CN109009171B - Attention assessment method, attention assessment system and computer-readable storage medium - Google Patents

Attention assessment method, attention assessment system and computer-readable storage medium Download PDF

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CN109009171B
CN109009171B CN201810868482.0A CN201810868482A CN109009171B CN 109009171 B CN109009171 B CN 109009171B CN 201810868482 A CN201810868482 A CN 201810868482A CN 109009171 B CN109009171 B CN 109009171B
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attention
answer
eeg
data
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CN109009171A (en
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韩璧丞
杨钊祎
苗仁恺
魏昕
邹思睿
杨锦陈
张媛
宗长松
张雷
倪晋
贺欢
程翼
林真
侯阅悦
徐艺泉
范晶晶
蒋戎
周子惠
海伦娜·李
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Shenzhen Xinliu Technology Co ltd
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Priority to PCT/CN2019/089042 priority patent/WO2020024688A1/en
Priority to PCT/US2019/054057 priority patent/WO2020037332A2/en
Priority to BR112021001717-8A priority patent/BR112021001717A2/en
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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 assessment method, attention assessment system and computer-readable storage medium
Technical Field
The invention relates to the technical field of attention assessment, in particular to an attention assessment method, an attention assessment system and a computer readable storage medium.
Background
Attention refers to the ability of a person to direct and concentrate on something, and is a common psychological characteristic of psychological processes accompanied by sensory perception, memory, thinking, imagination, etc. the mental activities direct and concentrate on a certain object. Attention can be classified into the following five categories according to the attention dimension: selective attention (selective attention), translational attention (alternation attention), sustained attention (sustained attention), distractive attention (differentiated attention), and attention breadth (attention width).
Since attention has important relevance and influence on many aspects of the user, for example, the level of attention of children affects their cognitive development, attention games are now being introduced to test the attention of users in order to specifically foster and boost their attention at a later stage. However, currently, the assessment of attention is mainly scored through some related attention games, and the assessment result is only based on the scoring rules of the game itself, so that the problem of low accuracy of the assessment result exists.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide an attention assessment method, an attention assessment system and a computer readable storage medium, aiming at improving the accuracy of an attention assessment result.
In order to achieve the above object, the present invention provides an attention assessment method applied to an attention assessment system, where the attention assessment system includes an attention assessment terminal and an intelligent headband, and the attention assessment method includes 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.
Optionally, the preset multivariate regression equation has a general formula: and Z is aX + bY, wherein Z is the attention score, X is the answer score, Y is the EEG score, and a and b are respectively corresponding optimal coefficients.
Optionally, the preset attention game includes a continuous attention game and other attention games, the other attention games include a selective attention game, a convertible attention game, a dispersive attention game and an attention span game, and the step of acquiring, by the attention evaluation terminal, answer data of the user in the preset attention game and acquiring corresponding brain wave EEG data through the smart headring includes:
the attention evaluation terminal respectively acquires first answer data and second answer data when a user carries out a continuous attention game and other attention games, and respectively acquires corresponding first EEG data and second EEG data through the intelligent head ring;
the step of processing the answer data and the EEG data to obtain corresponding answer scores and EEG scores comprises the following steps:
processing the first answer data, the first EEG data, the second answer data and the second EEG data respectively to obtain corresponding first answer scores, first EEG scores, second answer scores and second EEG scores;
the step of obtaining an attention score value according to the answer score, the EEG score and a preset multivariate regression equation comprises the following steps:
and obtaining a score value of the continuous attention game and score values of other attentions according to the first answer score, the first EEG score, the second answer score, the second EEG score and a preset multivariate regression equation.
Optionally, the attention assessment method further comprises:
acquiring first evaluation answer data and a first self-evaluation when an evaluator plays the continuous attention game, and acquiring corresponding first evaluation EEG data through the intelligent head ring;
preprocessing the first evaluation answer data and the first evaluation EEG data respectively to obtain corresponding first scores and second scores;
performing kernel density estimation on the first score and the second score respectively to obtain a corresponding first distribution curve and a corresponding second distribution curve;
obtaining a rating answer score of continuous attention according to the first score and the first distribution curve, and obtaining a rating EEG score of continuous attention according to the second score and the second distribution curve;
and constructing a first multivariate regression equation according to the evaluation answer score of the persistent attention, the evaluation EEG score of the persistent attention and the first self-score, obtaining a first optimal coefficient of the first multivariate regression equation through a normal equation, and substituting the first optimal coefficient into the first multivariate regression equation to obtain the multivariate regression equation of the persistent attention of the preset multivariate regression equation.
Optionally, the attention assessment method further comprises:
acquiring second evaluation answer data and second self-evaluation when the evaluator plays the other attention games, and acquiring corresponding second evaluation EEG data through the intelligent head ring;
respectively preprocessing the second evaluation answer data and the second evaluation EEG data to obtain a third score and a fourth score which correspond to each other;
performing kernel density estimation on the third score and the fourth score respectively to obtain a corresponding third distribution curve and a corresponding fourth distribution curve;
obtaining the scores of the assessment answers of other attentions according to the third score and the third distribution curve, and obtaining the scores of the assessment EEG of other attentions according to the fourth score and the fourth distribution curve;
and constructing a second multivariate regression equation according to the evaluation answer scores of the other attentions, the evaluation EEG scores of the other attentions and the second self-scores, obtaining a second optimal coefficient of the second multivariate regression equation through a normal equation, and substituting the second optimal coefficient into the second multivariate regression equation to obtain the multivariate regression equation of the other attentions of the preset multivariate regression equation.
