Personality traits identification method and system

Abstract

The invention relates to a personality characteristic identification method and system. The method comprises the following steps: constructing a time stress inducing method, and designing a risk decision task under time stress; collecting electroencephalogram data of the testee during the risk decision-making task under the time pressure; after preprocessing the electroencephalogram data, analyzing the electroencephalogram data of a brain region corresponding to a preset electrode to obtain a preset number of relative power characteristics; and establishing a personality characteristic identification model based on an SVM classification model, and completing training and verification of the personality characteristic identification model based on the relative power characteristics. The method provides new theoretical basis and technical support for personality identification research, can be used for personnel selection of emergency situation processing tasks, and has important theoretical and application values for guaranteeing the safety and reliability of a man-machine system from the source.

Description

Background Art

[0002] When faced with emergencies such as aviation emergency flights and catastrophic accidents, operators are under great time pressure. Under such pressure, individuals need to make quick and correct decisions, which often involve risk judgment. Once a decision is wrong, it may even cause a tragedy of aircraft crash and death. Risk decision-making refers to the choice that an individual makes that may bring gains or losses when facing uncertain situations. Individuals with different personality traits have significant differences in risk decision-making behavior under time pressure.

[0003] Personality traits are relatively stable psychological characteristics that individuals exhibit in terms of behavior, thinking, and emotion, which have an important impact on individuals' cognitive processes and decision-making behaviors. In recent years, researchers have paid more and more attention to the relationship between personality traits and risk decision-making. However, traditional scale measurement methods have certain limitations in personality identification, such as strong subjectivity and susceptibility to situational factors. Therefore, it is of great significance to explore an objective and effective personality identification method.

[0004] As a non-invasive, high-temporal-resolution method for detecting brain activity, electroencephalography (EEG) has been widely used in psychology, neuroscience, and other fields. EEG signals contain rich information about brain activity and can reflect an individual's cognitive process in a task. Previous studies have shown that there is a certain correlation between EEG characteristics and personality traits. However, the current research on the relationship between EEG characteristics and personality traits in risk decision-making tasks under time pressure is still insufficient.

[0005] Therefore, one or more methods are needed to solve the above problems.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0007] The purpose of the present disclosure is to provide a personality trait identification method, system, electronic device and computer-readable storage medium, thereby overcoming one or more problems caused by limitations and defects of related technologies at least to a certain extent.

[0008] According to one aspect of the present disclosure, a personality trait identification method is provided, comprising:

[0009] Construct a time pressure induction method and design risk decision-making tasks under time pressure;

[0010] Collect EEG data of the subjects when they are performing risk decision-making tasks under time pressure;

[0011] After preprocessing the EEG data, analyzing the EEG data of the brain area corresponding to the preset electrodes to obtain a preset number of relative power features;

[0012] A personality trait recognition model based on the SVM classification model is established, and training and verification of the personality trait recognition model are completed based on the relative power feature.

[0013] In an exemplary embodiment of the present disclosure, the time pressure inducing method in the method further comprises:

[0014] The average time taken by the subjects to complete the preset decision-making task was collected and calculated, and 80% of the average time was set as the subject time under time pressure.

[0015] In an exemplary embodiment of the present disclosure, the risk decision-making task under time pressure of the method further includes:

[0016] Based on the preset risk investment options and alternative risk options, risk decision-making tasks are generated under no time pressure and under time pressure respectively.

[0017] In an exemplary embodiment of the present disclosure, the method further includes:

[0018] In the risk decision-making task under time pressure, the test subjects need to fill in the time pressure scale after completing the risk decision-making task to evaluate the time pressure they feel during the experiment.

[0019] In an exemplary embodiment of the present disclosure, the method further includes:

[0020] Based on the preset EEG acquisition equipment, multi-channel EEG data of the test subjects are collected when they perform risk decision-making tasks under time pressure.

