CN109567830B - Personality measuring method and system based on neural response - Google Patents
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Abstract
The embodiment of the invention provides a personality measuring method and system based on neural response, wherein the provided method comprises the following steps: the method comprises the steps of playing personality test materials in a preset material library to a testee, and receiving electroencephalogram signals of the testee; extracting response features in the electroencephalogram signals to construct response feature vectors; inputting the response characteristic vector into a preset personality trait regression model, and calculating the personality trait index of the testee through the personality trait regression model; the response characteristic is the average value of the EEG components related to the personality of the testee in a preset time range after the testee observes a preset personality test material. The method provided by the embodiment of the invention induces the relative cognitive state of the personality traits of the testee by using the specific material, records the electroencephalogram signal in the process, further judges the personality traits of the testee according to the electroencephalogram signal, and can carry out more accurate and real evaluation on the personality traits based on objective electroencephalogram data.
Description
Technical Field
The embodiment of the invention relates to engineering psychology, in particular to a personality measuring method and system based on neural response.
Background
Personality is an important concept used by psychology to describe the differences in cognition and behavior of an individual, reflecting the unique patterns of thought, emotion and behavior that one distinguishes from others. How to accurately measure personality is an important topic in the field of psychology and is also widely concerned by the public. The current common personality description method is five personality. It divides personality traits into five dimensions: extroversion (outward), which represents the outward level of the personality; the nerve (neroticism), which represents the level of emotional stability; openness (openness), which represents the level of patency of a personality; humanity (acquiescence) is preferred, meaning the level of affinity for a personality; conscientiousness (consciientiosness), which represents a level of scrupulous personality.
In the prior art, the main two methods for personality testing are: self-presenting scale personality tests and personality projection tests. However, in the self-display scale method, the subject evaluates his personality traits according to his/her mind, and the result of this measurement method is determined by the personal report of the subject, and thus, when the target individual is in an environment such as a selection competition, the subject is easily disturbed by subjective falsification. In the projection test and situation test method, the testee needs to freely describe the material or react under a certain situation, and the testee observes the description content or the reaction so as to deduce the personality of the testee. This type of measurement also lacks objective evaluation criteria and requires a significant expenditure of time and labor.
In the prior art, an objective and automatic personality testing method is not provided, so that the testee can be visually and quickly measured for the personality, meanwhile, in the personality measurement in the prior art, testing materials are often not universal, and due to different cultural backgrounds of the testee, a certain deviation can be generated in a testing result.
Disclosure of Invention
The embodiment of the invention provides a personality measuring method and system based on neural response, which are used for solving the problem that no objective and automatic personality testing method exists in the prior art to visually and quickly measure the personality of a tester.
In a first aspect, an embodiment of the present invention provides a personality measurement method based on a neural response, including:
the method comprises the steps of playing personality test materials in a preset material library to a testee, and receiving electroencephalogram signals of the testee;
extracting response features in the electroencephalogram signals to construct response feature vectors; and inputting the response characteristic vector into a preset personality trait regression model, and calculating the personality trait index of the testee through the personality trait regression model.
The response characteristic is the average value of key electroencephalogram components of the testee in a preset time range after the testee observes a preset personality test material.
In a second aspect, an embodiment of the present invention provides a personality measurement system based on neural response, including:
the signal receiving module is used for playing personality test materials in a preset material library to a testee and receiving the electroencephalogram signals of the testee;
the characteristic extraction module is used for extracting response characteristics in the electroencephalogram signals to construct response characteristic vectors;
the calculation module is used for inputting the response characteristic vector into a preset personality trait regression model and calculating the personality trait index of the testee through the personality trait regression model;
the response characteristic is the average value of key electroencephalogram components of the testee in a preset time range after the testee observes a preset personality test material.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the neural response-based personality measurement method provided in the first aspect.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the neural response-based personality measurement method provided in the first aspect.