Optionally, the step of processing the first answer data, the first EEG data, the second answer data, and the second EEG data respectively to obtain corresponding first answer score, first EEG score, second answer score, and second EEG score includes:
preprocessing the first answer data, the first EEG data, the second answer data and the second EEG data respectively to obtain a fifth score, a sixth score, a seventh score and an eighth score;
obtaining a first curve lower area corresponding to the fifth value and a first total area between the first distribution curve and a horizontal axis through integration according to the fifth value and the first distribution curve, and recording a percentage value of the first curve lower area and the first total area as a first answer score;
obtaining a second area under the curve corresponding to the sixth score and a 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 recording the percentage value of the second area under the curve and the second total area as a first EEG score;
obtaining a third curve lower area corresponding to the seventh score and a 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 recording a percentage value of the third curve lower area and the third total area as a second answer score;
and obtaining a fourth area under the curve corresponding to the eighth score and a 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 recording the percentage value of the fourth area under the curve and the fourth total area as a second EEG score.
Optionally, the step of obtaining the score value of the continuous attention game and the score values of other attentions according to the first answer score, the first EEG score, the second answer score, the second EEG score and a preset multivariate regression equation comprises:
and obtaining the score value of the continuous attention game according to the first answer score, the first EEG score and the multivariate regression equation of the continuous attention in the preset multivariate regression equation, and obtaining the score value of other attention according to the second answer score, the second EEG score and the multivariate regression equation of other attention in the preset multivariate regression equation.
Optionally, the first answer data and the first evaluated answer data include a maximum number of correct answers and a maximum total number of answers in succession, and the second answer data and the second evaluated answer data include a number of correct answers and a number of wrong answers.
In addition, in order to achieve the above object, the present invention further provides an attention assessment system, which includes an attention assessment terminal and an intelligent head ring, and further includes a memory, a processor, and an attention assessment program stored in the memory and operable on the processor, wherein the attention assessment program, when executed by the processor, implements the steps of the attention assessment method as described above.
Further, to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon an attention evaluation program which, when executed by a processor, implements the steps of the attention evaluation method as described above.
The invention provides an attention evaluation method, an attention evaluation system and a computer readable storage technology. The attention evaluation terminal acquires answer data when a user carries out a preset attention game and acquires corresponding EEG data through an intelligent head ring; and processing the answer data and the EEG data to obtain corresponding answer scores and EEG scores, and finally, bringing the answer scores and the EEG scores into a preset multivariate regression equation to obtain the attention scores. The invention acquires EEG data by utilizing brain-computer interface technology, combines the answer data with the EEG data, processes to obtain corresponding answer score and EEG score, and calculates the attention score by a multivariate regression equation between the attention score obtained by early-stage optimization and the answer score and the EEG score.
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Fig. 1 is a schematic terminal structure diagram of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a first embodiment of the attention assessment method according to the present invention;
FIG. 3 is a flowchart illustrating a second embodiment of the attention assessment method according to the present invention;
FIG. 4 is a flowchart illustrating a third embodiment of the attention assessment method according to the present invention;
FIG. 5 is a diagram illustrating a first distribution curve according to an embodiment of the present invention;
FIG. 6 is a flowchart illustrating a fourth embodiment of the attention assessment method according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the prior art, since attention has important relevance and influence on many aspects of the user, for example, the attention level of children affects the cognitive development of the children, many attention games are now introduced to test the attention of the user so as to culture and promote the attention of the user at a later stage. However, currently, the assessment of attention is mainly scored through some related attention games, and the assessment result is only based on the scoring rules of the game itself, so that the problem of low accuracy of the assessment result exists.
In order to solve the technical problems, the invention provides an attention assessment method, an attention assessment system and a computer readable storage technology. The attention evaluation terminal acquires answer data when a user carries out a preset attention game and acquires corresponding EEG data through an intelligent head ring; and processing the answer data and the EEG data to obtain corresponding answer scores and EEG scores, and finally, bringing the answer scores and the EEG scores into a preset multivariate regression equation to obtain the attention scores. The invention acquires EEG data by utilizing brain-computer interface technology, combines the answer data with the EEG data, processes to obtain corresponding answer score and EEG score, and calculates the attention score by a multivariate regression equation between the attention score obtained by early-stage optimization and the answer score and the EEG score.
Referring to fig. 1, fig. 1 is a schematic terminal structure diagram of a hardware operating environment according to an embodiment of the present invention.
The terminal in the embodiment of the invention is an attention evaluation terminal, the attention evaluation terminal can be a PC (personal computer), and can also be a mobile terminal device with a display function, such as a smart phone, a tablet personal computer, a portable computer and the like, and a preset attention game is built in the attention evaluation terminal.
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 a communication bus 1002 is used to enable connective 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, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., a Wi-Fi interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the terminal structure shown in fig. 1 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and an attention evaluation program.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and performing data communication with the backend server; the user interface 1003 is mainly used for connecting a client and performing data communication with the client; and the processor 1001 may be configured to invoke the attention-assessment program stored in the memory 1005 and perform the following operations:
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.
Further, the processor 1001 may call the attention-assessment program stored in the memory 1005, and also perform the following operations:
the general formula of the preset multivariate regression equation is as follows: and Z is aX + bY, wherein Z is the attention score, X is the answer score, Y is the EEG score, and a and b are respectively corresponding optimal coefficients.
