relative weight of each performance with respect to each operating state is determined based on vector elements in the eigenvector.

[0016] In one embodiment, obtaining a third relative weight of each operating state relative to each tested function includes:

[0017] For each state combination formed by two operating states, based on the difference in importance between the two operating states in the state combination in the evaluation process of the tested function, determine the importance scale value corresponding to the state combination according to a third preset value assignment rule;

[0018] Constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix;

[0019] For the eigenvector corresponding to the maximum eigenvalue, a third relative weight of each operating state with respect to each measured function is determined based on vector elements in the eigenvector.

[0020] In one embodiment, obtaining a fourth relative weight of each tested function relative to the brain-computer interface includes:

[0021] For each function combination formed by two tested functions, based on the difference in importance of the two tested functions in the function combination to the brain-computer interface, determine the importance scale value corresponding to the function combination according to a fourth preset value assignment rule;

[0022] Constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix;

[0023] For the eigenvector corresponding to the maximum eigenvalue, a fourth relative weight of each measured function relative to the brain-computer interface is determined based on vector elements in the eigenvector.

[0024] In one embodiment, obtaining the evaluation value corresponding to the brain-computer interface based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight includes:

[0025] Based on the current indicator value and the first relative weight, obtaining an evaluation value corresponding to each performance;

[0026] Based on the performance evaluation value and the second relative weight, obtaining an evaluation value corresponding to each operating state;

[0027] Based on the evaluation value of the operating state and the third relative weight, obtaining an evaluation value corresponding to each tested function;

[0028] Based on the evaluation value of the measured function and the fourth relative weight, a corresponding evaluation value of the brain-computer interface is obtained.

[0029] In a second aspect, the present application also provides a brain-computer interface testing device, the device comprising:

[0030] A first acquisition module is used to acquire, for a brain-computer interface of a brain-controlled vehicle, a plurality of tested functions of the brain-computer interface, a plurality of performances of the tested functions, a plurality of performance indicators of the performances, and a plurality of operating states of the brain-controlled vehicle, and acquire a first relative weight of each performance indicator relative to the corresponding performance, a second relative weight of each performance relative to each operating state, a third relative weight of each operating state relative to each tested function, and a fourth relative weight of each tested function relative to the brain-computer interface;

[0031] A second acquisition module is used to obtain the current indicator value of each performance indicator in real time during the functional test of the brain-computer interface;

[0032] The third acquisition module is used to obtain the corresponding evaluation value of the brain-computer interface based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, and test the brain-computer interface based on the evaluation value.

[0033] In a third aspect, the present application further provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method in any one of the above embodiments when executing the computer program.

[0034] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method in any one of the above embodiments are implemented.

[0035] In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the method in any one of the above embodiments are implemented.

[0036] The above-mentioned brain-computer interface test method, device, computer equipment, computer-readable storage medium and computer program product, for the brain-computer interface of the brain-controlled vehicle, obtains multiple tested functions of the brain-computer interface, multiple performances of the tested functions, multiple performance indicators of the performance, and multiple operating states of the brain-controlled vehicle, and obtains the first relative weight of each performance indicator relative to the corresponding performance, the second relative weight of each performance relative to each operating state, the third relative weight of each operating state relative to each tested function, and the fourth relative weight of each tested function relative to the brain-computer interface; in the process of functional testing of the brain-computer interface, the current index value of each performance indicator is obtained in real time; based on the current index value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, the corresponding evaluation value of the brain-computer interface is obtained, and the brain-computer interface is tested based on the evaluation value. The method provided by the present application can comprehensively test the brain-computer interface from different dimensions, thereby effectively improving the test accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0038] FIG1 is a schematic diagram of a flow chart of a brain-computer interface testing method in one embodiment;

[0039] FIG2 is a schematic flow chart of a first relative weight acquisition method in one embodiment;

[0040] FIG3 is an overall structural diagram of a brain-computer interface testing method in another embodiment;

[0041] FIG4 is a structural diagram of an evaluation result layer, a function layer, and a scenario layer in another embodiment;

[0042] FIG5 is a structural diagram of a dynamic scene performance dimension layer and an indicator layer in another embodiment;

[0043] FIG6 is a structural diagram of a static scene performance dimension layer and an indicator layer in another embodiment;

[0044] FIG7 is a structural block diagram of a brain-computer interface testing device in one embodiment;

[0045] FIG. 8 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] In one embodiment, as shown in FIG1 , a brain-computer interface testing method is provided. This embodiment is illustrated by applying the method to a terminal. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0048] S102. For the brain-computer interface of a brain-controlled vehicle, obtain multiple tested functions of the brain-computer interface, multiple performances of the tested functions, multiple performance indicators of the performances, and multiple operating states of the brain-controlled vehicle, and obtain a first relative weight of each performance indicator relative to the corresponding performance, a second relative weight of each performance relative to each operating state, a third relative weight of each operating state relative to each tested function, and a fourth relative weight of each tested function relative to the brain-computer interface.

