CN117972471A - Brain information driven key post talent selection system - Google Patents

Brain information driven key post talent selection system Download PDF

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CN117972471A
CN117972471A CN202311529830.9A CN202311529830A CN117972471A CN 117972471 A CN117972471 A CN 117972471A CN 202311529830 A CN202311529830 A CN 202311529830A CN 117972471 A CN117972471 A CN 117972471A
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毕路拯
史健廷
费炜杰
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Beijing Institute of Technology BIT
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Abstract

The invention discloses a brain information driven key post talent selection system, which comprises: the device comprises an acquisition module, a characteristic extraction module and a classification module; the acquisition module is used for acquiring brain information processing motion information and scalp brain electrical signals of the testee; the feature extraction module is used for processing motion information based on brain information and obtaining brain information processing motion feature vectors; preprocessing scalp electroencephalogram signals, and reducing the dimension of the preprocessed scalp electroencephalogram signals based on a trained variation self-encoder model to obtain dimension-reduced electroencephalogram feature vectors; and the classification module is used for carrying out standardization processing on the brain information processing motion feature vector and the brain electrical feature vector after dimension reduction and connecting the brain information processing motion feature vector and the brain electrical feature vector, and establishing a talent selection decision model through the classifier to obtain talent selection results. The invention provides a new thought for research and development of talent selection models, provides a new method and field for brain science research and application, and has important theoretical research and practical application values.

Description

Brain information driven key post talent selection system
Technical Field
The invention belongs to the technical fields of psychology, management science and neuroscience, and particularly relates to a brain information driven key post talent selection system.
Background
With the development of society, psychology mainly researching psychological activities and character behaviors is increasingly widely applied, and the demands of various industries on psychological measuring tools are also increasing. A great deal of research has been done at home and abroad in the fields of application testing, talent selection and post configuration. The earliest related studies currently seen were efficacy studies of the multi-way assay published in 1954 by handset and Duncan (Handyside & Duncan). The testee is a low-level supervisor of the enterprise, and whether the testee is hired or not is determined according to the evaluation result, wherein the evaluation method comprises paper pen test, two different main test interviews, a main test group interview (PANEL INTERVIEW) and recommendation information. The plug (Silzer) reported in 1986 a study to determine a predictor of manager success. The study involved 1749 administrators from different departments at different levels who used the california personality questionnaire (California Personality Inventory) and a series of cognitive tests. There are few tests available for personnel selection in domestic related research, and the tests commonly used in China at present include: the system comprises a Katel 16 personality questionnaires (16 PF), a Minnesota multiple personality questionnaires (MMPI), a basic professional ability test (GATB), a standard Rayleigh reasoning test (SPM) projection test, a theme system vision test, a Luo Xia ink test and the like.
However, most psychological tests and methods of professional prediction are related to social and cultural backgrounds, and the effectiveness of the method is difficult to ensure when the cultural background is left. Moreover, this approach first defines the various capabilities, while also proving that the psychological tests used can accurately measure these capabilities. Moreover, the traditional test adopts a question-answer mode or a chart mode, and the dynamic change rule of psychological activities can be rarely represented. And the traditional psychology combines the contents of the world view, the moral view and the like of people when researching psychological activities, so that great difficulty is brought to researching the relationship between the psychological activities of people and the capacities, the characters and the behavioral characteristics of people. Because the psychological activities of the people are characterized differently, the exposed ability and action behavior of the people are also characterized differently, thus a new research mode and thought can be provided.
Therefore, the psychological activity process can be regarded as a brain information processing process, a mathematical model of brain information processing movement is further provided, the movement rule is analyzed and revealed by using more mature experiments and theories in engineering, the rule is measured and researched, and an engineering type psychological activity research system is established.
In addition, in the field of psychology, brain-machine interface technology is widely used, which can help researchers to understand the cognitive and emotional processes of humans. Brain-computer interface (BCI) is a technology that allows direct conversion of human brain activity into computer instructions or control signals. One common brain-machine interface technique is implemented by measuring electroencephalogram (EEG) signals. In psychology, brain-machine interfaces have been applied in many aspects such as cognitive research, emotion research, mental disease research, etc., such as research of cognitive processes of attention, memory and decision, recognition of brain electrical activity patterns in different emotion states, diagnosis of diseases such as depression and Attention Deficit Hyperactivity Disorder (ADHD), etc. In general, brain-machine interface technology has great potential in psychological research.