Further, the preset attention game includes a continuous attention game and other attention games, the other attention games include a selective attention game, a convertible attention game, a distraction attention game and an attention span game, and the processor 1001 may call the attention evaluation program stored in the memory 1005 and further perform the following operations:
the attention evaluation terminal respectively acquires first answer data and second answer data when a user carries out a continuous attention game and other attention games, and respectively acquires corresponding first EEG data and second EEG data through the intelligent head ring;
processing the first answer data, the first EEG data, the second answer data and the second EEG data respectively to obtain corresponding first answer scores, first EEG scores, second answer scores and second EEG scores;
and obtaining a score value of the continuous attention game and score values of other attentions according to the first answer score, the first EEG score, the second answer score, the second EEG score and a preset multivariate regression equation.
Further, the processor 1001 may call the attention-assessment program stored in the memory 1005, and also perform the following operations:
acquiring first evaluation answer data and a first self-evaluation when an evaluator plays the continuous attention game, and acquiring corresponding first evaluation EEG data through the intelligent head ring;
preprocessing the first evaluation answer data and the first evaluation EEG data respectively to obtain corresponding first scores and second scores;
performing kernel density estimation on the first score and the second score respectively to obtain a corresponding first distribution curve and a corresponding second distribution curve;
obtaining a rating answer score of continuous attention according to the first score and the first distribution curve, and obtaining a rating EEG score of continuous attention according to the second score and the second distribution curve;
and constructing a first multivariate regression equation according to the evaluation answer score of the persistent attention, the evaluation EEG score of the persistent attention and the first self-score, obtaining a first optimal coefficient of the first multivariate regression equation through a normal equation, and substituting the first optimal coefficient into the first multivariate regression equation to obtain the multivariate regression equation of the persistent attention of the preset multivariate regression equation.
Further, the processor 1001 may call the attention-assessment program stored in the memory 1005, and also perform the following operations:
acquiring second evaluation answer data and second self-evaluation when the evaluator plays the other attention games, and acquiring corresponding second evaluation EEG data through the intelligent head ring;
respectively preprocessing the second evaluation answer data and the second evaluation EEG data to obtain a third score and a fourth score which correspond to each other;
performing kernel density estimation on the third score and the fourth score respectively to obtain a corresponding third distribution curve and a corresponding fourth distribution curve;
obtaining the scores of the assessment answers of other attentions according to the third score and the third distribution curve, and obtaining the scores of the assessment EEG of other attentions according to the fourth score and the fourth distribution curve;
and constructing a second multivariate regression equation according to the evaluation answer scores of the other attentions, the evaluation EEG scores of the other attentions and the second self-scores, obtaining a second optimal coefficient of the second multivariate regression equation through a normal equation, and substituting the second optimal coefficient into the second multivariate regression equation to obtain the multivariate regression equation of the other attentions of the preset multivariate regression equation.
Further, the processor 1001 may call the attention-assessment 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 a fifth score, a sixth score, a seventh score and an eighth score;
obtaining a first curve lower area corresponding to the fifth value and a first total area between the first distribution curve and a horizontal axis through integration according to the fifth value and the first distribution curve, and recording a percentage value of the first curve lower area and the first total area as a first answer score;
obtaining a second area under the curve corresponding to the sixth score and a 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 recording the percentage value of the second area under the curve and the second total area as a first EEG score;
obtaining a third curve lower area corresponding to the seventh score and a 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 recording a percentage value of the third curve lower area and the third total area as a second answer score;
and obtaining a fourth area under the curve corresponding to the eighth score and a 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 recording the percentage value of the fourth area under the curve and the fourth total area as a second EEG score.
Further, the processor 1001 may call the attention-assessment program stored in the memory 1005, and also perform the following operations:
and obtaining the score value of the continuous attention game according to the first answer score, the first EEG score and the multivariate regression equation of the continuous attention in the preset multivariate regression equation, and obtaining the score value of other attention according to the second answer score, the second EEG score and the multivariate regression equation of other attention in the preset multivariate regression equation.
Further, the first answer data and the first evaluation answer data include a maximum continuous answer correct number and an answer total number, and the second answer data and the second evaluation answer data include an answer correct number and an answer error number.
Based on the hardware structure, the embodiment of the attention assessment method is provided.
The invention provides an attention assessment method.
Referring to fig. 2, fig. 2 is a schematic flow chart illustrating a first embodiment of an attention assessment method according to the present invention.
In the embodiment of the invention, the attention assessment method is applied to an attention assessment system, and the attention assessment system comprises an attention assessment terminal and an intelligent head ring. The attention assessment terminal is internally provided with a preset attention game for a user and an assessor to assess attention, wherein the preset attention game comprises a continuous attention game and other attention games, the other attention games comprise a selective attention game, a convertible attention game, a dispersive attention game and an attention span game, and the attention assessment terminal is used for acquiring answer data and EEG data sent by an intelligent head ring when the user and the assessor conduct the preset attention game and then processing the answer data and the EEG data to obtain a final attention score. The intelligent head ring uses a brain-computer interface technology, is used for collecting EEG (Electroencephalogram) data of a user and an evaluator, and can be in communication connection with the attention evaluation terminal so as to transmit the EEG to the attention evaluation terminal for processing and evaluation.
