CN109009171A - Attention evaluation method, system and computer-readable storage medium - Google Patents

Abstract

The invention discloses an attention evaluation method. The attention assessment method is applied to an attention assessment system, the attention assessment system comprises an attention assessment terminal and an intelligent head ring, and the attention assessment method comprises the following steps: the attention evaluation terminal acquires answer data of a user when the user carries out a preset attention game, and acquires corresponding brain wave (EEG) data through the intelligent head ring; processing the answer data and the EEG data to obtain corresponding answer scores and EEG scores; and obtaining an attention point value according to the answer point value, the EEG value and a preset multivariate regression equation. The invention also discloses an attention evaluation system and a computer readable storage medium. The invention can improve the accuracy of the attention evaluation result.

Description

Attention evaluation method, system and computer-readable storage medium

technical field

The present invention relates to the technical field of attention evaluation, in particular to an attention evaluation method, system and computer-readable storage medium.

Background technique

Attention refers to the ability of people's mental activities to point and concentrate on certain things, it is the pointing and concentration of mental activities to a certain object, and it is a common psychological feature accompanied by psychological processes such as perception, memory, thinking, and imagination. . Attention can be divided into the following five categories according to the dimension of attention: selective attention, alternating attention, sustained attention, and divided attention and attention span.

Since attention has important associations and influences on many aspects of users, for example, children's attention level affects their cognitive development, therefore, many attention games are now launched to test users' attention, so as to cultivate them in a targeted manner later. and increase user attention. However, the current evaluation of attention is mainly through some related attention games for scoring, and the evaluation results are only based on the scoring rules of the game itself, and there is a problem of low accuracy of the evaluation results.

The above content is only used to assist in understanding the technical solution of the present invention, and does not mean that the above content is admitted as prior art.

Contents of the invention

The main purpose of the present invention is to provide an attention evaluation method, system and computer-readable storage medium, aiming at improving the accuracy of attention evaluation results.

In order to achieve the above object, the present invention provides a method for evaluating attention, which is applied to an evaluation system for attention. The system for evaluating attention includes an evaluation terminal for attention and an intelligent headband. The method for evaluating attention includes the following steps:

The attention evaluation terminal obtains the answer data when the user performs a preset attention game, and obtains the corresponding brain wave EEG data through the smart headband;

Processing the answer data and EEG data to obtain corresponding answer scores and EEG scores;

According to the answer score, the EEG score and the preset multivariate regression equation, the attention score is obtained.

Optionally, the general formula of the preset multivariate regression equation is: Z=aX+bY, wherein Z is the attention score, X is the answer score, Y is the EEG score, and a and b are the corresponding the optimal coefficient of .

Optionally, the preset attention games include sustained attention games and other attention games, and the other attention games include selective attention games, conversion attention games, distraction attention games and attention games. In the breadth game, the attention evaluation terminal obtains the answer data when the user performs the preset attention game, and obtains the corresponding brain wave EEG data through the smart headband, including:

The attention evaluation terminal respectively obtains the first answer data and the second answer data when the user performs the continuous attention game and other attention games, and respectively obtains the corresponding first EEG data and the second EEG data through the smart headband. EEG data;

The step of processing the answer data and EEG data to obtain corresponding answer scores and EEG scores includes:

Respectively process the first answer data, the first EEG data, the second answer data and the second EEG data to obtain the corresponding first answer score, first EEG score, second answer score and second EEG Score;

The step of obtaining the attention score value according to the described answer score, EEG score and preset multivariate regression equation includes:

According to the first answer score, the first EEG score, the second answer score, the second EEG score and the preset multivariate regression equation, the score value of the continuous attention game and the score value of other attention are obtained .

Optionally, the attention evaluation method also includes:

Obtain the first evaluation answer data and the first self-scoring when the evaluator performs the continuous attention game, and obtain the corresponding first evaluation EEG data through the smart headband;

Respectively preprocessing the first evaluation answer data and the first evaluation EEG data to obtain corresponding first scores and second scores;

performing kernel density estimation on the first score and the second score respectively to obtain corresponding first distribution curves and second distribution curves;

Obtain the assessment answer score of sustained attention according to the first score and the first distribution curve, and obtain the assessment EEG score of sustained attention according to the second score and the second distribution curve;

According to the evaluation answer score of the persistent attention, the evaluation EEG score of the persistent attention and the first self-scoring construct the first multivariate regression equation, and obtain the first multivariate regression equation by the normal equation An optimal coefficient, the first optimal coefficient is brought into the first multivariate regression equation to obtain the multivariate regression equation of sustained attention of the preset multivariate regression equation.

Optionally, the attention evaluation method also includes:

Obtain the second evaluation answer data and the second self-scoring when the evaluator performs the other attention games, and obtain the corresponding second evaluation EEG data through the smart headband;

Respectively preprocessing the second evaluation answer data and the second evaluation EEG data to obtain corresponding third scores and fourth scores;

Carrying out kernel density estimation on the third score and the fourth score respectively to obtain corresponding third distribution curves and fourth distribution curves;

According to the third score and the third distribution curve, obtain the evaluation answer score of other attention, and obtain the evaluation EEG score of other attention according to the fourth score and the fourth distribution curve;

According to the evaluation answer score of other attention, the evaluation EEG score of other attention and the second self-scoring construction second multivariate regression equation, and obtain the second maximum of the second multivariate regression equation by normal equation The optimal coefficient is to bring the second optimal coefficient into the second multivariate regression equation to obtain other attention multivariate regression equations of the preset multivariate regression equation.