[0049] Among them, a brain-controlled vehicle is a vehicle directly controlled by brain waves. The brain-controlled vehicle uses a brain-computer interface to convert the driver's brain wave signals into vehicle control instructions, thereby realizing the operation of the vehicle without traditional physical operations; the brain-computer interface is an interface that directly connects the human brain and external devices. It can capture and interpret brain activities, and then convert these activities into executable signals for controlling external devices. In this embodiment, the brain-computer interface is used to connect the driver's brain and the brain-controlled vehicle; optionally, the functions to be tested include but are not limited to data acquisition functions, signal processing functions, intent decoding functions and device control functions; the various performances of the functions to be tested include but are not limited to safety, anti-interference, accuracy, timeliness and comfort; performance indicators are used to evaluate the corresponding performance. For example, the performance indicators of anti-interference may include anti-vibration ability, anti-noise ability and anti-emotional interference ability; multiple operating states include different states of the brain-controlled vehicle during driving and different states of the brain-controlled vehicle when it is stationary.

[0050] S104. During the functional test of the brain-computer interface, the current indicator value of each performance indicator is obtained in real time.

[0051] Optionally, the current indicator value of each performance indicator may be determined based on the assignment rule in Table 1.

[0052] Table 1

[0053]

[0054] In addition to the assignment rules in Table 1, the assignment rules for the current indicator value also include: when the test result of the performance indicator is between very good and average, the current indicator value of the performance indicator is determined to be 2; when the test result of the performance indicator is between average and very poor, the current indicator value of the performance indicator is determined to be 4.

[0055] S106. Based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, obtain the corresponding evaluation value of the brain-computer interface, and test the brain-computer interface based on the evaluation value.

[0056] Optionally, based on the current indicator value and the first relative weight, obtain the corresponding evaluation value of each performance; based on the evaluation value of the performance and the second relative weight, obtain the corresponding evaluation value of each operating state; based on the evaluation value of the operating state and the third relative weight, obtain the corresponding evaluation value of each measured function; based on the evaluation value of the measured function and the fourth relative weight, obtain the corresponding evaluation value of the brain-computer interface.

[0057] In the above-mentioned brain-computer interface test method, for the brain-computer interface of the brain-controlled vehicle, multiple tested functions of the brain-computer interface, multiple performances of the tested functions, multiple performance indicators of the performances, and multiple operating states of the brain-controlled vehicle are obtained, and the first relative weight of each performance indicator relative to the corresponding performance, the second relative weight of each performance relative to each operating state, the third relative weight of each operating state relative to each tested function, and the fourth relative weight of each tested function relative to the brain-computer interface are obtained; in the process of functional testing of the brain-computer interface, the current index value of each performance index is obtained in real time; based on the current index value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, the corresponding evaluation value of the brain-computer interface is obtained, and the brain-computer interface is tested based on the evaluation value. The method provided by the present application can comprehensively test the brain-computer interface from different dimensions, thereby effectively improving the test accuracy.

[0058] In some embodiments, as shown in FIG2 , obtaining a first relative weight of each performance indicator relative to the corresponding performance includes:

[0059] S202: for each indicator combination formed by two performance indicators, based on the difference in importance of the two performance indicators in the indicator combination to the performance, determine the importance scale value corresponding to the indicator combination according to the first preset value assignment rule.

[0060] S204: construct a matrix with importance scale values ​​as matrix elements, and obtain the maximum eigenvalue of the matrix.

[0061] S206 . For the eigenvector corresponding to the maximum eigenvalue, determine a first relative weight of each performance indicator relative to the performance based on vector elements in the eigenvector.

[0062] The importance scale value is used to evaluate the difference in importance of two performance indicators in the indicator combination to the corresponding performance.

[0063] Optionally, the characteristic vector corresponding to security is a={a1, a2, a3, …, a i}, where a i is the performance index corresponding to security, i is the number of performance indexes corresponding to security; the eigenvector corresponding to anti-interference is k={k1, k2, k3, …, k j}, where k j is the performance index corresponding to anti-interference, j is the number of performance indexes corresponding to anti-interference; the eigenvector corresponding to accuracy is z={z1, z2, z3, …, z v}, where z v is the performance index corresponding to accuracy, v is the number of performance indexes corresponding to accuracy; the eigenvector corresponding to timeliness is t={t1, t2, t3, …, tp}, where t p is the performance index corresponding to timeliness, p is the number of performance indexes corresponding to timeliness; the eigenvector corresponding to comfort is s={s1, s2, s3, …, s q}, where s q is the performance index corresponding to comfort, and q is the number of performance indexes corresponding to comfort.

[0064] In this embodiment, the first relative weight corresponding to each performance indicator is determined based on the importance scale value between every two performance indicators, so that the determined first relative weight is more accurate, thereby making the evaluation result of the brain-computer interface more accurate.