The attention assessment method comprises the following steps:
step S10, the attention assessment terminal acquires answer data when a user carries out a preset attention game, and acquires corresponding brain wave EEG data through the intelligent head loop;
in this embodiment, the attention evaluating terminal first acquires answer data of a user when the user performs a preset attention game, and acquires corresponding EEG data through an intelligent head loop. The answer data may include, but is not limited to, correct answer number, incorrect answer number, maximum continuous correct answer number, and total answer number, and different answer data may be obtained according to different types of the preset attention game. For example, when the preset attention game is a continuous attention game, the corresponding answer data may be recorded as first answer data, and the first answer data may include the maximum number of correct answers and the total number of answers in a continuous answer; when the predetermined attention game is another attention game, the corresponding answer data may be recorded as second answer data, and the second answer data may include the maximum number of correct continuous answers and the total number of answers.
Step S20, processing the answer data and the 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 acquired answer data and EEG data may not be identical, and the corresponding data processing methods are different. For a specific processing method, reference may be made to the following embodiments, which are not described herein again. In correspondence to the above embodiments, the answer score may include a first answer score and a second answer score, and the EEG score may include a first EEG score and a second EEG score.
And step S30, obtaining an attention point value according to the answer score, the EEG score and a preset multivariate regression equation.
And finally, the attention evaluation terminal obtains a final attention score value according to the answer score, the EEG score and a preset multivariate regression equation. Wherein the predetermined multivariate regression equation comprises a persistent attention multivariate regression equation and other attention multivariate regression equations, and the other attention multivariate regression equations comprise a selective attention multivariate regression equation, a transformative attention multivariate regression equation, a dispersive attention multivariate regression equation, and an attention span multivariate regression equation. The general formula of the preset multivariate regression equation is as follows: and Z is aX + bY, wherein Z is the attention score, X is the answer score, Y is the EEG score, and a and b are respectively corresponding optimal coefficients. And substituting the answer score and the EEG score into a preset multivariate regression equation to obtain the attention score.
The embodiment provides an attention assessment method, which is applied to an attention assessment system, and the attention assessment system comprises an attention assessment terminal and an intelligent head ring. The attention evaluation terminal acquires answer data when a user carries out a preset attention game and acquires corresponding EEG data through an intelligent head ring; and processing the answer data and the EEG data to obtain corresponding answer scores and EEG scores, and finally, bringing the answer scores and the EEG scores into a preset multivariate regression equation to obtain the attention scores. The invention acquires EEG data by utilizing brain-computer interface technology, combines the answer data with the EEG data, processes to obtain corresponding answer score and EEG score, and calculates the attention score by a multivariate regression equation between the attention score obtained by early-stage optimization and the answer score and the EEG score.
Further, please refer to fig. 3, in which fig. 3 illustrates a second embodiment of the attention-assessing method according to the present invention.
In the first embodiment shown in fig. 2, in view of the fact that the basic attributes of the continuous attention and other attentions (including calculating the selective attention, the converted attention, the distraction attention and the attention span) are not consistent, the processing method and algorithm are different when calculating the scores corresponding to the attentions of the dimensions, wherein the algorithm for calculating the four attention scores of the selective attention, the converted attention, the distraction attention and the attention span is the same, and the algorithm for calculating the continuous attention score is another algorithm. Accordingly, the preset attention game includes a continuous attention game and other attention games including a selective attention game, a transitional attention game, a distraction attention game, and an attention span game. Of course, in one embodiment, the predetermined attention game may include 5 levels, each level corresponding to one attention test. Step S10 includes:
step S100, the attention evaluation terminal respectively acquires first answer data and second answer data when a user carries out a continuous attention game and other attention games, and respectively acquires corresponding first EEG data and second EEG data through the intelligent head ring;
in this embodiment, since the algorithms of the sustained attention score and the other attention scores are not consistent, the game data of each corresponding game needs to be acquired, and corresponding processing and calculation needs to be performed. Firstly, the attention testing terminal respectively acquires first answer data and second answer data when the user conducts the continuous attention game and other attention games, and respectively acquires corresponding first EEG data and second EEG data through the intelligent head ring, wherein the first answer data comprises but is not limited to the maximum continuous answer correct number and the total answer number, and the second answer data comprises but is not limited to the answer correct number and the answer error number.
At this time, step S20 includes:
step S200, processing the first answer data, the first EEG data, the second answer data and the second EEG data respectively to obtain corresponding first answer score, first EEG score, second answer score and second EEG score;
then, the first answer data, the first EEG data, the second answer data and the second EEG data are processed respectively to obtain corresponding first answer score, first EEG score, second answer score and second EEG score. The specific processing method may refer to that described in the following fifth embodiment, which is not described herein again.
At this time, step S30 includes:
and step S300, obtaining a score value of the continuous attention game and score values of other attentions according to the first answer score, the first EEG score, the second answer score, the second EEG score and a preset multivariate regression equation.
And finally, obtaining the score value of the continuous attention game and the score values of other attentions 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 predetermined multivariate regression equations are optimized in the previous period, and can be described in the third and fourth embodiments below, and include a multivariate regression equation of continuous attention and a multivariate regression equation of other attention, and the multivariate regression equation of other attention includes a multivariate regression equation of selective attention, a multivariate regression equation of transformative attention, a multivariate regression equation of dispersive attention, and a multivariate regression equation of attention span. And substituting the first answer score and the first EEG score into the multivariate regression equation of the continuous attention to obtain the score value of the continuous attention game. Similarly, the second answer score and the second EEG score are correspondingly substituted into a multivariate regression equation of other attention, so that the score values of other attention games can be obtained.
Further, please refer to fig. 4, wherein fig. 4 is a flowchart illustrating a attention assessment method according to a third embodiment of the present invention.