Optionally, the first answer data, the first EEG data, the second answer data, and the second EEG data are respectively processed to obtain corresponding first answer scores, first EEG scores, and second answer scores. Score and the steps of the second EEG score, including:

Preprocessing the first answer data, the first EEG data, the second answer data, and the second EEG data, respectively, to obtain corresponding fifth scores, sixth scores, seventh scores, and eighth scores;

According to the fifth score and the first distribution curve, the area under the first curve corresponding to the fifth score and the first total area between the first distribution curve and the horizontal axis are obtained by integration, and the obtained The percentage value of the area under the first curve and the first total area is recorded as the first answer score;

According to the sixth score and the second distribution curve, the area under the second curve corresponding to the sixth score and the second total area between the second distribution curve and the horizontal axis are obtained by integration, and the obtained The percentage value of the area under the second curve and the second total area is recorded as the first EEG score;

According to the seventh score and the third distribution curve, the area under the third curve corresponding to the seventh score and the third total area between the third distribution curve and the horizontal axis are obtained by integration, and the obtained The percentage value of the area under the third curve and the third total area is recorded as the second answer score;

According to the eighth score and the fourth distribution curve, the area under the fourth curve corresponding to the eighth score and the fourth total area between the fourth distribution curve and the horizontal axis are obtained by integration, and the obtained The percentage value of the area under the fourth curve and the fourth total area is recorded as the second EEG score.

Optionally, according to the first answer score, the first EEG score, the second answer score, the second EEG score and a preset multivariate regression equation, the score and the score of the continuous attention game are obtained. Other attention-scoring steps include:

Obtain the score value of the continuous attention game according to the multivariate regression equation of the sustained attention in the first answer score, the first EEG score and the preset multivariate regression equation, and according to the second answer score value, the second EEG score, and the multivariate regression equation of other attention in the preset multivariate regression equation to obtain the score value of other attention.

Optionally, the first answer data and the first evaluation answer data include the maximum number of consecutive correct answers and the total number of answers, and the second answer data and the second evaluation answer data include the number of correct answers and the number of wrong answers.

In addition, in order to achieve the above object, the present invention also provides an attention evaluation system, the attention evaluation system includes an attention evaluation terminal and a smart headband, and also includes a memory, a processor and a An attention evaluation program running on the processor, when the attention evaluation program is executed by the processor, the steps of the above-mentioned attention evaluation method are realized.

In addition, in order to achieve the above object, the present invention also provides a computer-readable storage medium, on which an attention evaluation program is stored, and when the attention evaluation program is executed by a processor, the above-mentioned Steps in the Attention Assessment Method.

The present invention provides an attention evaluation method, system and computer-readable storage technology. The attention evaluation method is applied to an attention evaluation system, and the attention evaluation system includes an attention evaluation terminal and an intelligent headband. The attention evaluation terminal obtains the answer data of the user during the preset attention game, and obtains the corresponding EEG data through the smart headband; then processes the answer data and EEG data to obtain the corresponding answer score and EEG score , and finally bring the answer score and EEG score into the preset multivariate regression equation to get the attention score. The present invention uses brain-computer interface technology to obtain EEG data, combines the answer data and EEG data, processes and obtains the corresponding answer score and EEG score, and obtains the attention score, answer score and EEG score through the previous optimization Compared with the prior art, which only performs evaluation and scoring according to the scoring rules of the game itself, the present invention can improve the accuracy of the attention evaluation results.

Description of drawings

Fig. 1 is a schematic diagram of the terminal structure of the hardware operating environment involved in the solution of the embodiment of the present invention;

Fig. 2 is a schematic flow chart of the first embodiment of the attention evaluation method of the present invention;

Fig. 3 is a schematic flow chart of the second embodiment of the attention evaluation method of the present invention;

4 is a schematic flow chart of the third embodiment of the attention evaluation method of the present invention;

Fig. 5 is a schematic diagram of the first distribution curve in the embodiment of the present invention;

FIG. 6 is a schematic flowchart of a fourth embodiment of the attention evaluation method of the present invention.

The realization of the purpose of the present invention, functional characteristics and advantages will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

Detailed ways

It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

In the prior art, since attention has important associations and influences on many aspects of the user, for example, children's attention level affects their cognitive development, therefore, many attention games are now launched to test the user's attention, so that In the later stage, we will cultivate and improve the user's attention in a targeted manner. However, the current evaluation of attention is mainly through some related attention games for scoring, and the evaluation results are only based on the scoring rules of the game itself, and there is a problem of low accuracy of the evaluation results.

In order to solve the above technical problems, the present invention provides an attention evaluation method, system and computer-readable storage technology, the attention evaluation method is applied to the attention evaluation system, and the attention evaluation system includes an attention evaluation terminal and a smart headband . The attention evaluation terminal obtains the answer data of the user during the preset attention game, and obtains the corresponding EEG data through the smart headband; then processes the answer data and EEG data to obtain the corresponding answer score and EEG score , and finally bring the answer score and EEG score into the preset multivariate regression equation to get the attention score. The present invention uses brain-computer interface technology to obtain EEG data, combines the answer data and EEG data, processes and obtains the corresponding answer score and EEG score, and obtains the attention score, answer score and EEG score through the previous optimization Compared with the prior art, which only performs evaluation and scoring according to the scoring rules of the game itself, the present invention can improve the accuracy of the attention evaluation results.

Please refer to FIG. 1 . FIG. 1 is a schematic diagram of a terminal structure of a hardware operating environment involved in the solution of an embodiment of the present invention.

The terminal of the embodiment of the present invention is an attention evaluation terminal. The attention evaluation terminal can be a PC, or a mobile terminal device with a display function such as a smart phone, a tablet computer, a portable computer, etc., and the attention evaluation terminal has a built-in Preset attention game.

As shown in FIG. 1 , the terminal may include: a processor 1001 , such as a CPU, a communication bus 1002 , a user interface 1003 , a network interface 1004 , and a memory 1005 . Wherein, the communication bus 1002 is used to realize connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. Optionally, the network interface 1004 may include a standard wired interface and a wireless interface (such as a Wi-Fi interface). The memory 1005 can be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001 .

Those skilled in the art can understand that the terminal structure shown in FIG. 1 does not constitute a limitation on the terminal, and may include more or less components than those shown in the figure, or combine some components, or arrange different components.

As shown in FIG. 1 , the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an attention evaluation program.