[0065] In some embodiments, the second relative weight of each performance relative to each operating state is obtained, including: for each performance combination formed by two performances, when the brain-controlled vehicle is in each operating state, based on the difference in importance of the two performances in the performance combination in the evaluation process of the measured function, according to a second preset assignment rule, determining the importance scale value corresponding to the performance combination; constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; for the eigenvector corresponding to the maximum eigenvalue, determining the second relative weight of each performance relative to each operating state based on the vector elements in the eigenvector.

[0066] The importance scale value is used to evaluate the difference in importance between two performances in the performance combination in the evaluation process of the measured function.

[0067] Optionally, the corresponding eigenvector for each operating state is G={g a , g k , g z , g t , g s}, where g a , g k , g z , g t , g s They are the second relative weights of safety, anti-interference, accuracy, timeliness and comfort respectively.

[0068] In this embodiment, the second relative weight corresponding to each performance is determined based on the importance scale value between every two performances, so that the determined second relative weight is more accurate, thereby making the evaluation result of the brain-computer interface more accurate.

[0069] In some embodiments, obtaining a third relative weight of each operating state relative to each tested function includes: for each state combination formed by two operating states, based on the difference in importance of the two operating states in the state combination in the evaluation process of the tested function, determining the importance scale value corresponding to the state combination according to a third preset assignment rule; constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; for the eigenvector corresponding to the maximum eigenvalue, determining the third relative weight of each operating state relative to each tested function based on the vector elements in the eigenvector.

[0070] The importance scale value is used to evaluate the difference in importance between two operating states in the state combination in the evaluation process of the measured function.

[0071] Optionally, the multiple operating states include m motion states and n static states, and the corresponding characteristic vector of each measured function is C={C1, C2, ..., C m , C m+1 , ..., C m+n}, where C1, C2, ..., C m , C m+1 , ..., C m+n The third relative weights are respectively corresponding to each motion state or static state.

[0072] In this embodiment, the third relative weight corresponding to each operating state is determined based on the importance scale value between every two operating states, so that the determined third relative weight is more accurate, thereby making the evaluation result of the brain-computer interface more accurate.

[0073] In some embodiments, obtaining a fourth relative weight of each tested function relative to the brain-computer interface includes: for each function combination formed by two tested functions, based on the difference in importance of the two tested functions in the function combination to the brain-computer interface, according to a fourth preset assignment rule, determining the importance scale value corresponding to the function combination; constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; for the eigenvector corresponding to the maximum eigenvalue, determining the fourth relative weight of each tested function relative to the brain-computer interface based on the vector elements in the eigenvector.

[0074] Among them, the importance scale value is used to evaluate the difference in the importance of the two tested functions in the function combination to the brain-computer interface; the corresponding importance scale values ​​of the function combination composed of different tested functions are shown in Table 2:

[0075] Table 2

[0076]

[0077] In Table 2, wij is the importance scale value, N is the total number of functions tested, and w ij The assignment rules are shown in Table 3:

[0078] Table 3

[0079]

[0080] In addition to the assignment rules in Table 3, the fourth preset assignment rule also includes: when the importance of one function is between equally important and slightly important than that of another function, the corresponding w of the two functions are assigned ij Assign a value of 2. When one feature is slightly more important than another feature and the importance is between slightly more important and significantly more important, the corresponding w of the two features is ij Assign a value of 4. When the importance of one feature is between obviously important and strongly important than another feature, the corresponding w of the two features is ij Assign a value of 6. When one feature is more important than another feature, the corresponding w of the two features is ij Assign the value to 8.

[0081] Based on the contents in Table 2 and Table 3, the final matrix with importance scale values ​​as matrix elements is as follows:

[0082]

[0083] The largest eigenvalue of the matrix is ​​denoted by λ max , and for λ max The eigenvector of is normalized and the normalized eigenvector is recorded as W={W D1 , W D2 , ..., W DN}, where W D1 , W D2 , ..., W DN are the fourth weight vectors corresponding to each tested function.

[0084] In this embodiment, the fourth weight vector corresponding to each measured function is determined based on the importance scale value between every two measured functions, so that the determined fourth weight vector is more accurate, thereby making the evaluation result of the brain-computer interface more accurate.

[0085] In some embodiments, based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, the corresponding evaluation value of the brain-computer interface is obtained, including: based on the current indicator value and the first relative weight, obtaining the corresponding evaluation value of each performance; based on the evaluation value of the performance and the second relative weight, obtaining the corresponding evaluation value of each operating state; based on the evaluation value of the operating state and the third relative weight, obtaining the corresponding evaluation value of each measured function; based on the evaluation value of the measured function and the fourth relative weight, obtaining the corresponding evaluation value of the brain-computer interface.