Based on the first embodiment and the second embodiment, before the user is evaluated, an evaluator needs to be selected, and a corresponding algorithm is optimized according to the answer result of the evaluator. Therefore, before step S100, the attention assessment method further includes:
step S410, acquiring first evaluation answer data and first self-evaluation when an evaluator plays the continuous attention game, and acquiring corresponding first evaluation EEG data through the intelligent head ring;
in this embodiment, the persistent attention refers to the duration of attention to important messages, and the algorithm thereof is inconsistent with the algorithm of other attention scores.
In this embodiment, the attention evaluation terminal first acquires first evaluation answer data and a first self-evaluation when the evaluator plays the continuous attention game, and acquires corresponding first evaluation EEG data through the intelligent head loop. Wherein, the first test question data comprises the maximum correct number of continuous answers and the total number of answers; the first self-evaluation is the self-evaluation score of the evaluator itself input by the attention evaluation terminal after the continuous attention game is completed (before the evaluator inputs, the meaning represented by the continuous attention can be explained to ensure the evaluator to self-evaluate after understanding, so that the accuracy of the algorithm is improved, and the accuracy of the final evaluation result is improved).
It should be noted that, in order to ensure the accuracy of the attention algorithm, there are certain requirements on the selection and number of the evaluators, where the selection requirements are not specifically set forth, and the number of the evaluators should be within a certain range, and can be selected and set according to the actual situation.
Step S420, preprocessing the first evaluation answer data and the first evaluation EEG data respectively to obtain corresponding first scores and second scores;
secondly, preprocessing the first evaluation answer data and the first evaluation EEG data respectively to obtain a corresponding first score and a corresponding second score. Specifically, the first score is obtained by calculating a percentage value of the maximum number of consecutive correct answers to the total number of answers in the first assessment answer data, for example, 5 questions in total (i.e., the total number of answers is 5) in the continuous attention game, and when a certain tester answers the 3 rd and 4 th questions (i.e., the maximum number of correct consecutive answers is 2), the first score is 2/5 × 100 — 40. Then, an average concentration value corresponding to the first evaluation EEG data is calculated through a concentration algorithm, and the time t corresponding to the time t which is continuously greater than the average concentration value to the maximum extent is obtained according to the first evaluation EEG data and the average concentration value1And calculating the time t1The percentage value to the total game time is the second score. Wherein the concentration algorithm is obtained by a plurality of tests and optimization, and is not disclosed herein.
Step S430, performing kernel density estimation on the first score and the second score respectively to obtain a corresponding first distribution curve and a corresponding second distribution curve;
and thirdly, performing kernel density estimation on the first score and the second score respectively to obtain a corresponding first distribution curve and a corresponding second distribution curve. The specific implementation principle and technology can refer to the prior art, and are not described in detail herein.
Step S440, obtaining a rating answer score of continuous attention according to the first score and the first distribution curve, and obtaining a rating EEG score of continuous attention according to the second score and the second distribution curve;
then, a rating answer score for the sustained attention is obtained from the first score and the first distribution curve, and a rating EEG score for the sustained attention is obtained from the second score and the second distribution curve. Specifically, an area S11 between the curve corresponding to the left side portion of the first distribution curve and the horizontal axis and an area S12 between the first distribution curve and the horizontal axis are calculated, and then a percentage value of the area S11 to the area S12 is calculated, which is the score of the sustained attention test answer. For example, in the above example, if the first score is 40 and the corresponding first distribution curve is shown in fig. 5, S11 is the area corresponding to the shaded portion in fig. 5. Then, an area S21 between the curve corresponding to the left side portion of the second distribution curve and the horizontal axis and an area S22 between the second distribution curve and the horizontal axis are calculated, and then the percentage value of the area S21 to the area S22 is calculated, which is the evaluation EEG score of the persistent attention. For convenience of explanation, the first and second distribution curves may be respectively denoted as C1 and C2, and the first and second distribution curves may be respectively denoted as f1(x)、f2(x) The concrete formula is as follows:
Figure GDA0002513868000000141
Figure GDA0002513868000000142
step S450, a first multivariate regression equation is constructed according to the evaluation answer value of the continuous attention, the evaluation EEG value of the continuous attention and the first self-evaluation, a first optimal coefficient of the first multivariate regression equation is obtained through a normal equation, and the first optimal coefficient is substituted into the first multivariate regression equation to obtain the multivariate regression equation of the continuous attention of the preset multivariate regression equation.
Finally, a first multivariate regression equation is constructed according to the evaluation answer score of the persistent attention, the evaluation EEG score of the persistent attention and the first self-score, and the first multivariate regression equation can be: z1＝a1X1+b1Y1Wherein Z is1Denotes the first self-score, X1Assessment answer score, Y, representing sustained attention1And expressing the evaluation EEG score of the continuous attention, then obtaining a first optimal coefficient of the first multivariate regression equation through a normal equation, and substituting the first optimal coefficient into the first multivariate regression equation to obtain the multivariate regression equation of the continuous attention of the preset multivariate regression equation.
For example, in the above example, since 15 evaluators were selected, 15 sets of first evaluation answer data, 15 sets of first self-scoring, and 15 sets of first evaluation EEG data were acquired, and after processing, 15 sets of evaluation answer scores for sustained attention and 15 sets of evaluation EEG scores for sustained attention were obtained. Then, according to 15 groups of first self-scoring, 15 groups of evaluation answer scores of continuous attention, 15 groups of evaluation EEG scores of continuous attention and a first multivariate regression equation, an optimal coefficient a is found out through a normal equation1And b1Let a be10.6 and b1The sustained attention score is calculated as 0.4: z1＝0.6X1+0.4Y1。
Referring to fig. 6, fig. 6 is a flowchart illustrating a attention assessment method according to a fourth embodiment of the present invention.