In the terminal shown in Figure 1, the network interface 1004 is mainly used to connect to the background server and perform data communication with the background server; the user interface 1003 is mainly used to connect to the client and perform data communication with the client; and the processor 1001 can be used for Call the attention evaluation program stored in memory 1005, and perform the following operations:

The attention evaluation terminal obtains the answer data when the user performs a preset attention game, and obtains the corresponding brain wave EEG data through the smart headband;

Processing the answer data and EEG data to obtain corresponding answer scores and EEG scores;

According to the answer score, the EEG score and the preset multivariate regression equation, the attention score is obtained.

Further, the processor 1001 can call the attention evaluation program stored in the memory 1005, and also perform the following operations:

The general formula of the preset multivariate regression equation is: Z=aX+bY, wherein Z is the attention score, X is the answer score, Y is the EEG score, and a and b are the corresponding optimal coefficients respectively .

Further, the preset attention games include continuous attention games and other attention games, and the other attention games include selective attention games, conversion attention games, distraction attention games and attention span game, the processor 1001 can call the attention evaluation program stored in the memory 1005, and also perform the following operations:

The attention evaluation terminal respectively obtains the first answer data and the second answer data when the user performs the continuous attention game and other attention games, and respectively obtains the corresponding first EEG data and the second EEG data through the smart headband. EEG data;

Respectively process the first answer data, the first EEG data, the second answer data and the second EEG data to obtain the corresponding first answer score, first EEG score, second answer score and second EEG Score;

According to the first answer score, the first EEG score, the second answer score, the second EEG score and the preset multivariate regression equation, the score value of the continuous attention game and the score value of other attention are obtained .

Further, the processor 1001 can call the attention evaluation program stored in the memory 1005, and also perform the following operations:

Obtain the first evaluation answer data and the first self-scoring when the evaluator performs the continuous attention game, and obtain the corresponding first evaluation EEG data through the smart headband;

Respectively preprocessing the first evaluation answer data and the first evaluation EEG data to obtain corresponding first scores and second scores;

performing kernel density estimation on the first score and the second score respectively to obtain corresponding first distribution curves and second distribution curves;

Obtain the assessment answer score of sustained attention according to the first score and the first distribution curve, and obtain the assessment EEG score of sustained attention according to the second score and the second distribution curve;

According to the evaluation answer score of the persistent attention, the evaluation EEG score of the persistent attention and the first self-scoring construct the first multivariate regression equation, and obtain the first multivariate regression equation by the normal equation An optimal coefficient, the first optimal coefficient is brought into the first multivariate regression equation to obtain the multivariate regression equation of sustained attention of the preset multivariate regression equation.

Further, the processor 1001 can call the attention evaluation program stored in the memory 1005, and also perform the following operations:

Obtain the second evaluation answer data and the second self-scoring when the evaluator performs the other attention games, and obtain the corresponding second evaluation EEG data through the smart headband;

Respectively preprocessing the second evaluation answer data and the second evaluation EEG data to obtain corresponding third scores and fourth scores;

Carrying out kernel density estimation on the third score and the fourth score respectively to obtain corresponding third distribution curves and fourth distribution curves;

According to the third score and the third distribution curve, obtain the evaluation answer score of other attention, and obtain the evaluation EEG score of other attention according to the fourth score and the fourth distribution curve;

According to the evaluation answer score of other attention, the evaluation EEG score of other attention and the second self-scoring construction second multivariate regression equation, and obtain the second maximum of the second multivariate regression equation by normal equation The optimal coefficient is to bring the second optimal coefficient into the second multivariate regression equation to obtain other attention multivariate regression equations of the preset multivariate regression equation.

Further, the processor 1001 can call the attention evaluation program stored in the memory 1005, and also perform the following operations:

Preprocessing the first answer data, the first EEG data, the second answer data, and the second EEG data, respectively, to obtain corresponding fifth scores, sixth scores, seventh scores, and eighth scores;

According to the fifth score and the first distribution curve, the area under the first curve corresponding to the fifth score and the first total area between the first distribution curve and the horizontal axis are obtained by integration, and the obtained The percentage value of the area under the first curve and the first total area is recorded as the first answer score;

According to the sixth score and the second distribution curve, the area under the second curve corresponding to the sixth score and the second total area between the second distribution curve and the horizontal axis are obtained by integration, and the obtained The percentage value of the area under the second curve and the second total area is recorded as the first EEG score;

According to the seventh score and the third distribution curve, the area under the third curve corresponding to the seventh score and the third total area between the third distribution curve and the horizontal axis are obtained by integration, and the obtained The percentage value of the area under the third curve and the third total area is recorded as the second answer score;

According to the eighth score and the fourth distribution curve, the area under the fourth curve corresponding to the eighth score and the fourth total area between the fourth distribution curve and the horizontal axis are obtained by integration, and the obtained The percentage value of the area under the fourth curve and the fourth total area is recorded as the second EEG score.

Further, the processor 1001 can call the attention evaluation program stored in the memory 1005, and also perform the following operations:

Obtain the score value of the continuous attention game according to the multivariate regression equation of the sustained attention in the first answer score, the first EEG score and the preset multivariate regression equation, and according to the second answer score value, the second EEG score, and the multivariate regression equation of other attention in the preset multivariate regression equation to obtain the score value of other attention.

Further, the first answer data and the first evaluation answer data include the maximum number of consecutive correct answers and the total number of answers, and the second answer data and the second evaluation answer data include the number of correct answers and the number of wrong answers.

Based on the above hardware structure, an embodiment of the attention evaluation method of the present invention is proposed.

The invention provides an attention evaluation method.

Please refer to FIG. 2 . FIG. 2 is a schematic flowchart of the first embodiment of the attention evaluation method of the present invention.

In the embodiment of the present invention, the attention evaluation method is applied to an attention evaluation system, and the attention evaluation system includes an attention evaluation terminal and a smart headband. Among them, the attention evaluation terminal has built-in preset attention games for users and testers to evaluate attention. The preset attention games include continuous attention games and other attention games, and other attention games include Selective attention games, conversion attention games, distraction attention games, and attention span games. The attention evaluation terminal is used to obtain answer data and send smart headbands when users and evaluators play preset attention games. EEG data, and then processed to get the final attention score. The smart headband uses brain-computer interface technology to collect EEG (Electroencephalogram, brain wave) data of users and evaluators, and can communicate with the attention evaluation terminal to transmit EEG to the attention evaluation terminal for processing and evaluation.