The background shows that the talent selection decision method based on the brain information processing motion mathematical model and the brain-computer interface has important research value. Brain thinking activity is a special information processing exercise, and by researching brain information processing through the general rule of exercise, the exercise characteristic and the exposed behavior characteristic and capability characteristic of the characteristic can be revealed. In addition, by collecting and analyzing EEG signals, researchers can learn about the cognitive and emotional processes of humans and can acquire rich information from the cognitive and emotional processes, thereby providing more accurate talent selection decision results. Therefore, the invention provides a brain information driven key post talent selection system based on a mathematical model of brain information processing movement and brain-computer interface technology.
Disclosure of Invention
The invention aims to solve the defects of the prior art, and provides a brain information driven key post talent selection system which realizes talent selection of key posts through a mathematical model of brain information processing movement and brain-computer interface technology.
In order to achieve the above object, the present invention provides the following solutions:
a brain information driven key post talent selection system comprising: the device comprises an acquisition module, a characteristic extraction module and a classification module;
The acquisition module is used for acquiring brain information processing motion information and scalp brain electrical signals of the testee;
the feature extraction module is used for processing motion information based on the brain information to obtain brain information processing motion feature vectors; preprocessing the scalp electroencephalogram signal, and reducing the dimension of the preprocessed scalp electroencephalogram signal based on a trained variation self-encoder model to obtain a dimension-reduced electroencephalogram characteristic vector;
The classifying module is used for carrying out standardization processing on the brain information processing motion feature vector and the brain electricity feature vector after dimension reduction and connecting the brain information processing motion feature vector and the brain electricity feature vector, and establishing a talent selection decision model through a classifier to obtain talent selection results.
Preferably, the acquisition module comprises a brain information processing motion information acquisition unit and a scalp brain electrical signal acquisition unit;
The brain information processing movement information acquisition unit is used for continuously adding a manual work workload curve based on a testee to obtain the brain information processing movement information;
The scalp electroencephalogram signal acquisition unit is used for acquiring the scalp electroencephalogram signal of a testee during continuous addition manual operation based on an electroencephalogram acquisition system.
Preferably, the brain information processing motion feature vector includes an inertial feature, an elastic feature, an energy feature, a deviation degree, and an ability feature, which are respectively in the upper and lower half.
Preferably, in the feature extraction module, the process of obtaining the motion feature vector of brain information processing includes:
Based on the workload curve of the testee, obtaining each row coefficient of the workload in the upper half and the lower half by adopting a least square method And/>The formula is as follows:
in the method, in the process of the invention, Representing the average value of the total workload of the ith testee, q ij represents the workload of the jth row of the ith testee,Representing the total workload average of each subject,/>Represents the mean of the workload of each subject at row j, where i=1, 2,..4000, represents the number of subjects; j=1, 2,..30, representing the number of workload lines;
based on the line coefficients and the average value of the total workload of the ith testee, obtaining the total workload of the ith testee Estimation of expected workload of jth line under conditions/>The formula is as follows:
Obtaining a working amount reference difference based on the estimated working amount of a j-th row of a testee, the expected working amount of the j-th row and the average value and standard deviation of each row of difference of the working amounts of all testees, and carrying out standardization processing on the working amount reference difference;
Based on the presence of the subject And estimating the expected work amount of the j-th line under the condition and obtaining the brain information processing motion characteristic vector by the work amount reference difference after the normalization processing.
Preferably, in the feature extraction module, the variation self-encoder model includes an encoder and a decoder;
The encoder is used for encoding the scalp electroencephalogram signals to obtain potential space parameters; wherein the potential spatial parameters include a mean vector and a variance vector;
The decoder is configured to decode the potential spatial parameters to obtain reconstructed input data.
Preferably, training the variation self-encoder model, wherein the adopted loss function comprises reconstruction loss and KL divergence;
Based on the reconstruction loss, measuring a difference of the decoder output from an original input;
based on the KL divergence, a difference between a distribution of the encoder output and a standard normal distribution is measured.
Preferably, the formula of the normalization process is:
x: is the original feature vector;
x': normalized feature vectors;
rx: a 10-dimensional vector of the maximum values of the original features;
lx: a 10-dimensional vector of the minimum values of the original features;
rx': a 10-dimensional vector composed of maximum values of the features after normalization;
lx': normalized 10-dimensional vector of minimum values of features.
Preferably, the classifier is a regularized linear discriminant analysis classifier, and the mathematical expression is as follows:
y＝wTu
where u represents one sample of the input classifier, i.e., an n-dimensional feature vector, y is the classification result, and w is the projection matrix w.