Based on the first embodiment shown in fig. 2, before step S100, the attention assessment method further includes:
referring to step S510 in fig. 6, second assessment answer data and second self-scoring when the assessor performs the other attention game are obtained, and corresponding second assessment EEG data is obtained through the smart headband;
in the present embodiment, since the algorithm of the other attention scores is not consistent with the algorithm of the sustained attention scores, the algorithm optimization process of the other attention scores, that is, the algorithm optimization process of the four attention scores of selective attention, convertible attention, distractive attention and attention span, is described in the present embodiment.
In this embodiment, the attention evaluation terminal first acquires second evaluation answer data and second self-evaluation when the evaluator plays another attention game, and acquires corresponding second evaluation EEG data through the smart headband. Wherein, the second test question data comprises correct number and wrong number of answer; the second self-evaluation is the self-evaluation score of the evaluator to the self input by the attention evaluation terminal after the evaluator finishes other attention games (before the evaluator inputs, the meaning represented by other attention can be explained to ensure that the evaluator carries out self-evaluation after understanding, so that the accuracy of the algorithm is improved, and the accuracy of the final evaluation result is improved). It should be noted that the other attention games include a selective attention game, a convertible attention game, a dispersive attention game and an attention span game, therefore, in the data acquisition and calculation process of the embodiment, there are also 4 kinds of data corresponding to each attention, respectively, and the finally obtained other attention multivariate regression equations also include 4 kinds of, i.e., a selective attention multivariate regression equation, a convertible attention multivariate regression equation, a dispersive attention multivariate regression equation and an attention span multivariate regression equation.
Step S520, preprocessing the second evaluation answer data and the second evaluation EEG data respectively to obtain a third score and a fourth score which correspond to each other;
and secondly, preprocessing the second evaluation answer data and the second evaluation EEG data respectively to obtain a third score and a fourth score which correspond to each other. Specifically, the difference between the number of correct answers and the number of wrong answers in the second evaluation answer data is calculated, and the difference is the third score. And then calculating an average concentration value corresponding to the second evaluation EEG data through a concentration algorithm, wherein the average concentration value is a fourth value. Wherein the concentration algorithm is obtained by a plurality of tests and optimization, and is not disclosed herein.
Step S530, performing kernel density estimation on the third score and the fourth score respectively to obtain a corresponding third distribution curve and a corresponding fourth distribution curve;
and thirdly, performing kernel density estimation on the third score and the fourth score respectively to obtain a corresponding third distribution curve and a corresponding fourth distribution curve. The specific implementation principle and technology can refer to the prior art, and are not described in detail herein.
Step S540, obtaining the evaluation answer scores of other attentions according to the third score and the third distribution curve, and obtaining the evaluation EEG scores of other attentions according to the fourth score and the fourth distribution curve;
then, the assessment answer scores of other attentions are obtained according to the third score and the third distribution curve, and the assessment EEG scores of other attentions are obtained according to the fourth score and the fourth distribution curve. Specifically, an area S31 between a curve corresponding to the left side of the third distribution curve and the horizontal axis and an area S32 between the third distribution curve and the horizontal axis are calculated for the third score, and then a percentage value of the area S31 to the area S32 is calculated, which is the score of the other attention test questions. And calculating the area S41 between the curve of the left part of the fourth distribution curve and the horizontal axis and the area S42 between the third distribution curve and the horizontal axis, wherein the fourth score corresponds to the area S41 between the curve of the left part of the fourth distribution curve and the horizontal axis, and then calculating the percentage value of the area S41 and the area S42, namely the EEG score for other attention measurement. For convenience of explanation, the third and fourth scores may be respectively labeled as C3 and C4, and the third and fourth distribution curves may be respectively labeled as f3(x)、f4(x) The concrete formula is as follows:
Figure GDA0002513868000000171
Figure GDA0002513868000000172
and S550, constructing a second multivariate regression equation according to the evaluation answer scores of the other attentions, the evaluation EEG scores of the other attentions and the second self-evaluation, obtaining a second optimal coefficient of the second multivariate regression equation through a normal equation, and substituting the second optimal coefficient into the second multivariate regression equation to obtain the multivariate regression equation of the other attentions of the preset multivariate regression equation.
Finally, a second multivariate regression equation is constructed according to the other attention assessment answer scores, the other attention assessment EEG scores and the second self scores, and the second multivariate regression equation can be: z2＝a2X2+b2Y2Wherein Z is2Denotes the second self-score, X2Score of assessment questions, Y, representing other attentions2And expressing the evaluated EEG scores of other attentions, then obtaining a second optimal coefficient of the second multivariate regression equation through a normal equation, and substituting the second optimal coefficient into the second multivariate regression equation to obtain a multivariate regression equation of other attentions of the preset multivariate regression equation.
For example, regarding selective attention, in the above example, since 15 evaluators were selected, 15 sets of second evaluation answer data, 15 sets of second self-scoring, and 15 sets of second evaluation EEG data were acquired, and after being processed, 15 sets of other-attention evaluation answer scores and 15 sets of other-attention evaluation EEG scores were obtained, respectively. Then, according to 15 groups of second self-scoring, 15 groups of scores of the evaluated answers of other attentions, 15 groups of scores of the evaluated EEG of other attentions and a second multivariate regression equation, an optimal coefficient a is found out through a normal equation2And b2Let a be20.5 and b2The selective attention score is calculated as 0.7: z2＝0.5X2+0.7Y2。
It should be noted that the steps S410 to S450 in the second embodiment and the steps S510 to S550 in the third embodiment are not executed in sequence.