[0086] Among them, the corresponding evaluation value of the brain-computer interface can be calculated based on the following formula:

[0087] G=A*a T *g a + K*k T *g k + Z*z T *g z + T*t T *g t + S*s T *g s

[0088] U=G1*C1+G2*C2+ ... +G m *C m +G m+1 *C m+1 + ... + G m+n * C m+n

[0089] M=U1*W D1 +U2*W D2 + ... +U N *W DN

[0090] Where a, k, z, t, s are the corresponding feature vectors of safety, anti-interference, accuracy, timeliness and comfort, A, K, Z, T, S are the current index values ​​of each performance index, G1, G2, ..., G m , G m+1 , ..., G m+n are the corresponding evaluation values ​​for each operating state, U1, U2, ..., U N are the corresponding evaluation values ​​of each tested function, and M is the corresponding evaluation value of the brain-computer interface.

[0091] In this embodiment, based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, the corresponding evaluation value of the brain-computer interface is obtained, so that the obtained evaluation value is more accurate.

[0092] In one embodiment, another brain-computer interface testing method is provided, and the overall structure diagram of the method is shown in FIG3. In this embodiment, a hierarchical structure framework of the evaluation method is established, and the overall system structure is divided into 5 layers, the first layer is the evaluation result layer, the second layer is the function layer, the third layer is the scenario layer, the fourth layer is the performance dimension layer, and the fifth layer is the indicator layer. Among them, the first layer is the evaluation result finally obtained by the target evaluation method to be constructed for the brain-computer interface function to be evaluated; the second layer mainly includes the various functions of the brain-computer interface, and the comprehensive evaluation of the brain-computer interface is realized by evaluating the various functions of the brain-computer interface; the third layer is the scene required for evaluating the brain-computer interface function, and the scene can be roughly divided into two categories: dynamic scenes and static scenes. The dynamic scene is used to evaluate the brain-computer interface function under the condition of the vehicle moving scene, and the static scene is used to evaluate the brain-computer interface function under the condition of the vehicle stationary scene; the fourth layer is the 5 theoretical dimensions of the brain-computer interface function evaluation under the corresponding scene, and the brain-computer interface function is evaluated from the dimensions of safety, anti-interference, accuracy, timeliness, and comfort, among which, safety The safety dimension mainly defines the assessment of the security risks introduced by the brain-computer interface function, the anti-interference dimension mainly defines the assessment of the stability of the brain-computer interface function under various interferences, the accuracy dimension mainly defines the assessment of the accuracy of the brain-computer interface function in the acquisition and execution of instructions, the timeliness dimension mainly defines the assessment of the speed and efficiency of the brain-computer interface function in the acquisition and execution of instructions, and the comfort dimension mainly defines the assessment of the mental and physical comfort brought to people by the brain-computer interface function during the implementation process; the fifth layer is the basic elements that constitute the performance dimension. The indicator layer converts the performance dimension into multiple specific indicators, which are directly aimed at the needs of evaluating the brain-computer interface function. Taking anti-interference as an example, the indicators it includes include vibration resistance, noise resistance, and emotional interference resistance.

[0093] This embodiment is aimed at the interactive performance evaluation of the brain-computer interface with speed control function and music playback control function and the vehicle and environment. The evaluation method to be constructed can be described as: selecting evaluation scenarios for the two functions of the brain-computer interface respectively, evaluating the speed control function in two different dynamic scenarios, and evaluating the music playback control function in a dynamic scene and a static scene. The evaluation result layer, function layer, and scene layer structure of the evaluation method are shown in Figure 4. The structure of the performance dimension layer and indicator layer of the dynamic scene and the specific indicators of the indicator layer are shown in Figure 5, and the structure of the performance dimension layer and indicator layer of the static scene and the specific indicators of the indicator layer are shown in Figure 6.

[0094] The weights of each level are obtained using the interactive performance evaluation result calculation method of this embodiment.

[0095] Figure 4 shows the structure of the evaluation result layer, function layer, and scenario layer of the example evaluation method. D1 , W D2 are the relative weights of the speed control function and the music playback control function, C1 and C2 are the relative weights of dynamic scene 1 and dynamic scene 2, C3 and C4 are the relative weights of dynamic scene 3 and static scene 1.

[0096] Figure 5 shows the performance dimension layer and indicator layer of the dynamic scene in the example evaluation method. 1a , g 1k , g 1z , g 1t , g 1s Indicates the relative weights of the five performance dimensions of safety, anti-interference, accuracy, timeliness, and comfort. 11 、a 12 、a 13 are the relative weights of the three indicators under the security dimension, A 11 , A 12 , A 13 are the index values ​​corresponding to these three indicators respectively. 11 , k 12 , k 13 are the relative weights of the three indicators under the anti-interference dimension, K 11 , K 12 , K 13 are the index values ​​corresponding to these three indicators respectively. 11 、z 12 are the relative weights of the two indicators under the accuracy dimension, Z 11 , Z 12 are the index values ​​corresponding to these two indicators respectively. 11 ,t 12 ,t 13 are the relative weights of the three indicators under the timeliness dimension, T 11 、T 12 、T 13 are the index values ​​corresponding to these three indicators respectively. 11 、s 12 、s 13 are the relative weights of the three indicators under the comfort dimension, S 11 , S 12 , S 13 The first number 1 in the parameter subscript of scene 1 is replaced by 2 and 3 to represent the corresponding parameters of dynamic scene 2 and dynamic scene 3.