Compared with the prior art, the invention has the beneficial effects that: the invention realizes talent selection of key posts through the mathematical model of brain information processing movement and brain-computer interface technology, is applicable to testees with various social culture backgrounds, various capacities, characters and behavioral characteristics, and has reliable results. The invention provides a new thought for research and development of talent selection models, is an important application of neuroscience, psychology and artificial intelligence in talent selection, provides a new method and field for brain science research and application, and has important theoretical research and practical application values.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the embodiments are briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a brain information driven key post talent selection system in an embodiment of the invention;
FIG. 2 is a schematic diagram of a brain information driven key post talent selection system in an embodiment of the invention;
FIG. 3 is a diagram showing the positions of the specified electrodes of the brain and scalp of a tested person according to the embodiment of the invention;
FIG. 4 is a schematic representation of a continuous addition operation in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of a workload graph in an embodiment of the present invention;
FIG. 6 is a graph illustrating the workload of different persons according to the embodiment of the present invention;
fig. 7 is a schematic diagram of a variable self-encoder used in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
As shown in fig. 1-2, a brain information driven key post talent selection system includes: the device comprises an acquisition module, a characteristic extraction module and a classification module;
The acquisition module is used for acquiring brain information processing motion information and scalp brain electrical signals of the testee;
the further implementation mode is that the acquisition module comprises a brain information processing movement information acquisition unit and a scalp brain electrical signal acquisition unit;
The brain information processing movement information acquisition unit is used for continuously adding a manual work workload curve based on the testee to obtain brain information processing movement information; specifically, 82 persons of a higher manager of the university and 164 persons of a research and development staff of the xx company are taken as training samples, 246 persons are taken as training samples, each person is subjected to continuous addition manual operation, a continuous addition operation table is shown in fig. 4, the operation amount is 30 lines, 15 lines are respectively arranged in the upper half and the lower half, the acquired two operation amount curves in the upper half and the lower half are brain information processing exercise information, and the operation amount curves are shown in fig. 5 and 6;
The scalp electroencephalogram signal acquisition unit is used for acquiring scalp electroencephalograms signals of a tested person during continuous addition manual operation based on the 64-channel electroencephalogram acquisition system while carrying out continuous addition manual operation, and a position diagram of scalp electrodes of the tested person is shown in fig. 3;
The feature extraction module is used for processing motion information based on brain information and obtaining brain information processing motion feature vectors; preprocessing scalp electroencephalogram signals, and reducing the dimension of the preprocessed scalp electroencephalogram signals based on a trained variation self-encoder model to obtain dimension-reduced electroencephalogram feature vectors;
In a further embodiment, the brain information processing motion feature vector includes 10 features including an inertial feature, an elastic feature, an energy feature, a degree of deviation, and an ability feature, respectively, in the upper and lower half.
In a further embodiment, in the feature extraction module, the process of obtaining the brain information processing motion feature vector is as follows:
based on the workload curve of the testee, a least square method is adopted to obtain each row coefficient of the workload in the upper half and the lower half AndThe formula is as follows:
in the method, in the process of the invention, Representing the average value of the total workload of the ith testee, q ij represents the workload of the jth row of the ith testee,Representing the total workload average of each subject,/>Represents the mean of the workload of each subject at row j, where i=1, 2,..4000, represents the number of subjects; j=1, 2,..30, representing the number of workload lines;
based on each line coefficient and the average value of the total work load of the ith testee, the obtained value is obtained Estimation of expected workload of jth line under conditions/>The formula is as follows:
in particular, q ij and The correlation coefficient between them can be calculated by the following formula:
table 1 shows the coefficients of the rows in the upper and lower halves And/>Is calculated as the result of:
TABLE 1
Obtaining a working amount reference difference based on the estimated working amount of the j-th row of the testee, the expected working amount of the j-th row and the average value and standard deviation of each row of difference of the working amounts of all testees, and carrying out standardization processing on the working amount reference difference;
in the method, in the process of the invention,
Δ j: the work amount reference difference is an actual measurement variable in the factor analysis.
Q j: setting the job load of the j-th row of the testee
Evaluation reference work amount (estimation of desired work amount) of the j-th row corresponding thereto
Average of the dispersion for each row of 4000 samples (all subject workload);
Standard deviation for each row delta of 4000 samples, j=1, 2, 30; n=4000
Based on the presence of the subjectAnd estimating the expected workload of the j line under the condition and obtaining the workload reference difference after normalization processing to obtain the brain information processing motion characteristic vector.