Further, a fifth embodiment of the attention evaluating method of the present invention is proposed based on the above-described embodiments shown in fig. 2 to 4.
Based on the foregoing embodiments, in this embodiment, step S200 includes:
step S210, preprocessing the first answer data, the first EEG data, the second answer data and the second EEG data respectively to obtain a fifth score, a sixth score, a seventh score and an eighth score;
in this embodiment, after the attention evaluating terminal respectively acquires the first answer data and the second answer data when the user performs the persistent attention game and the other attention games, and respectively acquires the corresponding first EEG data and second EEG data through the smart headring, the first answer data, the first EEG data, the second answer data, and the second EEG data are respectively preprocessed to obtain corresponding fifth score, sixth score, seventh score, and eighth score. Specifically, a percentage value of the maximum continuous correct answer number to the total answer number in the first answer data is calculated, and the percentage value is a fifth value; calculating a difference value of subtracting the number of answer errors from the number of answer correct numbers in the second answer data to obtain a sixth score; calculating an average concentration value corresponding to the first EEG data through a concentration algorithm, obtaining the time which is continuously longer than the average concentration value at the longest according to the first EEG data and the average concentration value, and calculating a percentage value of the time and the total game time, namely a seventh score; and calculating an average concentration value corresponding to the second EEG data through a concentration algorithm, namely an eighth value.
Step S220, obtaining a first area under the curve corresponding to the fifth value and a first total area between the first distribution curve and a horizontal axis through integration according to the fifth value and the first distribution curve, and recording a percentage value of the first area under the curve and the first total area as a first answer score;
step S230, obtaining a second area under the curve corresponding to the sixth score and a 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 recording the percentage value of the second area under the curve and the second total area as a first EEG score;
step S240, obtaining a third curve lower area corresponding to the seventh score and a third total area between the third distribution curve and a horizontal axis through integration according to the seventh score and the third distribution curve, and recording a percentage value of the third curve lower area and the third total area as a second answer score;
step S250, obtaining a fourth area under the curve corresponding to the eighth score and a 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 recording a percentage value of the fourth area under the curve and the fourth total area as a second EEG score.
And then, obtaining the area under the first curve corresponding to the fifth value and a first total area between the first distribution curve and the horizontal axis by integration according to the fifth value and the first distribution curve, and recording the percentage value of the area under the first curve and the first total area as a first answer score. Wherein the first distribution curve is obtained in the optimization process of the algorithm, the fifth value is marked as C5 for the convenience of description, and the fifth value is C5 and the first distribution curve f1(x) If the area under the first curve corresponding to the fifth value obtained by integration is denoted as S13, and the first total area between the first distribution curve and the horizontal axis is S12 in the above embodiment, then:
Figure GDA0002513868000000191
similarly, according to the sixth score and the second distribution curve, obtaining a second area under the curve corresponding to the sixth score and a second total area between the second distribution curve and the horizontal axis through integration, and recording a percentage value of the second area under the curve and the second total area as a first EEG score; obtaining a third curve lower area corresponding to the seventh score and a 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 recording a percentage value of the third curve lower area and the third total area as a second answer score; and obtaining a fourth area under the curve corresponding to the eighth score and a 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 recording the percentage value of the fourth area under the curve and the fourth total area as a second EEG score. The specific processing method can refer to the processing method described in the foregoing embodiment, and is not described herein again.
It should be noted that, the steps from step S220 to step S250 are not executed in sequence.
At this time, step S300 may further include:
step S310, obtaining a score value of the continuous attention game according to the first answer score, the first EEG score and a multivariate regression equation of continuous attention in a preset multivariate regression equation, and obtaining score values of other attention according to the second answer score, the second EEG score and multivariate regression equations of other attention in the preset multivariate regression equation.
In this embodiment, the score value of the continuous attention game can be obtained by substituting the first answer score and the first EEG score into the multivariate regression equation of the continuous attention in the preset multivariate regression equation. Similarly, the second answer score and the second EEG score are correspondingly substituted into the other attention multivariate regression equations in the preset multivariate regression equation, and the score values of other attention games can be obtained.
The invention further provides an attention assessment system, which comprises an attention assessment terminal and an intelligent head ring, and further comprises a memory, a processor and an attention assessment program stored on the memory and capable of running on the processor, wherein when the attention assessment program is executed by the processor, the steps of the attention assessment method according to any one of the above embodiments are realized.
The specific embodiment of the attention evaluation system of the present invention is substantially the same as the embodiments of the attention evaluation method described above, and will not be described herein again.
The present invention also provides a computer readable storage medium having stored thereon an attention evaluation program which, when executed by a processor, implements the steps of the attention evaluation method as described in any one of the above embodiments.