[0021] In an exemplary embodiment of the present disclosure, the method further includes:

[0022] After preprocessing the EEG data, the EEG data of the frontal area, central area, parietal area and occipital area of ​​the brain corresponding to the preset electrodes are collected;

[0023] The AR power spectrum was used to calculate the relative power of the EEG data in five EEG frequency bands, namely, δ (1-4 Hz), θ (4-7 Hz), α (8-14 Hz), β (15-30 Hz), and γ (31-45 Hz), to obtain a preset number of relative power features.

[0024] In an exemplary embodiment of the present disclosure, the method further includes:

[0025] Establishing a personality trait recognition model based on the SVM classification model, wherein the personality trait recognition model includes two types: risk seeking type and risk aversion type;

[0026] completing training and validating the personality trait recognition model based on the relative power feature;

[0027] Personality trait recognition is achieved based on the personality trait recognition model.

[0028] In one aspect of the present disclosure, a personality trait identification system is provided, comprising:

[0029] The risk decision-making task module under time pressure is used to construct a time pressure induction method and design a risk decision-making task under time pressure;

[0030] The EEG data acquisition module is used to collect EEG data of the test subject during the risk decision-making task under time pressure;

[0031] An EEG data processing module is used to analyze the EEG data of the brain area corresponding to the preset electrodes after preprocessing the EEG data to obtain a preset number of relative power characteristics;

[0032] The risk decision-making personality trait identification module is used to establish a personality trait identification model based on the SVM classification model, and complete the training and verification of the personality trait identification model based on the relative power characteristics.

[0033] In one aspect of the present disclosure, there is provided an electronic device, comprising:

[0034] Processor; and

[0035] A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method according to any one of the above items.

[0036] In one aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method according to any one of the above items is implemented.

[0037] A personality trait identification method in an exemplary embodiment of the present disclosure, wherein the method includes: constructing a time pressure inducing method and designing a risk decision-making task under time pressure; collecting EEG data of the test subject during the risk decision-making task under time pressure; after preprocessing the EEG data, analyzing the EEG data of the brain area corresponding to the preset electrode to obtain a preset number of relative power features; establishing a personality trait identification model based on the SVM classification model, and completing the training and verification of the personality trait identification model based on the relative power features. The present disclosure provides a new theoretical basis and technical support for personality identification research, which can be used for the selection of personnel for emergency situation handling tasks, and has important theoretical and application value for ensuring the safety and reliability of the human-machine system from the root.

[0038] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and other features and advantages of the present disclosure will become more apparent by describing in detail example embodiments thereof with reference to the attached drawings.

[0040] Figure 1 A flowchart of a personality trait identification method according to an exemplary embodiment of the present disclosure is shown;

[0041] Figure 2 A schematic diagram of an application scenario of a personality trait identification method according to an exemplary embodiment of the present disclosure is shown;

[0042] Figure 3 A structural block diagram of a personality trait recognition system according to an exemplary embodiment of the present disclosure is shown;

[0043] Figure 4 A block diagram schematically shows an electronic device according to an exemplary embodiment of the present disclosure;

[0044] Figure 5 A schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0045] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

[0046] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, materials, systems, steps, etc. may be adopted. In other cases, known structures, methods, systems, implementations, materials or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0047] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or these functional entities or parts of these functional entities may be implemented in one or more software hardened modules, or these functional entities may be implemented in different networks and / or processor systems and / or microcontroller systems.

[0048] In this exemplary embodiment, a personality trait identification method is first provided; Figure 1 As shown in , the personality trait identification method may include the following steps:

[0049] Step S110, constructing a time pressure inducing method and designing a risk decision-making task under time pressure;

[0050] Step S120, collecting EEG data of the test subject during the risk decision-making task under time pressure;

[0051] Step S130, after preprocessing the EEG data, analyzing the EEG data of the brain area corresponding to the preset electrodes to obtain a preset number of relative power features;

[0052] Step S140, establishing a personality trait recognition model based on the SVM classification model, and completing the training and verification of the personality trait recognition model based on the relative power feature.