The focus assessment method includes:

Step S10, the attention evaluation terminal obtains the answer data when the user plays the preset attention game, and obtains the corresponding brain wave EEG data through the smart headband;

In this embodiment, the attention evaluation terminal first obtains the user's answer data when playing the preset attention game, and obtains the corresponding EEG data through the smart headband. Among them, the answer data may include but not limited to the number of correct answers, the number of wrong answers, the maximum number of consecutive correct answers and the total number of answers, and different answer data can be obtained according to different types of preset attention games. For example, when the default attention game is a continuous attention game, the corresponding answer data can be recorded as the first answer data, which can include the maximum number of correct answers in a row and the total number of answers; the default attention game is other During the attention game, the corresponding answer data can be recorded as the second answer data, and the second answer data can include the maximum number of consecutive correct answers and the total number of answers.

Step S20, processing the answer data and EEG data to obtain corresponding answer scores and EEG scores;

Then, the answer data and the EEG data are processed to obtain corresponding answer scores and EEG scores. Specifically, due to different types of attention evaluations, the obtained answer data and EEG data may also be inconsistent, and the corresponding data processing methods are also different. For specific processing methods, reference may be made to the following embodiments, and details are not described here. Corresponding to the above embodiment, the answer score may include the first answer score and the second score, and the EEG score may include the first EEG score and the second EEG score.

Step S30, according to the answer score, EEG score and the preset multivariate regression equation, the attention score is obtained.

Finally, the attention evaluation terminal obtains the final attention score value according to the answer score, the EEG score and the preset multivariate regression equation. Among them, the preset multivariate regression equation includes the multivariate regression equation of continuous attention and the multivariate regression equation of other attention, and the multivariate regression equation of other attention includes the multivariate regression equation of selective attention, conversion Multivariate regression equations for attention, multivariate regression for distraction, and multivariate regression for attention span. The general formula of the preset multivariate regression equation is: Z=aX+bY, wherein Z is the attention score, X is the answer score, Y is the EEG score, and a and b are the corresponding optimal coefficients respectively. Put the answer score and EEG score into the preset multivariate regression equation to get the attention score.

This embodiment provides an attention evaluation method, which is applied to an attention evaluation system, and the attention evaluation system includes an attention evaluation terminal and a smart headband. The attention evaluation terminal obtains the answer data of the user during the preset attention game, and obtains the corresponding EEG data through the smart headband; then processes the answer data and EEG data to obtain the corresponding answer score and EEG score , and finally bring the answer score and EEG score into the preset multivariate regression equation to get the attention score. The present invention uses brain-computer interface technology to obtain EEG data, combines the answer data and EEG data, processes and obtains the corresponding answer score and EEG score, and obtains the attention score, answer score and EEG score through the previous optimization Compared with the prior art, which only performs evaluation and scoring according to the scoring rules of the game itself, the present invention can improve the accuracy of the attention evaluation results.

Further, please refer to FIG. 3 , which is a second embodiment of the attention evaluation method of the present invention.

Based on the first embodiment shown in Fig. 2, in view of the inconsistency of the basic attributes of sustained attention and other attention (comprising computational selective attention, conversion attention, distraction attention and attention span), therefore, When calculating the scores corresponding to each dimension of attention, the processing methods and algorithms are also different. Among them, the calculation of the four attention scores of selective attention, conversion attention, distraction attention and attention span The algorithm is the same, and the algorithm for calculating the sustained attention score is another. Correspondingly, the preset attention game includes continuous attention game and other attention games, and said other attention games include selective attention game, conversion attention game, distraction attention game and attention span game. Certainly, in a specific embodiment, the preset attention game may include 5 checkpoints, and each checkpoint corresponds to testing a kind of attention. Step S10 includes:

Step S100, the attention evaluation terminal respectively obtains the first answer data and the second answer data when the user plays the continuous attention game and other attention games, and respectively obtains the corresponding first EEG data through the smart headband and second EEG data;

In this embodiment, since the algorithms of the persistent attention score and other attention scores are inconsistent, it is necessary to obtain the game data of each corresponding game and perform corresponding processing and calculation respectively. First, the attention evaluation terminal respectively obtains the first answer data and the second answer data when the user plays the continuous attention game and other attention games, and obtains the corresponding first EEG data and second EEG data respectively through the smart headband , wherein, the first answer data includes but not limited to the maximum number of consecutive correct answers and the total number of answers, and the second answer data includes but not limited to the number of correct answers and the number of wrong answers.

At this point, step S20 includes:

Step S200, respectively process the first answer data, the first EEG data, the second answer data and the second EEG data to obtain corresponding first answer scores, first EEG scores, second answer scores and Second EEG score;

Then, the first answer data, the first EEG data, the second answer data and the second EEG data are respectively processed to obtain the corresponding first answer score, first EEG score, second answer score and second EEG Score. For a specific processing method, reference may be made to the description in the fifth embodiment below, which will not be repeated here.

At this point, step S30 includes:

Step S300, according to the first answer score, the first EEG score, the second answer score, the second EEG score and the preset multivariate regression equation, obtain the score value of the continuous attention game and other attention score value.

Finally, according to the first answer score, the first EEG score, the second answer score, the second EEG score and the preset multivariate regression equation, the score value of the continuous attention game and other attention scores are obtained . Among them, the preset multivariate regression equation is optimized in the early stage, and can refer to the description in the third and fourth embodiments below. The preset multivariate regression equation includes the multivariate regression equation of continuous attention and other attention Multivariate regression equations for force, multivariate regression equations for other attention include multivariate regression equations for selective attention, multivariate regression equations for switching attention, multivariate regression equations for distracted attention, and multivariate regression equations for attention span. Multivariate regression equation. Putting the first answer score and the first EEG score into the multivariate regression equation of sustained attention, the score value of the sustained attention game can be obtained. Similarly, the scores of other attention games can be obtained by bringing the second answer score and the second EEG score into the multivariate regression equation of other attention.