The specific embodiment of the computer-readable storage medium of the present invention is substantially the same as the embodiments of the attention assessment method described above, and will not be described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (9)

1. An attention assessment method is characterized by being 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 respectively acquires first answer data and second answer data when a user carries out a continuous attention game and other attention games, and respectively acquires corresponding first EEG data and second EEG data through the intelligent head ring;
processing the first answer data, the first EEG data, the second answer data and the second EEG data respectively to obtain corresponding first answer scores, first EEG scores, second answer scores and second EEG scores;
substituting the first answer score and the first EEG score into a multivariate regression equation of continuous attention in a preset multivariate regression equation to obtain a score value of the continuous attention, and substituting the second answer score and the second EEG score into multivariate regression equations of other attention in the preset multivariate regression equation to obtain score values of other attention;
wherein, the step of processing the first answer data, the first EEG data, the second answer data and the second EEG data respectively to obtain corresponding first answer score, first EEG score, second answer score and second EEG score comprises:
calculating the percentage value of the maximum continuous correct answer number to the total answer number in the first answer data, and recording as a fifth value;
calculating the difference value of subtracting the number of answer errors from the number of answer correct numbers in the second answer data, and recording as a sixth score;
calculating a first average concentration value corresponding to the first EEG data, obtaining time which is continuously longer than the first average concentration value at the longest according to the first EEG data and the first average concentration value, and calculating a percentage value of the time to total game time, and recording the percentage value as a seventh score;
calculating a second average concentration value corresponding to the second EEG data and recording as an eighth value;
and obtaining the area under the curve corresponding to each score and the total area between each distribution curve and the horizontal axis by integration according to the fifth score, the sixth score, the seventh score and the eighth score and the distribution curves corresponding to each score, respectively, calculating the percentage value of the area under the curve corresponding to each score and the corresponding total area, and obtaining the corresponding first answer score, the corresponding first EEG score, the corresponding second answer score and the corresponding second EEG score according to each percentage value.
2. The attention assessment method according to claim 1, wherein said predetermined multivariate regression equation has a general formula of: and Z is aX + bY, wherein Z is the attention score, X is the answer score, Y is the EEG score, and a and b are respectively corresponding optimal coefficients.
3. The attention scoring method of claim 1, wherein the other attention games include a selective attention game, a transitional attention game, a distractive attention game, and an attention span game.
4. The attention assessment method according to claim 1, further comprising:
acquiring first evaluation answer data and a first self-evaluation when an evaluator plays the continuous attention game, and acquiring corresponding first evaluation EEG data through the intelligent head ring;
preprocessing the first evaluation answer data and the first evaluation EEG data respectively to obtain corresponding first scores and second scores;
performing kernel density estimation on the first score and the second score respectively to obtain a corresponding first distribution curve and a corresponding second distribution curve;
obtaining a rating answer score of continuous attention according to the first score and the first distribution curve, and obtaining a rating EEG score of continuous attention according to the second score and the second distribution curve;
and constructing a first multivariate regression equation according to the evaluation answer score of the persistent attention, the evaluation EEG score of the persistent attention and the first self-score, obtaining a first optimal coefficient of the first multivariate regression equation through a normal equation, and substituting the first optimal coefficient into the first multivariate regression equation to obtain the multivariate regression equation of the persistent attention of the preset multivariate regression equation.
5. The attention assessment method according to claim 4, further comprising:
acquiring second evaluation answer data and second self-evaluation when the evaluator plays the other attention games, and acquiring corresponding second evaluation EEG data through the intelligent head ring;
respectively preprocessing the second evaluation answer data and the second evaluation EEG data to obtain a third score and a fourth score which correspond to each other;
performing kernel density estimation on the third score and the fourth score respectively to obtain a corresponding third distribution curve and a corresponding fourth distribution curve;
obtaining the scores of the assessment answers of other attentions according to the third score and the third distribution curve, and obtaining the scores of the assessment EEG of other attentions according to the fourth score and the fourth distribution curve;
and constructing a second multivariate regression equation according to the evaluation answer scores of the other attentions, the evaluation EEG scores of the other attentions and the second self-scores, obtaining a second optimal coefficient of the second multivariate regression equation through a normal equation, and substituting the second optimal coefficient into the second multivariate regression equation to obtain the multivariate regression equation of the other attentions of the preset multivariate regression equation.
6. The attention assessment method according to claim 5, wherein the step of obtaining the area under the curve corresponding to each score and the total area between each distribution curve and the horizontal axis by integration according to the fifth score, the sixth score, the seventh score, the eighth score and the distribution curve corresponding to each score, respectively, and calculating the percentage value of the area under the curve corresponding to each score and the corresponding total area, and obtaining the corresponding first answer score, the first EEG score, the second answer score and the second EEG score according to each percentage value comprises:
obtaining a first curve lower area corresponding to the fifth value and a first total area between the first distribution curve and a horizontal axis through integration according to the fifth value and the first distribution curve, and recording a percentage value of the first curve lower area and the first total area as a first answer score;
obtaining a second area under the curve corresponding to the sixth score and a 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 recording the percentage value of the second area under the curve and the second total area as a first EEG score;
obtaining a third curve lower area corresponding to the seventh score and a 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 recording a percentage value of the third curve lower area and the third total area as a second answer score;
and obtaining a fourth area under the curve corresponding to the eighth score and a 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 recording the percentage value of the fourth area under the curve and the fourth total area as a second EEG score.
7. The attention evaluating method according to claim 6, wherein the first answer data and the first evaluated answer data include a maximum number of correct answers and a total number of answers in succession, and the second answer data and the second evaluated answer data include a number of correct answers and a number of wrong answers.
8. An attention assessment system, characterized in that the attention assessment system comprises an attention assessment terminal and an intelligent head ring, and further comprises a memory, a processor and an attention assessment program stored on the memory and operable on the processor, wherein the attention assessment program when executed by the processor implements the steps of the attention assessment method according to any one of claims 1 to 7.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon an attention-assessment program which, when executed by a processor, carries out the steps of the attention-assessment method according to any one of claims 1 to 7.
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