According to the method provided by the embodiment of the invention, the cognitive state related to the personality traits of the testee is induced by using the specific material, the electroencephalogram signal in the process is recorded, and then the personality traits of the testee are judged according to the electroencephalogram signal.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a personality measurement method based on neural response according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a personality measurement system based on neural response according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic flow chart of a personality measurement method based on neural response according to an embodiment of the present invention, where the provided method includes:
s1, playing personality test materials in a preset material library for a testee, and receiving electroencephalogram signals of the testee;
s2, extracting response features in the electroencephalogram signals to construct response feature vectors;
s3, inputting the response characteristic vector into a preset personality trait regression model, and calculating the personality trait index of the testee through the personality trait regression model;
the response characteristic is the average value of key electroencephalogram components of the testee in a preset time range after the testee observes a preset personality test material.
Specifically, taking 20 typical positive and negative emotions related in a positive emotion negative emotion scale (PANAS) as an example, for a new tested person, selecting 40 materials from a pre-constructed material library according to each emotion to play, obtaining electroencephalogram signals induced by the tested person in a test, simultaneously carrying out feature extraction on the collected electroencephalogram signals, obtaining response features of the tested person on the induced materials, aligning the electroencephalogram responses according to the occurrence moments of the materials, and carrying out time domain superposition averaging, wherein the superposition averaging is carried out on the materials under different emotion type attributes respectively. The important points are, but not limited to, delta (1-3Hz), theta (4-8Hz), alpha (8-13Hz), beta (14-30Hz), gamma (30-50Hz) and other band energies after the presentation of the evoked materials, and representative brain electrical components such as an early negative component EPN about 200ms after the presentation of the stimulation, a late brain electrical positive component LPP about 400ms, and a late negative component N400 about 400 + 700ms after the presentation of the stimulation. The extracted electroencephalogram features are mean values of the electroencephalogram features obtained by certain specific electrodes in electroencephalogram response induced by materials under certain emotion types within a certain specific time period. And forming a characteristic vector by using the event-related potential response characteristics of the testee to different inducing materials as input data of a personality trait regression model, and calculating the personality trait index of the testee through the personality trait regression model.
In the specific implementation, the formula is mainly used:
calculating to obtain personality trait index of the testee, wherein S isiA score index of the i-th personality dimension of the testee, aikFor weighting the combining coefficients, N is the number of features, fikAre elements in the characteristic group of the human personality trait related electroencephalogram respectively. And establishing a multiple regression equation by using the personality scale evaluation index of the specific evaluation scene as a dependent variable so as to obtain the weighted combination coefficient through learning. After training to obtain coefficients, new subjects measure the electroencephalogram and extract features, and incorporate the characteristics into the electroencephalogramAnd calculating by a corresponding formula to obtain the score index of a certain personality dimension.
And can further interpret the personality traits of the subject.
According to the method, the cognitive state related to the personality traits of the testee is induced by using the specific materials, the electroencephalogram signals in the process are recorded, the personality traits of the testee are judged according to the electroencephalogram signals, the problem that the traditional personality measuring method based on the objective electroencephalogram data is influenced by the subjective factors of the testee can be avoided, and the personality traits can be more accurately and truly evaluated.
On the basis of the above embodiment, before the step of playing the personality test material in the preset material library to the testee, the method further includes: selecting a plurality of materials as alternative materials for each emotion in the positive and negative emotion scale; scoring each material in the alternative materials, selecting a preset number of materials with highest score ranking, and constructing a material library; wherein the categories of the material include but are not limited to: one or more of pictures, sounds, text or video.