[0097] Figure 6 shows the performance dimension layer and indicator layer of the static scene in the example evaluation method. 4a , g 4k , g 4z , g 4t , g 4s Indicates the relative weights of the five performance dimensions of safety, anti-interference, accuracy, timeliness, and comfort. 41 、a 42 are the relative weights of the two indicators under the security dimension, A 41 , A 42 are the index values ​​corresponding to these two indicators respectively. 41 , k 42 are the relative weights of the two indicators under the anti-interference dimension, K 41 , K 42 are the index values ​​corresponding to these two indicators respectively. 41 、z 42 are the relative weights of the two indicators under the accuracy dimension, Z 41 , Z 42 are the index values ​​corresponding to these two indicators respectively. 41 ,t 42 ,t 43 are the relative weights of the three indicators under the timeliness dimension, T 41 、T 42 、T 43 are the index values ​​corresponding to these three indicators respectively. 41 、s 42 、s 43 、s 44 are the relative weights of the four indicators under the comfort dimension, S 41 , S 42 , S 43 , S 44 are the indicator values ​​corresponding to these four indicators respectively.

[0098] The final calculated interactive performance evaluation result is shown in the following formula:

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] M is the final evaluation result of the interaction performance of the brain-computer interface.

[0106] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed 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 part of the steps or stages in other steps.

[0107] Based on the same inventive concept, the embodiment of the present application also provides a brain-computer interface test device for implementing the above-mentioned brain-computer interface test method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more brain-computer interface test device embodiments provided below can refer to the limitations of the brain-computer interface test method above, and will not be repeated here.

[0108] In an exemplary embodiment, as shown in FIG7 , a brain-computer interface testing device 700 is provided, comprising: a first acquisition module 701 , a second acquisition module 702 and a third acquisition module 703 , wherein:

[0109] The first acquisition module 701 is used to obtain, for the brain-computer interface of a brain-controlled vehicle, multiple measured functions of the brain-computer interface, multiple performances of the measured functions, multiple performance indicators of the performances, and multiple operating states of the brain-controlled vehicle, and obtain a first relative weight of each performance indicator relative to the corresponding performance, a second relative weight of each performance relative to each operating state, a third relative weight of each operating state relative to each measured function, and a fourth relative weight of each measured function relative to the brain-computer interface.

[0110] The second acquisition module 702 is used to obtain the current indicator value of each performance indicator in real time during the functional test of the brain-computer interface.

[0111] The third acquisition module 703 is used to obtain the corresponding evaluation value of the brain-computer interface based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, and test the brain-computer interface based on the evaluation value.

[0112] In some embodiments, the first acquisition module 701 is also used to determine, for each indicator combination formed by two performance indicators, the importance scale value corresponding to the indicator combination according to a first preset assignment rule, based on the difference in importance of the two performance indicators in the indicator combination to the performance; construct a matrix with the importance scale values ​​as matrix elements, and obtain the maximum eigenvalue of the matrix; and for the eigenvector corresponding to the maximum eigenvalue, determine the first relative weight of each performance indicator relative to the performance based on the vector elements in the eigenvector.

[0113] In some embodiments, the first acquisition module 701 is also used to determine, for a performance combination formed by every two performances, the importance scale value corresponding to the performance combination according to a second preset assignment rule, based on the difference in importance of the two performances in the performance combination in the evaluation process of the measured function when the brain-controlled vehicle is in each operating state; construct a matrix with the importance scale values ​​as matrix elements, and obtain the maximum eigenvalue of the matrix; and for the eigenvector corresponding to the maximum eigenvalue, determine the second relative weight of each performance relative to each operating state based on the vector elements in the eigenvector.

[0114] In some embodiments, the first acquisition module 701 is also used to determine, for each state combination formed by two operating states, the importance scale value corresponding to the state combination according to a third preset assignment rule, based on the difference in importance of the two operating states in the state combination in the evaluation process of the measured function; construct a matrix with the importance scale values ​​as matrix elements, and obtain the maximum eigenvalue of the matrix; and for the eigenvector corresponding to the maximum eigenvalue, determine the third relative weight of each operating state relative to each measured function based on the vector elements in the eigenvector.

[0115] In some embodiments, the first acquisition module 701 is also used to determine, for each function combination formed by two tested functions, the importance scale value of the function combination according to a fourth preset assignment rule, based on the difference in importance of the two tested functions in the function combination to the brain-computer interface; construct a matrix with the importance scale values ​​as matrix elements, and obtain the maximum eigenvalue of the matrix; and for the eigenvector corresponding to the maximum eigenvalue, determine the fourth relative weight of each tested function relative to the brain-computer interface based on the vector elements in the eigenvector.