Further, please refer to FIG. 4 , which is a schematic flowchart of the third embodiment of the attention evaluation method of the present invention.

Based on the above-mentioned first embodiment and second embodiment, since it is necessary to select evaluators before evaluating users, the corresponding algorithm is optimized according to the results of the evaluators' answers. Therefore, before the step S100, the attention evaluation method also includes:

Step S410, obtaining the first evaluation answer data and the first self-scoring data when the evaluator plays the continuous attention game, and obtaining the corresponding first evaluation EEG data through the smart headband;

In this embodiment, sustained attention refers to the duration of focus on important information, and its algorithm is inconsistent with other attention score algorithms. This embodiment introduces the algorithm optimization process of sustained attention score.

In this embodiment, the attention evaluation terminal first obtains the first evaluation answer data and the first self-scoring data when the evaluator plays the continuous attention game, and obtains the corresponding first evaluation EEG data through the smart headband. Wherein, the first test and evaluation answer data includes the maximum continuous answer correct number and the total number of answers; the first self-scoring is after the tester finishes the continuous attention game, the self-scoring number to himself at the attention test and test terminal input (in Before the evaluators input, they can explain the meaning of continuous attention, so as to ensure that the evaluators understand and then conduct self-evaluation, improve the accuracy of the algorithm, and improve the accuracy of the final evaluation results).

It should be noted that in order to ensure the accuracy of the attention algorithm, there are certain requirements for the selection and number of evaluators. The selection requirements are not specifically elaborated. The number of evaluators should be within a certain range and can be selected according to the actual situation. It is determined that in this embodiment, as a preferred number of measurers, 15 evaluators can be selected for evaluation.

Step S420, respectively preprocessing the first evaluation answer data and the first evaluation EEG data to obtain corresponding first scores and second scores;

Secondly, the first evaluation answer data and the first evaluation EEG data are respectively preprocessed to obtain corresponding first scores and second scores. Concretely, by calculating the percentage value of the maximum consecutive correct answers and the total number of answers in the first test and evaluation answer data, it is the first score, for example, this continuous attention game has a total of 5 questions (that is, the total number of answers is 5), If a certain tester answers questions 3 and 4 correctly (that is, the maximum number of consecutive correct answers is 2), then the first score is 2/5*100=40. Then calculate the average concentration value corresponding to the first evaluation EEG data through the concentration algorithm, and obtain the longest continuous time t ₁ corresponding to the average concentration value based on the first evaluation EEG data and the average concentration value, And calculate the percentage value of this time _t1 and the total game time, which is the second score. Among them, the focus algorithm is obtained through multiple experiments and optimizations, and will not be disclosed here.

Step S430, performing kernel density estimation on the first score and the second score respectively to obtain corresponding first distribution curves and second distribution curves;

Again, kernel density estimation is performed on the first score and the second score respectively to obtain corresponding first distribution curves and second distribution curves. For specific implementation principles and technologies, reference may be made to the prior art, which will not be repeated here.

Step S440, according to the first score and the first distribution curve to obtain the test score of sustained attention, and according to the second score and the second distribution curve to obtain the test EEG score of sustained attention;

Then, according to the first score and the first distribution curve, the test score of sustained attention is obtained, and according to the second score and the second distribution curve, the EEG score of continuous attention is obtained. Specifically, calculate the area S11 between the curve and the horizontal axis corresponding to the left part of the first distribution curve of the first score, and the area S12 between the first distribution curve and the horizontal axis, and then calculate the percentage of the area S11 and the area S12 value, which is the test score of sustained attention. For example, in the above example, the first score is 40, the corresponding first distribution curve is shown in FIG. 5 , and S11 is the area corresponding to the shaded part in FIG. 5 . Next, calculate the area S21 between the curve and the horizontal axis corresponding to the left part of the second distribution curve of the second score, and the area S22 between the second distribution curve and the horizontal axis, and then calculate the percentage value of the area S21 and the area S22 , which is the EEG score for the evaluation of sustained attention. For the convenience of explanation, the first and second scores can be recorded as C1 and C2 respectively, and the first and second distribution curves can be recorded as f ₁ (x) and f ₂ (x) respectively. The specific formula is as follows:

Step S450, constructing a first multivariate regression equation according to the assessment answer score of sustained attention, the EEG score of sustained attention assessment and the first self-score, and obtaining the first multivariate regression through a normal equation The first optimal coefficient of the equation, the first optimal coefficient is brought into the first multivariate regression equation to obtain the multivariate regression equation of sustained attention of the preset multivariate regression equation.

Finally, construct the first multivariate regression equation according to the assessment answer score of sustained attention, the EEG score of sustained attention assessment and the first self-score, and the first multivariate regression equation can be: Z ₁ =a ₁ X ₁ +b ₁ Y ₁ , where Z ₁ represents the first self-assessment, X ₁ represents the test score of sustained attention, Y ₁ represents the test EEG score of sustained attention, and then, through the normal equation to get The first optimal coefficient of the first multivariate regression equation, the first optimal coefficient is brought into the first multivariate regression equation to obtain the multivariate regression equation of sustained attention of the preset multivariate regression equation.

For example, in the above example, since 15 assessors were selected, 15 sets of first assessment answer data, 15 sets of first self-assessment and 15 sets of first assessment EEG data were obtained. After processing, correspondingly 15 sets of Sustained attention test scores and 15 groups of sustained attention test EEG scores. Then, according to 15 groups of first self-scores, 15 groups of sustained attention test and answer scores, 15 groups of sustained attention test EEG scores and the first multivariate regression equation, find the optimal coefficient a ₁ through the normal equation and b ₁ , assuming a ₁ =0.6 and b ₁ =0.4, the formula for calculating the score of sustained attention is: Z ₁ =0.6X ₁ +0.4Y ₁ .