[0053] A personality trait identification method in an exemplary embodiment of the present disclosure, wherein the method includes: constructing a time pressure inducing method and designing a risk decision-making task under time pressure; collecting EEG data of the test subject during the risk decision-making task under time pressure; after preprocessing the EEG data, analyzing the EEG data of the brain area corresponding to the preset electrode to obtain a preset number of relative power features; establishing a personality trait identification model based on the SVM classification model, and completing the training and verification of the personality trait identification model based on the relative power features. The present disclosure provides a new theoretical basis and technical support for personality identification research, which can be used for the selection of personnel for emergency situation handling tasks, and has important theoretical and application value for ensuring the safety and reliability of the human-machine system from the root.

[0054] Next, a personality trait identification method in this exemplary embodiment will be further described.

[0055] Embodiment 1:

[0056] In step S110, a time pressure inducing method may be constructed, and a risk decision-making task under time pressure may be designed.

[0057] In the embodiment of this example, the time pressure inducing method in the method further includes:

[0058] The average time taken by the subjects to complete the preset decision-making task was collected and calculated, and 80% of the average time was set as the subject time under time pressure.

[0059] In the embodiment of this example, the risk decision-making task under time pressure of the method further includes:

[0060] Based on the preset risk investment options and alternative risk options, risk decision-making tasks are generated under no time pressure and under time pressure respectively.

[0061] In the embodiment of this example, the method further comprises:

[0062] In the risk decision-making task under time pressure, the test subjects need to fill in the time pressure scale after completing the risk decision-making task to evaluate the time pressure they feel during the experiment.

[0063] In step S120, EEG data of the test subject during the risk decision-making task under time pressure may be collected.

[0064] In the embodiment of this example, the method further comprises:

[0065] Based on the preset EEG acquisition equipment, multi-channel EEG data of the test subjects are collected when they perform risk decision-making tasks under time pressure.

[0066] In step S130, after preprocessing the EEG data, the EEG data of the brain area corresponding to the preset electrodes may be analyzed to obtain a preset number of relative power features.

[0067] In the embodiment of this example, the method further comprises:

[0068] After preprocessing the EEG data, the EEG data of the frontal area, central area, parietal area and occipital area of ​​the brain corresponding to the preset electrodes are collected;

[0069] The AR power spectrum was used to calculate the relative power of the EEG data in five EEG frequency bands, namely, δ (1-4 Hz), θ (4-7 Hz), α (8-14 Hz), β (15-30 Hz), and γ (31-45 Hz), to obtain a preset number of relative power features.

[0070] In step S140, a personality trait recognition model based on the SVM classification model may be established, and the personality trait recognition model may be trained and verified based on the relative power feature.

[0071] In the embodiment of this example, the method further comprises:

[0072] Establishing a personality trait recognition model based on the SVM classification model, wherein the personality trait recognition model includes two types: risk seeking type and risk aversion type;

[0073] completing training and validating the personality trait recognition model based on the relative power feature;

[0074] Personality trait recognition is achieved based on the personality trait recognition model.

[0075] Embodiment 2:

[0076] In the embodiment of this example, see Figure 1 , which shows a flowchart of the implementation of the personality trait identification method and system based on EEG characteristics of risk decision-making under time pressure provided by an embodiment of the present invention, which is described in detail as follows:

[0077] Construction of the time pressure induction method: Since the time for each person to make a decision is specific, setting a unified time limit will cause some people to not feel the existence of time pressure. Therefore, in order to better induce the stress state of each person, the time pressure is set according to 80% of the average time of all reactions of each individual without time pressure. That is, the time pressure induction method is: first calculate the average time M for the subjects to complete a given decision task without time pressure, and then use 80% of M as the time limit for completing the decision task, which will induce time pressure on the subjects.