[0116] The third acquisition module 703 is also used to obtain the corresponding evaluation value of each performance based on the current indicator value and the first relative weight; to obtain the corresponding evaluation value of each operating state based on the evaluation value of the performance and the second relative weight; to obtain the corresponding evaluation value of each measured function based on the evaluation value of the operating state and the third relative weight; and to obtain the corresponding evaluation value of the brain-computer interface based on the evaluation value of the measured function and the fourth relative weight.

[0117] Each module in the above-mentioned brain-computer interface test device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0118] In an exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG8. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. 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 input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, a near field communication (NFC) or other technologies. When the computer program is executed by the processor, a brain-computer interface testing method is implemented.

[0119] Those skilled in the art will understand that the structure shown in FIG. 8 is merely a block diagram of a partial 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 arrangement of components.

[0120] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: for a brain-computer interface of a brain-controlled vehicle, multiple tested functions of the brain-computer interface, multiple performances of the tested functions, multiple performance indicators of the performances, and multiple operating states of the brain-controlled vehicle are obtained, and a first relative weight of each performance indicator relative to the corresponding performance, a second relative weight of each performance relative to each operating state, a third relative weight of each operating state relative to each tested function, and a fourth relative weight of each tested function relative to the brain-computer interface are obtained; in the process of functional testing the brain-computer interface, the current indicator value of each performance indicator is obtained in real time; based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, the corresponding evaluation value of the brain-computer interface is obtained, and the brain-computer interface is tested based on the evaluation value.

[0121] In one embodiment, the method of obtaining the first relative weight of each performance indicator relative to the corresponding performance implemented when the processor executes a computer program includes: for each indicator combination formed by two performance indicators, based on the difference in importance of two performance indicators in the indicator combination to the performance, according to a first preset assignment rule, determining the importance scale value corresponding to the indicator combination; constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; for the eigenvector corresponding to the maximum eigenvalue, determining the first relative weight of each performance indicator relative to the performance based on the vector elements in the eigenvector.

[0122] In one embodiment, the second relative weight of each performance relative to each operating state is obtained when the processor executes a computer program, including: for each performance combination formed by two performances, when the brain-controlled vehicle is in each operating state, based on the difference in importance of the two performances in the performance combination in the evaluation process of the tested function, according to a second preset assignment rule, determining the importance scale value corresponding to the performance combination; constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; for the eigenvector corresponding to the maximum eigenvalue, determining the second relative weight of each performance relative to each operating state based on the vector elements in the eigenvector.

[0123] In one embodiment, the method of obtaining the third relative weight of each operating state relative to each tested function implemented when the processor executes a computer program includes: for each state combination formed by two operating states, based on the difference in importance of the two operating states in the state combination in the evaluation process of the tested function, according to a third preset assignment rule, determining the importance scale value corresponding to the state combination; constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; for the eigenvector corresponding to the maximum eigenvalue, determining the third relative weight of each operating state relative to each tested function based on the vector elements in the eigenvector.

[0124] In one embodiment, the method of obtaining the fourth relative weight of each tested function relative to the brain-computer interface implemented by the processor when executing a computer program includes: for each function combination formed by two tested functions, based on the difference in importance of the two tested functions in the function combination to the brain-computer interface, according to a fourth preset assignment rule, determining the importance scale value corresponding to the function combination; constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; for the eigenvector corresponding to the maximum eigenvalue, determining the fourth relative weight of each tested function relative to the brain-computer interface based on the vector elements in the eigenvector.

[0125] In one embodiment, the processor executes a computer program to obtain an evaluation value corresponding to the brain-computer interface based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, including: obtaining an evaluation value corresponding to each performance based on the current indicator value and the first relative weight; obtaining an evaluation value corresponding to each operating state based on the evaluation value of the performance and the second relative weight; obtaining an evaluation value corresponding to each measured function based on the evaluation value of the operating state and the third relative weight; obtaining an evaluation value corresponding to the brain-computer interface based on the evaluation value of the measured function and the fourth relative weight.

[0126] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: for a brain-computer interface of a brain-controlled vehicle, a plurality of tested functions of the brain-computer interface, a plurality of performances of the tested functions, a plurality of performance indicators of the performances, and a plurality of operating states of the brain-controlled vehicle are obtained, and a first relative weight of each performance indicator relative to the corresponding performance, a second relative weight of each performance relative to each operating state, a third relative weight of each operating state relative to each tested function, and a fourth relative weight of each tested function relative to the brain-computer interface are obtained; in the process of functional testing the brain-computer interface, a current indicator value of each performance indicator is obtained in real time; based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, a corresponding evaluation value of the brain-computer interface is obtained, and the brain-computer interface is tested based on the evaluation value.

[0127] In one embodiment, the computer program implemented by the processor when executed obtains the first relative weight of each performance indicator relative to the corresponding performance, including: for each indicator combination formed by two performance indicators, based on the difference in importance of two performance indicators in the indicator combination to the performance, according to a first preset assignment rule, determining the importance scale value corresponding to the indicator combination; constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; for the eigenvector corresponding to the maximum eigenvalue, determining the first relative weight of each performance indicator relative to the performance based on the vector elements in the eigenvector.