Specifically, the inertial feature amounts M u and M l of the brain information processing motion in the upper and lower half can be obtained according to the following formula:
The elastic feature amounts K u and K l of the brain information processing motion in the upper and lower half can be obtained according to the following equation:
the energy feature values E u and E l of the brain information processing movement in the upper and lower half can be obtained according to the following equation:
The capability feature quantities AV u and AV l of the brain information processing movement in the upper and lower half can be obtained according to the following formula:
in the method, in the process of the invention,
AV l is the capacity feature of brain information processing movement in the lower half;
AV u is the ability characteristic of brain information processing movement in the upper half;
q j is the job size of the j-th line.
The degree of deviation PF u and PF l in the upper and lower half can be obtained by the following formula:
q j is the j-th row workload of the testee;
The evaluation reference operation of the j-th row of the subject.
A further embodiment is that the process of preprocessing the electroencephalogram signal comprises bandpass filtering, downsampling, baseline correction, artifact filtering and common average reference. Wherein, the band-pass filtering intercepts the brain electrical signal of 0.5-20Hz, downsamples to 100Hz, and the artifact filtering adopts the FastICA method.
A further embodiment is that in the feature extraction module, a variation self-encoder (VAE) is used to reduce the dimension of the electroencephalogram signal: the variational self-encoder structure is shown in fig. 7 and is a deep learning model, the main idea is to encode input data into parameters of potential space through a neural network, then sample the parameters to obtain potential variables, and finally decode the potential variables into reconstructed input data through another neural network.
Specifically, the variation self-encoder model includes an encoder and a decoder;
The encoder is used for encoding scalp electroencephalogram signals to obtain potential space parameters; wherein the potential spatial parameters include a mean vector and a variance vector;
The decoder is used for decoding the potential space parameters to obtain reconstructed input data;
a further embodiment is that the training variation is from an encoder model, and the loss function employed includes reconstruction loss and KL divergence;
Based on the reconstruction loss, measuring a difference of the decoder output from the original input;
Based on the KL divergence, the difference between the distribution of the encoder output and the standard normal distribution is measured.
The optimizer used in this embodiment is an SGD optimizer, and in the training cycle, we first obtain parameters of the potential space through the encoder, then sample the parameters to obtain potential variables, then obtain reconstructed input data through the decoder, and finally calculate the loss function and update the model parameters.
After training is completed, the new test sample can be subjected to dimension reduction, input data are converted into parameters of potential space through an encoder, and then the mean value vector is taken as the dimension reduced data. The dimension of the feature vector of the brain electrical signal subjected to dimension reduction is 1 x 20.
And the classification module is used for carrying out standardization processing on the brain information processing motion feature vector and the brain electrical feature vector after dimension reduction and connecting the brain information processing motion feature vector and the brain electrical feature vector, and establishing a talent selection decision model through the classifier to obtain talent selection results.
In a further embodiment, the brain information processing motion feature vector (10 dimensions) is not identical to the reduced brain electrical feature vector (20 dimensions) in terms of the value of each feature, and therefore each feature is normalized so that each feature is located at [ -1,1].
The formula of normalization processing is:
x: is the original feature vector;
x': normalized feature vectors;
rx: a 10-dimensional vector of the maximum values of the original features;
lx: a 10-dimensional vector of the minimum values of the original features;
rx': a 10-dimensional vector composed of maximum values of the features after normalization;
lx': normalized 10-dimensional vector of minimum values of features.
The further implementation mode is that the two normalized feature vectors are connected and input into a classifier, a talent selection decision model is established, and a final talent selection classification result is obtained; the classifier uses Regularized Linear Discriminant Analysis (RLDA). The mathematical expression is as follows:
y＝wTu
where u represents one sample of the input classifier, i.e., an n-dimensional feature vector, y is the classification result, and w is the projection matrix w.
The projection matrix w can be calculated by the following formula
Wherein mu e and mu n represent the mean of all target training samples and the mean of all non-target training samples, respectively, and Sigma' w is a regularized intra-class dispersion matrix that can be calculated from the following equation
∑′w＝(1-λ)∑w+λvI
Sigma w is an intra-class dispersion matrix that can be obtained by summing the covariance matrices of the two classes of samples. λ is an adjustable parameter, the value range is (0, 1), I is the identity matrix, trace () represents the trace of the matrix, and d is the dimension of the in-class dispersion matrix Σ w.