[0078] Designed for the risk decision-making task under time pressure: The entire experimental plan consists of two experiments, Experiment 1 and Experiment 2. In Experiment 1 and Experiment 2, two pie charts will appear on the screen during each trial, representing two different "lotteries" A and B. Each pie chart uses two colors with different area ratios to represent the probability of gain or loss after choosing the "lottery".

[0079] Experiment 1 was a situation without time pressure. Experiment 2 imposed personalized time limits on each trial to induce time pressure. The length of the time limit was set to 80% of the average reaction time of the volunteers in each trial in Experiment 1. After Experiment 2, the volunteers were required to fill in the time pressure scale to assess the time pressure they felt during the experiment. Those who did feel time pressure were included in the subsequent analysis. Experiment 2 was a risk decision-making task under time pressure, which included 48 trials. As shown, there were two alternative "lotteries" in each trial. In this trial, choosing lottery A had a 67% chance of getting 48 yuan and a 33% chance of losing 12 yuan; choosing lottery B had a 68% chance of getting 15 yuan and a 32% chance of getting 37.5 yuan. The expectation of lottery A was 28.2 yuan, and the expectation of lottery B was 22.2 yuan. In this trial, volunteers who tended to seek risks would choose lottery A because lottery A had a 67% chance of earning 48 yuan, which was more than the maximum return of lottery B of 37.5 yuan. Volunteers who tend to be risk-averse are more inclined to choose Lottery B, because Lottery A has a 33% chance of losing money. During the task, a 32-lead EEG acquisition system was used to collect EEG signals from volunteers. Electrodes were placed on the scalp surface according to the international 10-20 standard. The distribution of EEG electrodes and brain area division are shown in the following figure. Figure 2 After completing the task, the expected value was statistically analyzed to obtain the risk decision types of the volunteers under time pressure, and they were divided into "risk seeking type" and "risk averse type".

[0080] For EEG signal processing and analysis: Before analyzing EEG signals, interference signals must be removed first to obtain good results. Bandpass and notch filtering are used to remove artifacts from EEG signals. 1-45Hz bandpass filtering can remove artifacts caused by high-frequency electromyography, slow voltage drift, etc., and notch filtering can remove 50Hz power frequency interference in EEG signals. After filtering, independent component analysis is used to remove eye artifacts in EEG signals to obtain relatively pure EEG signals. During the task, the volunteers first saw the "lottery" information on the screen, then calculated and judged the benefits of the two lottery tickets, and finally made a decision. This is closely related to the volunteers' visual perception, decision-making judgment, and executive control functions, which correspond to the four brain regions of the frontal area (F), central area (C), parietal area (P), and occipital area (O). Therefore, when processing the EEG signals of volunteers, three electrodes in the frontal area (F3, Fz, F4), three electrodes in the central area (C3, Cz, C4), three electrodes in the parietal area (P3, Pz, P4), and three electrodes in the occipital area (O1, Oz, O2) were taken for analysis. Five EEG frequency bands were extracted using FIR filters, namely δ (1-4 Hz), θ (4-7 Hz), α (8-14 Hz), β (15-30 Hz), and γ (31-45 Hz). The relative power of each rhythmic wave was calculated using AR power spectrum estimation in the four brain regions (averaged for the three electrodes in each brain region), and a total of 20 (5 frequency bands × 4 brain regions) relative power features were obtained.

[0081] Construction and classification of personality trait model based on EEG features: Using the risk decision type obtained by expected value in the "lottery" task experiment 2 as the label, the principal component analysis of the above 20 EEG relative power features is performed to reduce the dimension to remove redundant features and prevent overfitting. The obtained feature values ​​are used to build the SVM classification model. Next, the reduced-dimensional features are input into the SVM classifier for model training and testing. The data is divided into two categories of "risk seeking" and "risk aversion" in a binary classification method, thereby realizing the identification of decision-making risk preference personality traits. Based on this classification model, in subsequent use, there is no need to go through tedious expected value calculations, only through EEG signals, to identify people's decision-making risk preferences.