[0128] In one embodiment, the computer program implemented by the processor when executed obtains the second relative weight of each performance relative to each operating state, including: for each performance combination formed by two performances, when the brain-controlled vehicle is in each operating state, based on the difference in importance of the two performances in the performance combination in the evaluation process of the tested function, according to a second preset assignment rule, determining the importance scale value corresponding to the performance combination; constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; for the eigenvector corresponding to the maximum eigenvalue, determining the second relative weight of each performance relative to each operating state based on the vector elements in the eigenvector.

[0129] In one embodiment, the computer program implemented when executed by the processor to obtain the third relative weight of each operating state relative to each tested function includes: for each state combination formed by two operating states, based on the difference in importance of the two operating states in the state combination in the evaluation process of the tested function, according to a third preset assignment rule, determining the importance scale value corresponding to the state combination; constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; for the eigenvector corresponding to the maximum eigenvalue, determining the third relative weight of each operating state relative to each tested function based on the vector elements in the eigenvector.

[0130] In one embodiment, the method of obtaining the fourth relative weight of each tested function relative to the brain-computer interface when the computer program is executed by the processor includes: for each function combination formed by two tested functions, based on the difference in importance of the two tested functions in the function combination to the brain-computer interface, according to a fourth preset assignment rule, determining the importance scale value corresponding to the function combination; constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; for the eigenvector corresponding to the maximum eigenvalue, determining the fourth relative weight of each tested function relative to the brain-computer interface based on the vector elements in the eigenvector.

[0131] In one embodiment, the computer program implemented when executed by the processor obtains the corresponding evaluation value of the brain-computer interface based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, including: obtaining the corresponding evaluation value of each performance based on the current indicator value and the first relative weight; obtaining the corresponding evaluation value of each operating state based on the evaluation value of the performance and the second relative weight; obtaining the corresponding evaluation value of each measured function based on the evaluation value of the operating state and the third relative weight; obtaining the corresponding evaluation value of the brain-computer interface based on the evaluation value of the measured function and the fourth relative weight.

[0132] In one embodiment, a computer program product is provided, comprising a computer program, which implements the following steps when executed by a processor: for a brain-computer interface of a brain-controlled vehicle, obtaining multiple tested functions of the brain-computer interface, multiple performances of the tested functions, multiple performance indicators of the performances, and multiple operating states of the brain-controlled vehicle, and obtaining a first relative weight of each performance indicator relative to the corresponding performance, a second relative weight of each performance relative to each operating state, a third relative weight of each operating state relative to each tested function, and a fourth relative weight of each tested function relative to the brain-computer interface; in the process of functional testing the brain-computer interface, obtaining the current indicator value of each performance indicator in real time; based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, obtaining the corresponding evaluation value of the brain-computer interface, and testing the brain-computer interface based on the evaluation value.

[0133] In one embodiment, the computer program implemented by the processor when executed obtains the first relative weight of each performance indicator relative to the corresponding performance, including: for each indicator combination formed by two performance indicators, based on the difference in importance of two performance indicators in the indicator combination to the performance, according to a first preset assignment rule, determining the importance scale value corresponding to the indicator combination; constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; for the eigenvector corresponding to the maximum eigenvalue, determining the first relative weight of each performance indicator relative to the performance based on the vector elements in the eigenvector.

[0134] In one embodiment, the computer program implemented by the processor when executed obtains the second relative weight of each performance relative to each operating state, including: for each performance combination formed by two performances, when the brain-controlled vehicle is in each operating state, based on the difference in importance of the two performances in the performance combination in the evaluation process of the tested function, according to a second preset assignment rule, determining the importance scale value corresponding to the performance combination; constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; for the eigenvector corresponding to the maximum eigenvalue, determining the second relative weight of each performance relative to each operating state based on the vector elements in the eigenvector.

[0135] In one embodiment, the computer program implemented when executed by the processor to obtain the third relative weight of each operating state relative to each tested function includes: for each state combination formed by two operating states, based on the difference in importance of the two operating states in the state combination in the evaluation process of the tested function, according to a third preset assignment rule, determining the importance scale value corresponding to the state combination; constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; for the eigenvector corresponding to the maximum eigenvalue, determining the third relative weight of each operating state relative to each tested function based on the vector elements in the eigenvector.

[0136] In one embodiment, the method of obtaining the fourth relative weight of each tested function relative to the brain-computer interface when the computer program is executed by the processor includes: for each function combination formed by two tested functions, based on the difference in importance of the two tested functions in the function combination to the brain-computer interface, according to a fourth preset assignment rule, determining the importance scale value corresponding to the function combination; constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; for the eigenvector corresponding to the maximum eigenvalue, determining the fourth relative weight of each tested function relative to the brain-computer interface based on the vector elements in the eigenvector.