RLDA is performed by comparing the y value with a threshold Tr. The testees in this embodiment include senior college manager staff and company research staff, and in the present invention, RLDA determines that the current sample is "manager" when y > Tr, and determines that the current sample is "research staff" when y < Tr.
The above embodiments are merely illustrative of the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, but various modifications and improvements made by those skilled in the art to which the present invention pertains are made without departing from the spirit of the present invention, and all modifications and improvements fall within the scope of the present invention as defined in the appended claims.

Claims (8)

1. A brain information driven key post talent selection system, comprising: the device comprises an acquisition module, a characteristic extraction module and a classification module;
The acquisition module is used for acquiring brain information processing motion information and scalp brain electrical signals of the testee;
the feature extraction module is used for processing motion information based on the brain information to obtain brain information processing motion feature vectors; preprocessing the scalp electroencephalogram signal, and reducing the dimension of the preprocessed scalp electroencephalogram signal based on a trained variation self-encoder model to obtain a dimension-reduced electroencephalogram characteristic vector;
The classifying module is used for carrying out standardization processing on the brain information processing motion feature vector and the brain electricity feature vector after dimension reduction and connecting the brain information processing motion feature vector and the brain electricity feature vector, and establishing a talent selection decision model through a classifier to obtain talent selection results.
2. The brain information driven key post talent selection system according to claim 1, wherein the acquisition module comprises a brain information processing movement information acquisition unit and a scalp brain electrical signal acquisition unit;
The brain information processing movement information acquisition unit is used for continuously adding a manual work workload curve based on a testee to obtain the brain information processing movement information;
The scalp electroencephalogram signal acquisition unit is used for acquiring the scalp electroencephalogram signal of a testee during continuous addition manual operation based on an electroencephalogram acquisition system.
3. The brain-information-driven key post talent selection system according to claim 2, wherein the brain-information-processing motion feature vector includes an inertial feature, an elastic feature, an energy feature, a degree of deviation, and a capacity feature, respectively, in the upper and lower halves.
4. The brain-information-driven key post talent selection system according to claim 3, wherein in the feature extraction module, the process of obtaining the brain-information-processing motion feature vector is as follows:
Based on the workload curve of the testee, obtaining each row coefficient of the workload in the upper half and the lower half by adopting a least square method And/>The formula is as follows:
in the method, in the process of the invention, Representing the average value of the total workload of the ith testee, q ij represents the workload of the jth row of the ith testee,Representing the total workload average of each subject,/>Represents the mean of the workload of each subject at row j, where i=1, 2,..4000, represents the number of subjects; j=1, 2,..30, representing the number of workload lines;
based on the line coefficients and the average value of the total workload of the ith testee, obtaining the total workload of the ith testee Estimation of expected workload of jth line under conditions/>The formula is as follows:
Obtaining a working amount reference difference based on the estimated working amount of a j-th row of a testee, the expected working amount of the j-th row and the average value and standard deviation of each row of difference of the working amounts of all testees, and carrying out standardization processing on the working amount reference difference;
Based on the presence of the subject And estimating the expected work amount of the j-th line under the condition and obtaining the brain information processing motion characteristic vector by the work amount reference difference after the normalization processing.
5. The brain-information-driven critical post talent selection system of claim 1, wherein in said feature extraction module, said variation self-encoder model comprises an encoder and a decoder;
The encoder is used for encoding the scalp electroencephalogram signals to obtain potential space parameters; wherein the potential spatial parameters include a mean vector and a variance vector;
The decoder is configured to decode the potential spatial parameters to obtain reconstructed input data.
6. The brain-information-driven critical post talent selection system of claim 5, wherein training the variational self-encoder model uses a loss function comprising reconstruction loss and KL divergence;
Based on the reconstruction loss, measuring a difference of the decoder output from an original input;
based on the KL divergence, a difference between a distribution of the encoder output and a standard normal distribution is measured.
7. The brain-information-driven key post talent selection system of claim 1, wherein the formula of the normalization process is:
x: is the original feature vector;
x': normalized feature vectors;
rx: a 10-dimensional vector of the maximum values of the original features;
lx: a 10-dimensional vector of the minimum values of the original features;
rx': a 10-dimensional vector composed of maximum values of the features after normalization;
Lx: normalized 10-dimensional vector of minimum values of features.
8. The brain-information-driven key post talent selection system of claim 7, wherein the classifier employs a regularized linear discriminant analysis classifier with the mathematical expression:
y＝wTu
where u represents one sample of the input classifier, i.e., an n-dimensional feature vector, y is the classification result, and w is the projection matrix w.
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