Specifically, in this embodiment, the material library includes but is not limited to evoked materials in multiple states such as emotion and cognition, and the method for establishing the database will be described herein by taking only the emotional evoked materials as an example. Taking 20 typical positive and negative emotions related in a positive and negative emotion rating scale (PANAS) as an example, the establishment of a material library comprises three steps, firstly, aiming at each emotion, 100 parts of pictures or sound materials are selected as alternatives; after that, 500 persons with different cultural backgrounds and different education degrees (wherein the education degrees are divided into 6 types of unexeducation, primary school degree, junior middle degree, high middle degree, university degree and student degree; the language cultural backgrounds are divided into a plurality of types including but not limited to Chinese, English, Japanese, German, Spanish and the like) are invited to draw 1 to 7 points on the degree of inducing the target emotion of the pictures and the sound phoneme materials by means of network questionnaires, interviews and the like, wherein 1 point is that the corresponding emotion can not be induced at all, and 7 points are that the corresponding emotion can be induced at all; each emotion will eventually be assigned to the top 20 sound material and the top 20 picture material in the library, and in this embodiment, the material category may include but is not limited to one or more of pictures, sounds, words, or videos.
On the basis of the above embodiment, before the step of inputting the response feature vector into the preset personality trait regression model, the method further includes: constructing a training sample library according to the electroencephalogram data of a plurality of persons with different characteristics and corresponding behavioristic personality scores; and training a regression model through the training sample library.
The key electroencephalogram components specifically comprise: the energy of frequency bands of delta, theta, alpha, beta and gamma wave bands in the electroencephalogram signals, an early negative component EPN of 200ms after being stimulated by the material, a late electroencephalogram positive component LPP of 400ms, and a late negative component N400 of 700ms after being stimulated by the material.
Specifically, in this embodiment, a personality trait regression model is first selected to be constructed, and the specific model construction action includes presenting corresponding evoked materials to more than 500 testees with different education degrees and different culture backgrounds, recording electroencephalograms of different electrode channels distributed in different brain areas for each tester, and extracting characteristics including, but not limited to, time domain, frequency domain and space domain in the electroencephalograms for personality trait identification. The frequency band energy of delta (1-3Hz), theta (4-8Hz), alpha (8-13Hz), beta (14-30Hz), gamma (30-50Hz) and the like in the electroencephalogram signal, the early negative component EPN about 200ms after the stimulation appears, the late electroencephalogram positive component LPP about 400ms, and the late negative component N400 about 400 + 700ms are the characteristics planned to be extracted but not limited to the characteristics. Finally, by collecting the electroencephalogram data of more than 500 different personality trait testees and the corresponding behavioristic personality scores of the testees, respectively training a regression model in the multiple dimensions, namely the personality trait regression model.
On the basis of the above embodiment, the step of playing the personality testing material in the preset material library to the testee specifically includes: selecting a preset number of personality test materials from the material library; playing the personality test material through a playing device; wherein the cultural background at least comprises an educational stratification level and a language background.
The electroencephalogram signal at least comprises: electroencephalogram signals acquired from the Fz, Cz, Pz, Oz, O1, O2, C3, C4, F3 and F4 electrodes.
In particular, it is contemplated that the content of the library of stimulus material may have an effect on the outcome of the personality test. For example, when the material is characters, for a user who cannot understand the meaning of the stimulus due to insufficient education or not learning the language, the difference of the characters carrying the meaning cannot be recognized and processed by the brain, and thus the personality recognition method cannot be used; in addition, for a picture or video stimulus, persons may feel interesting for the same scene and persons may feel offensive due to possible cultural background differences of the user, resulting in a stimulus that does not successfully induce the emotional color that the system designer originally intended to induce, resulting in possible bias in the result. Therefore, in the embodiment, in the process of constructing the material library, materials with universality for people with different cultural backgrounds are selected to construct the material library, and when the test materials are selected, only the materials in the material library need to be randomly selected, and a preset number of materials are selected to be played for a testee, so that the measurement is performed. Each tested person at least acquires 8 channels of electroencephalogram signals, the covering electrodes comprise Fz, Cz, Pz, Oz, O1, O2, C3, C4, F3 and F4, and the sampling rate is not lower than 200 Hz. The emotion detection and identification accuracy can be improved by increasing the number of the acquisition channels.