[0137] In one embodiment, the computer program implemented when executed by the processor obtains the corresponding evaluation value of the brain-computer interface based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, including: obtaining the corresponding evaluation value of each performance based on the current indicator value and the first relative weight; obtaining the corresponding evaluation value of each operating state based on the evaluation value of the performance and the second relative weight; obtaining the corresponding evaluation value of each measured function based on the evaluation value of the operating state and the third relative weight; obtaining the corresponding evaluation value of the brain-computer interface based on the evaluation value of the measured function and the fourth relative weight.

[0138] 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, and the collection, use and processing of relevant data must comply with relevant regulations.

[0139] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can 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 can include random access memory (RAM) or external cache memory, etc. As an 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 each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0140] The technical features of the above embodiments may 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 application.

[0141] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A brain-computer interface testing method, characterized in that: The method comprises: For a brain-computer interface of a brain-controlled vehicle, multiple measured functions of the brain-computer interface, multiple performances of the measured functions, multiple performance indicators of the performances, and multiple operating states of the brain-controlled vehicle are obtained, and a first relative weight of each performance indicator relative to the corresponding performance, a second relative weight of each performance relative to each operating state, a third relative weight of each operating state relative to each measured function, and a fourth relative weight of each measured function relative to the brain-computer interface is obtained; In the process of performing functional testing on the brain-computer interface, obtaining the current indicator value of each performance indicator in real time; Based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, an evaluation value corresponding to the brain-computer interface is obtained, and the brain-computer interface is tested based on the evaluation value.

2. The method according to claim 1, characterized in that The obtaining of a first relative weight of each performance indicator relative to the corresponding performance includes: For each indicator combination formed by two performance indicators, based on the difference in importance of the two performance indicators in the indicator combination to the performance, determine the importance scale value corresponding to the indicator combination according to the first preset value assignment rule; Constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; For the eigenvector corresponding to the maximum eigenvalue, a first relative weight of each performance indicator relative to the performance is determined based on vector elements in the eigenvector.

3. The method according to claim 1, characterized in that The obtaining of a second relative weight of each performance relative to each operating state comprises: For each performance combination formed by two performances, when the brain-controlled vehicle is in each operating state, based on the difference in importance of the two performances in the performance combination in the evaluation process of the tested function, determine the importance scale value corresponding to the performance combination according to the second preset value assignment rule; Constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; For the eigenvector corresponding to the maximum eigenvalue, a second relative weight of each performance with respect to each operating state is determined based on vector elements in the eigenvector.

4. The method according to claim 1, characterized in that: The obtaining of a third relative weight of each operating state relative to each tested function comprises: For each state combination formed by two operating states, based on the difference in importance between the two operating states in the state combination in the evaluation process of the tested function, determine the importance scale value corresponding to the state combination according to a third preset value assignment rule; Constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; For the eigenvector corresponding to the maximum eigenvalue, a third relative weight of each operating state with respect to each measured function is determined based on vector elements in the eigenvector.

5. The method according to claim 1, characterized in that: The obtaining of a fourth relative weight of each tested function relative to the brain-computer interface comprises: For each functional combination formed by two tested functions, based on the difference in importance of the two tested functions in the functional combination to the brain-computer interface, determine the importance scale value corresponding to the functional combination according to a fourth preset value assignment rule; Constructing a matrix with the importance scale values ​​as matrix elements, and obtaining the maximum eigenvalue of the matrix; For the eigenvector corresponding to the maximum eigenvalue, a fourth relative weight of each measured function relative to the brain-computer interface is determined based on vector elements in the eigenvector.

6. The method according to claim 1, characterized in that The obtaining, based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, a corresponding evaluation value of the brain-computer interface comprises: Based on the current indicator value and the first relative weight, obtaining an evaluation value corresponding to each performance; Based on the performance evaluation value and the second relative weight, obtaining an evaluation value corresponding to each operating state; Based on the evaluation value of the operating state and the third relative weight, obtaining an evaluation value corresponding to each tested function; Based on the evaluation value of the measured function and the fourth relative weight, a corresponding evaluation value of the brain-computer interface is obtained.

7. A brain-computer interface testing device, characterized in that: The device comprises: A first acquisition module is used to acquire, for a brain-computer interface of a brain-controlled vehicle, a plurality of tested functions of the brain-computer interface, a plurality of performances of the tested functions, a plurality of performance indicators of the performances, and a plurality of operating states of the brain-controlled vehicle, and acquire a first relative weight of each performance indicator relative to the corresponding performance, a second relative weight of each performance relative to each operating state, a third relative weight of each operating state relative to each tested function, and a fourth relative weight of each tested function relative to the brain-computer interface; A second acquisition module is used to obtain the current indicator value of each performance indicator in real time during the functional test of the brain-computer interface; The third acquisition module is used to obtain the corresponding evaluation value of the brain-computer interface based on the current indicator value, the first relative weight, the second relative weight, the third relative weight and the fourth relative weight, and test the brain-computer interface based on the evaluation value.

8. 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 6 are implemented.

9. 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 6 are implemented.

10. 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 6 are implemented.

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