Please refer to FIG. 3 . FIG. 3 is a schematic flowchart of the second embodiment of the attention evaluation method of the present invention.

Based on the first embodiment shown in FIG. 2, before step S100, the attention evaluation method also includes:

Step S510, obtaining the second evaluation answer data and second self-evaluation data of the evaluator when performing the other attention games, and obtaining the corresponding second evaluation EEG data through the smart headband;

In this embodiment, because the algorithm of other attention scores is inconsistent with the algorithm of continuous attention scores, therefore, the algorithm optimization process of other attention scores is introduced in this embodiment, that is, selective attention, conversion The algorithm optimization process of the four attention scores of sexual attention, distraction and attention span.

In this embodiment, the attention evaluation terminal first obtains the second evaluation answer data and the second self-scoring data when the evaluator plays other attention games, and obtains the corresponding second evaluation EEG data through the smart headband. Wherein, the second test and evaluation answer data includes the correct number of answers and the wrong number of answers; the second self-scoring is after the tester completes other attention games, the self-scoring number (in the tester) to himself at the attention test terminal input Before inputting, you can explain the meanings that correspond to other attentions, so as to ensure that the evaluators understand and then conduct self-evaluation, improve the accuracy of the algorithm, and improve the accuracy of the final evaluation results). It should be noted that these other attention games include selective attention games, conversion attention games, distraction attention games and attention span games. There are 4 kinds of attention data corresponding to each, and the multivariate regression equations of other attention finally obtained also include 4 kinds, namely, the multivariate regression equation of selective attention, the multivariate regression equation of conversion attention, the dispersion Multivariate regression equation for attention and multivariate regression equation for attention span.

Step S520, respectively preprocessing the second evaluation answer data and the second evaluation EEG data to obtain corresponding third scores and fourth scores;

Secondly, the second evaluation answer data and the second evaluation EEG data are respectively preprocessed to obtain the corresponding third score and fourth score. Specifically, by calculating the difference between the number of correct answers minus the number of wrong answers in the answer data of the second evaluation, the difference is the third score. Then, the average concentration value corresponding to the second evaluation EEG data is calculated by the concentration algorithm, and the average concentration value is the fourth score. Among them, the focus algorithm is obtained through multiple experiments and optimizations, and will not be disclosed here.

Step S530, performing kernel density estimation on the third score and the fourth score respectively to obtain corresponding third distribution curves and fourth distribution curves;

Again, kernel density estimation is performed on the third score and the fourth score respectively to obtain corresponding third distribution curves and fourth distribution curves. For specific implementation principles and technologies, reference may be made to the prior art, which will not be repeated here.

Step S540, according to the third score and the third distribution curve to obtain other attention evaluation answer scores, and according to the fourth score and the fourth distribution curve to obtain other attention evaluation EEG scores;

Then, according to the third score and the third distribution curve, the test and answer scores of other attention are obtained, and the test and EEG scores of other attention are obtained according to the fourth score and the fourth distribution curve. Specifically, calculate the third score corresponding to the area S31 between the curve on the left side of the third distribution curve and the horizontal axis, and the area S32 between the third distribution curve and the horizontal axis, and then calculate the percentage of the area S31 and the area S32 The value is the score of other attention evaluation questions. Calculate the fourth score corresponding to the area S41 between the curve on the left side of the fourth distribution curve and the horizontal axis, and the area S42 between the third distribution curve and the horizontal axis, and then calculate the percentage value of the area S41 and the area S42, namely EEG scores for other measures of attention. For the convenience of explanation, the third and fourth scores can be denoted as C3 and C4 respectively, and the third and fourth distribution curves can be denoted as f ₃ (x) and f ₄ (x) respectively. The specific formulas are as follows:

Step S550, constructing a second multivariate regression equation according to the test and answer scores of other attention, the test and EEG scores of other attention and the second self-score, and obtaining the result of the second multivariate regression equation through the normal equation The second optimal coefficient, the second optimal coefficient is brought into the second multivariate regression equation to obtain other attention multivariate regression equations of the preset multivariate regression equation.

Finally, construct a second multivariate regression equation according to the test and answer scores of other attention, the test and EEG scores of other attention and the second self-score, and the second multivariate regression equation can be: Z ₂ =a ₂ X ₂ +b ₂ Y ₂ , where Z ₂ represents the second self-assessment, X ₂ represents the test and answer scores of other attention, Y ₂ represents the test EEG score of other attention, and then, the second most is obtained through the normal equation The second optimal coefficient of the variable regression equation, the second optimal coefficient is brought into the second multivariate regression equation to obtain other attention multivariate regression equations of the preset multivariate regression equation.

For example, as far as selective attention is concerned, in the above example, since 15 assessors are selected, 15 sets of second assessment answer data, 15 sets of second self-assessment and 15 sets of second assessment EEG data are obtained. After processing, correspondingly, 15 groups of test and answer scores of other attention and 15 groups of test EEG scores of other attention were obtained. Then, according to 15 groups of second self-scores, 15 groups of test and answer scores of other attention, 15 groups of test EEG scores of other attention and the second multivariate regression equation, find out the optimal coefficients a ₂ and b through normal equations _2. Assuming a ₂ =0.5 and b ₂ =0.7, the formula for calculating the score of selective attention is: Z ₂ =0.5X ₂ +0.7Y ₂ .

It should be noted that, steps S410-S450 in the second embodiment and steps S510-S550 in the third embodiment are performed in no particular order.

Further, based on the above-mentioned embodiments shown in FIG. 2 to FIG. 4 , a fifth embodiment of the attention evaluation method of the present invention is proposed.