In summary, the method provided by the embodiment of the invention provides a set of automatic detection method for rapidly and accurately measuring personality traits based on electroencephalogram signal analysis, further, stimulation materials with universality are provided for testees, and the effectiveness of stimulation induction is guaranteed, so that the reliability of results and the wide applicability of the system are guaranteed, meanwhile, the diversity of the types of the materials guarantees the diversity of the types of stimulation, the automatic personality measurement method applicable across education degrees and across cultural backgrounds is realized, and the method has important application significance in the fields of personnel selection, professional planning and the like.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a personality measurement system based on neural response according to an embodiment of the present invention, where the system includes: a signal receiving module 21, a feature extraction module 22 and a calculation module 23.
The signal receiving module 21 is configured to play a personality test material in a preset material library to a subject, and receive an electroencephalogram signal of the subject;
the feature extraction module 22 is configured to extract response features in the electroencephalogram signal to construct a response feature vector;
the calculation module 23 is configured to input the response feature vector into a preset personality trait regression model, and calculate a personality trait index of the subject through the personality trait regression model;
the response characteristic is the average value of the key electroencephalogram characteristics of the testee in a preset time range after the testee observes a preset personality test material.
Specifically, for a new testee, the signal receiving module selects 40 materials from a pre-constructed material library for each emotion to play, electroencephalogram signals induced by the testee in the test are obtained, meanwhile, the characteristics of the acquired electroencephalogram signals are extracted, response characteristics of the testee to the induced materials are obtained, the characteristics extraction module aligns the electroencephalogram responses according to the occurrence time of the materials and performs time domain superposition averaging, and the superposition averaging is performed on the materials under different emotion type attributes respectively. The important points are, but not limited to, delta (1-3Hz), theta (4-8Hz), alpha (8-13Hz), beta (14-30Hz), gamma (30-50Hz) and other band energies after the presentation of the evoked materials, and representative brain electrical components such as an early negative component EPN about 200ms after the presentation of the stimulation, a late brain electrical positive component LPP about 400ms, and a late negative component N400 about 400 + 700ms after the presentation of the stimulation. The extracted electroencephalogram characteristics are average values of the electroencephalogram characteristics obtained by certain specific electrodes in electroencephalogram response induced by materials under certain emotion types within a certain specific time period. And forming a characteristic vector by using the event-related potential response characteristics of the testee to different inducing materials as input data of a personality trait regression model, and calculating the personality trait index of the testee through the personality trait regression model.
In the specific implementation, the formula is mainly used:
calculating to obtain personality trait index of the testee, wherein S isiA score index of the i-th personality dimension of the testee, aikFor weighting the combining coefficients, N is the number of features, fikAre elements in the characteristic group of the human personality trait related electroencephalogram respectively. And establishing a multiple regression equation by using the personality scale evaluation index of the specific evaluation scene as a dependent variable so as to obtain the weighted combination coefficient through learning. And calculating to obtain the personality trait score in a corresponding scale according to the obtained coefficient, and further interpreting the personality trait of the testee.
Through the system, the cognitive state related to the personality traits of the testee is induced by using specific materials, the electroencephalogram signals in the process are recorded, the personality traits of the testee are judged according to the electroencephalogram signals, the problem that the traditional personality measuring method is influenced by subjective factors of the testee can be avoided based on objective electroencephalogram data, and the personality traits are more accurately and truly evaluated.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the provided device includes: a processor (processor)301, a communication Interface (communication Interface)302, a memory (memory)303 and a bus 304, wherein the processor 301, the communication Interface 302 and the memory 303 complete communication with each other through the bus 304. Processor 301 may call logic instructions in memory 303 to perform methods including, for example: the method comprises the steps of playing personality test materials in a preset material library to a testee, and receiving electroencephalogram signals of the testee; extracting response features in the electroencephalogram signals to construct response feature vectors; inputting the response characteristic vector into a preset personality trait regression model, and calculating the personality trait index of the testee through the personality trait regression model; the response characteristic is the average value of key electroencephalogram components of the testee in a preset time range after the testee observes a preset personality test material.
An embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the method provided by the above method embodiments, for example, the method includes: the method comprises the steps of playing personality test materials in a preset material library to a testee, and receiving electroencephalogram signals of the testee; extracting response features in the electroencephalogram signals to construct response feature vectors; inputting the response characteristic vector into a preset personality trait regression model, and calculating the personality trait index of the testee through the personality trait regression model; the response characteristic is the average value of key electroencephalogram components of the testee in a preset time range after the testee observes a preset personality test material.
The present embodiments provide a non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the methods provided by the above method embodiments, for example, including: the method comprises the steps of playing personality test materials in a preset material library to a testee, and receiving electroencephalogram signals of the testee; extracting response features in the electroencephalogram signals to construct response feature vectors; inputting the response characteristic vector into a preset personality trait regression model, and calculating the personality trait index of the testee through the personality trait regression model; the response characteristic is the average value of key electroencephalogram components of the testee in a preset time range after the testee observes a preset personality test material.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A personality measurement method based on neural responses, comprising:
the method comprises the steps of playing personality test materials in a preset material library to a testee, and receiving electroencephalogram signals of the testee;
extracting response features in the electroencephalogram signals to construct response feature vectors;
inputting the response characteristic vector into a preset personality trait regression model, and calculating the personality trait index of the testee through the personality trait regression model;
the response characteristic is the average value of key electroencephalogram components of the testee in a preset time range after the testee observes a preset personality test material;
before the step of playing the personality testing material in the preset material library to the testee, the method further comprises the following steps:
selecting a plurality of materials as alternative materials for each emotion in the positive and negative emotion scale;
scoring each material in the alternative materials, selecting a preset number of materials with highest score ranking, and constructing a material library;
wherein, the testee specifically is: people with different cultural backgrounds and different education degrees.
2. The method of claim 1, wherein the categories of material include, but are not limited to: one or more of pictures, sounds, text or video.
3. The method of claim 1, wherein the step of inputting the response feature vector into a pre-defined personality trait regression model is preceded by the step of:
constructing a training sample library according to the electroencephalogram data of a plurality of persons with different personality traits and corresponding behavioral personality scores;
and training the personality trait regression model through the training sample library.
4. The method of claim 2, wherein the key brain electrical component data specifically comprises:
the energy of frequency bands of delta, theta, alpha, beta and gamma wave bands in the electroencephalogram signals, an early negative component EPN of 200ms after being stimulated by the material, a late electroencephalogram positive component LPP of 400ms, and a late negative component N400 of 700ms after being stimulated by the material.
5. The method of claim 1, wherein the brain electrical signals comprise at least:
electroencephalogram signals acquired from the Fz, Cz, Pz, Oz, O1, O2, C3, C4, F3 and F4 electrodes.
6. The method according to claim 1, wherein the step of playing the personality test material in the predetermined material library to the subject specifically comprises:
randomly selecting a preset number of personality test materials from the material library;
and playing the personality test material through a playing device.
7. A personality measurement system based on neural responses, comprising:
the signal receiving module is used for playing personality test materials in a preset material library to a testee and receiving the electroencephalogram signals of the testee;
the characteristic extraction module is used for extracting response characteristics in the electroencephalogram signals to construct response characteristic vectors;
the calculation module is used for inputting the response characteristic vector into a preset personality trait regression model and calculating the personality trait index of the testee through the personality trait regression model;
the response characteristic is the average value of key electroencephalogram components of the testee in a preset time range after the testee observes a preset personality test material;
before the step of playing the personality testing material in the preset material library to the testee, the method further comprises the following steps:
selecting a plurality of materials as alternative materials for each emotion in the positive and negative emotion scale;
scoring each material in the alternative materials, selecting a preset number of materials with highest score ranking, and constructing a material library;
wherein, the testee specifically is: people with different cultural backgrounds and different education degrees.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method for personality measurement based on neurological response of any of claims 1-6.
9. A non-transitory computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for personality measurement based on neural responses according to any one of claims 1 to 6.
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