Based on the above implementation manners, in this embodiment, step S200 includes:

Step S210, respectively preprocessing the first answer data, the first EEG data, the second answer data and the second EEG data to obtain the corresponding fifth score, sixth score, seventh score and eighth score Score;

In this embodiment, the attention evaluation terminal obtains the first answer data and the second answer data when the user plays the continuous attention game and other attention games respectively, and obtains the corresponding first EEG data through the smart headband respectively. After the data and the second EEG data, the first answer data, the first EEG data, the second answer data and the second EEG data are preprocessed respectively to obtain the corresponding fifth score, sixth score and seventh score and the eighth score. Specifically, calculate the percentage value of the maximum number of consecutive correct answers in the first answer data and the total number of answers, which is the fifth score; calculate the difference between the number of correct answers in the second test and evaluation answer data minus the number of wrong answers, that is It is the sixth score; the average concentration value corresponding to the first EEG data is calculated by the concentration algorithm, and the longest continuous time corresponding to the average concentration value is obtained according to the first EEG data and the average concentration value, And calculate the percentage value of this time and the total game time, which is the seventh score; calculate the average concentration value corresponding to the second EEG data through the focus algorithm, which is the eighth score.

Step S220, obtaining the first area under the curve corresponding to the fifth score and the first total area between the first distribution curve and the horizontal axis through integration according to the fifth score and the first distribution curve, And record the percentage value of the area under the first curve and the first total area as the first answer score;

Step S230, obtaining the second area under the curve corresponding to the sixth score and the second total area between the second distribution curve and the horizontal axis through integration according to the sixth score and the second distribution curve, And the percentage value of the area under the second curve and the second total area is recorded as the first EEG score;

Step S240, obtaining the third area under the curve corresponding to the seventh score and the third total area between the third distribution curve and the horizontal axis through integration according to the seventh score and the third distribution curve, And record the percentage value of the area under the third curve and the third total area as the second answer score;

Step S250, obtaining the fourth area under the curve corresponding to the eighth score and the fourth total area between the fourth distribution curve and the horizontal axis through integration according to the eighth score and the fourth distribution curve, And record the percentage value of the fourth area under the curve to the fourth total area as the second EEG score.

Then, according to the fifth score and the first distribution curve, the area under the first curve corresponding to the fifth score and the first total area between the first distribution curve and the horizontal axis are obtained through integration, and the first total area under the first curve The percentage value of the area and the first total area is recorded as the first answer score. Wherein, the _first distribution curve is obtained during the algorithm optimization process. For the convenience of explanation, the fifth score is recorded as C5. The area under the first curve corresponding to the fifth score is denoted as S13, and the first total area between the first distribution curve and the horizontal axis is S12 in the above-mentioned embodiment, then:

Similarly, according to the sixth score and the second distribution curve, the area under the second curve corresponding to the sixth score and the second total area between the second distribution curve and the horizontal axis are obtained through integration, and the second The percentage value of the area under the curve and the second total area is recorded as the first EEG score; according to the seventh score and the third distribution curve, the third area under the curve corresponding to the seventh score and the third distribution are obtained by integration The third total area between the curve and the horizontal axis, and the percentage value of the area under the third curve and the third total area is recorded as the second answer score; according to the eighth score and the fourth distribution curve, it is obtained by integrating with this The area under the fourth curve corresponding to the eighth score and the fourth total area between the fourth distribution curve and the horizontal axis, and the percentage value of the area under the fourth curve and the fourth total area is recorded as the second EEG score . For a specific processing method, reference may be made to what is described in the foregoing implementation manners, and details are not repeated here.

It should be noted that the execution of each step in step S220 to step S250 is not in any order.

At this point, step S300 may also include:

Step S310, according to the first answer score, the first EEG score and the multivariate regression equation of sustained attention in the preset multivariate regression equation to obtain the score value of the continuous attention game, and according to the first The multivariate regression equation of the second answer score, the second EEG score and other attention in the preset multivariate regression equation obtains the score value of other attention.

In this embodiment, according to the first answer score and the first EEG score into the multivariate regression equation of sustained attention in the preset multivariate regression equation, the score value of the sustained attention game can be obtained . Similarly, the scores of other attention games can be obtained by bringing the second answer score and the second EEG score into the multivariate regression equation of other attention in the preset multivariate regression equation.

The present invention also provides an attention evaluation system, which includes an attention evaluation terminal and an intelligent headband, and also includes a memory, a processor, and an attention system stored on the memory and operable on the processor. A force evaluation program, when the attention evaluation program is executed by the processor, the steps of the attention evaluation method described in any one of the above embodiments are realized.

The specific embodiments of the attention evaluation system of the present invention are basically the same as the above embodiments of the attention evaluation method, and will not be repeated here.

The present invention also provides a computer-readable storage medium, on which an attention evaluation program is stored, and when the attention evaluation program is executed by a processor, the attention as described in any one of the above embodiments is realized. Steps in the assessment method.

The specific embodiments of the computer-readable storage medium of the present invention are basically the same as the embodiments of the above-mentioned attention evaluation method, and will not be repeated here.

It should be noted that, as used herein, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or system comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or system. Without further limitations, an element defined by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article or system comprising that element.

The serial numbers of the above embodiments of the present invention are for description only, and do not represent the advantages and disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product in essence or the part that contributes to the prior art, and the computer software product is stored in a storage medium (such as ROM/RAM) , magnetic disk, optical disk), including several instructions to make a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) execute the method described in each embodiment of the present invention.

The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the description of the present invention and the contents of the accompanying drawings, or directly or indirectly used in other related technical fields , are all included in the scope of patent protection of the present invention in the same way.

Claims (10)

1. a kind of attention assessment method, which is characterized in that be applied to attention evaluation system, the attention evaluation system packet Include attention assessment terminal and intelligent headring, the attention assessment method the following steps are included:

The attention assessment terminal obtains answer data when user carries out default attention game, and passes through the intelligent head Ring obtains corresponding brain wave EEG data；

The answer data and EEG data are handled, corresponding answer score value and EEG score value are obtained；

According to the answer score value, EEG score value and default multivariate regression equation, the power that gains attention fractional value.

2. attention assessment method as described in claim 1, which is characterized in that the general formula of the default multivariate regression equation Are as follows: Z=aX+bY, wherein Z is attention score value, and X is answer score value, and Y is EEG score value, and a, b are respectively corresponding most major clique Number.

3. attention assessment method as described in claim 1, which is characterized in that the default attention game includes duration Attention game and other attention game, other described attention game include that selective attention game, transformational pay attention to Power game, dispersed attention game and attention range game, the attention assessment terminal obtain user and carry out default note Answer data when meaning power game, and the step of corresponding brain wave EEG data is obtained by the intelligent headring, comprising:

Attention assessment terminal obtains the when user carries out the game of Sustained attention power and other attention game respectively One answer data and the second answer data, and corresponding first EEG data and the 2nd EEG are obtained by the intelligent headring respectively Data；

It is described that the answer data and EEG data are handled, the step of obtaining corresponding answer score value and EEG score value, packet It includes:

The first answer data, the first EEG data, the second answer data and the second EEG data are handled respectively, obtained Corresponding first answer score value, the first EEG score value, the second answer score value and the 2nd EEG score value；

It is described according to the answer score value, EEG score value and default multivariate regression equation, gain attention power fractional value the step of, Include:

According to the first answer score value, the first EEG score value, the second answer score value, the 2nd EEG score value and default multivariate regression Equation obtains the fractional value of Sustained attention power game and the fractional value of other attentions.

4. attention assessment method as claimed in claim 3, which is characterized in that the attention assessment method further include:

It obtains the first assessment answer data and first when assessment person carries out the Sustained attention power game to score certainly, and passes through The intelligence headring obtains corresponding first assessment EEG data；

Respectively to it is described first assessment answer data and first assessment EEG data pre-process, obtain corresponding first score value, Second score value；

Density Estimator is carried out to first score value and the second score value respectively, obtains corresponding first distribution curve and second point Cloth curve；

The assessment answer score value of Sustained attention power is obtained according to first score value and the first distribution curve, and according to described Two score values and the second distribution curve obtain the assessment EEG score value of Sustained attention power；

It is scored certainly according to the assessment answer score value of the Sustained attention power, the assessment EEG score value of Sustained attention power and first The first multivariate regression equation is constructed, and the first most major clique of the first multivariate regression equation is obtained by normal equation Number, brings first optimal coefficient into the first multivariate regression equation to obtain the default multivariate regression equation The multivariate regression equation of Sustained attention power.

5. attention assessment method as claimed in claim 4, which is characterized in that the attention assessment method further include:

It obtains the second assessment answer data and second when the assessment person carries out other described attention game to score certainly, and leads to It crosses the intelligent headring and obtains corresponding second assessment EEG data；

The second assessment answer data and the second assessment EEG data are pre-processed respectively, obtain corresponding third score value With quartile；

Density Estimator is carried out to the third score value and quartile respectively, obtains corresponding third distribution curve and the 4th point Cloth curve；

The assessment answer score value of other attentions is obtained according to the third score value and third distribution curve, and according to the described 4th Score value and the 4th distribution curve obtain the assessment EEG score value of other attentions；

It is constructed according to the assessment EEG score value and second of the assessment answer score value of other attentions, other attentions from scoring Second multivariate regression equation, and the second optimal coefficient of the second multivariate regression equation is obtained by normal equation, it will Second optimal coefficient bring the second multivariate regression equation into obtain the default multivariate regression equation other The multivariate regression equation of attention.

6. attention assessment method as claimed in claim 5, which is characterized in that it is described respectively to the first answer data, First EEG data, the second answer data and the second EEG data are handled, and corresponding first answer score value, the first EEG are obtained The step of score value, the second answer score value and the 2nd EEG score value, comprising:

The first answer data, the first EEG data, the second answer data and the second EEG data are pre-processed respectively, obtained To corresponding quintile, the 6th score value, the 7th score value and the 8th score value；

It is obtained under the first curve corresponding with the quintile according to the quintile and the first distribution curve by integral First gross area between area and first distribution curve and horizontal axis, and by first area under the curve and first total face Long-pending percent value is denoted as the first answer score value；

It is obtained under the second curve corresponding with the 6th score value according to the 6th score value and the second distribution curve by integral Second gross area between area and second distribution curve and horizontal axis, and by second area under the curve and second total face Long-pending percent value is denoted as the first EEG score value；

It is obtained under third curve corresponding with the 7th score value according to the 7th score value and third distribution curve by integral The third gross area between area and the third distribution curve and horizontal axis, and by the third area under the curve and the total face of third Long-pending percent value is denoted as the second answer score value；

It is obtained under the 4th curve corresponding with the 8th score value according to the 8th score value and the 4th distribution curve by integral The 4th gross area between area and the 4th distribution curve and horizontal axis, and by the 4th area under the curve and the 4th total face Long-pending percent value is denoted as the 2nd EEG score value.

7. attention assessment method as claimed in claim 5, which is characterized in that described according to the first answer score value, One EEG score value, the second answer score value, the 2nd EEG score value and default multivariate regression equation obtain point of continuous property attention game The step of numerical value and the fractional value of other attentions, comprising:

According to the more of the Sustained attention power in the first answer score value, the first EEG score value and default multivariate regression equation Variable regression equation obtains the fractional value of continuous property attention game, and according to the second answer score value, the 2nd EEG score value and pre- If the multivariate regression equation of other attentions in multivariate regression equation obtains the fractional value of other attentions.

8. attention assessment method as claimed in claim 7, which is characterized in that the first answer data and the first assessment are answered Inscribing data includes the maximum continuous positive exact figures of answer and answer sum, and the second answer data and the second assessment answer data include The positive exact figures of answer and answer error number.

9. a kind of attention evaluation system, which is characterized in that the attention evaluation system includes attention assessment terminal and intelligence Can headring, further include memory, processor and be stored in the attention that can be run on the memory and on the processor Assessment program, the attention assessment program are realized when being executed by the processor as described in any item of the claim 1 to 8 The step of attention assessment method.

10. a kind of computer readable storage medium, which is characterized in that be stored with attention on the computer readable storage medium Assessment program, the attention assessment program realize such as attention described in any item of the claim 1 to 8 when being executed by processor The step of power assessment method.