[0082] In the embodiment of this example, the personality trait identification method and system based on EEG characteristics of risk decision-making under time pressure of the present invention can make a more objective, accurate and reliable assessment of the personality traits of risk decision-making under time pressure through EEG characteristics without cumbersome questionnaires or expected score calculations, thereby providing guidance information for the selection of personnel for emergency situation handling tasks.

[0083] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0084] In addition, in this exemplary embodiment, a personality trait recognition system is also provided. Figure 3 As shown, the personality trait identification system 300 may include: a risk decision-making task module 310 under time pressure, an EEG data acquisition module 320, an EEG data processing module 330, and a risk decision-making personality trait identification module 340. Among them:

[0085] The risk decision-making task module 310 under time pressure is used to construct a time pressure inducing method and design a risk decision-making task under time pressure;

[0086] The EEG data collection module 320 is used to collect EEG data of the test subject during the risk decision-making task under time pressure;

[0087] The EEG data processing module 330 is used to analyze the EEG data of the brain area corresponding to the preset electrodes after preprocessing the EEG data to obtain a preset number of relative power features;

[0088] The risk decision personality trait identification module 340 is used to establish a personality trait identification model based on the SVM classification model, and complete the training and verification of the personality trait identification model based on the relative power feature.

[0089] The specific details of each of the above personality trait identification system modules have been described in detail in the corresponding personality trait identification method, so they will not be repeated here.

[0090] It should be noted that although several modules or units of a personality trait identification system 300 are mentioned in the above detailed description, such division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0091] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0092] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0093] Refer to the following Figure 4 An electronic device 400 according to such an embodiment of the present invention is described. Figure 4 The electronic device 400 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0094] like Figure 4 As shown, the electronic device 400 is in the form of a general computing device. The components of the electronic device 400 may include but are not limited to: the at least one processing unit 410, the at least one storage unit 420, a bus 430 connecting different system components (including the storage unit 420 and the processing unit 410), and a display unit 440.

[0095] The storage unit stores program codes, which can be executed by the processing unit 410, so that the processing unit 410 performs the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification. For example, the processing unit 410 can perform the following steps: Figure 1 Steps S110 to S140 shown in FIG.

[0096] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 4201 and / or a cache storage unit 4202 , and may further include a read-only storage unit (ROM) 4203 .

[0097] The storage unit 420 may also include a program / utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0098] Bus 430 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0099] The electronic device 400 may also communicate with one or more external devices 470 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 450. In addition, the electronic device 400 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 460. As shown, the network adapter 460 communicates with other modules of the electronic device 400 via a bus 430. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0100] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal system, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0101] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible embodiments, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to perform the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0102] refer to Figure 5 As shown, a program product 500 for implementing the above method according to an embodiment of the present invention is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, system or device.

[0103] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0104] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, wherein readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, system, or device.

[0105] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0106] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0107] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0108] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0109] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A personality trait identification method, characterized in that: The method comprises: Construct a time pressure inducing method and design risk decision-making tasks under time pressure; Collect EEG data of the subjects when they are performing risk decision-making tasks under time pressure; After preprocessing the EEG data, analyzing the EEG data of the brain area corresponding to the preset electrodes to obtain a preset number of relative power features; A personality trait recognition model based on the SVM classification model is established, and training and verification of the personality trait recognition model are completed based on the relative power feature.

2. The method according to claim 1, characterized in that The time pressure inducing method in the method further comprises: The average time taken by the subjects to complete the preset decision-making task was collected and calculated, and 80% of the average time was set as the subject time under time pressure.

3. The method according to claim 1, characterized in that The risk decision-making tasks under time pressure of the method also include: Based on the preset risk investment options and alternative risk options, risk decision-making tasks are generated under no time pressure and under time pressure respectively.

4. The method according to claim 3, characterized in that The method further comprises: In the risk decision-making task under time pressure, the test subjects need to fill in the time pressure scale after completing the risk decision-making task to evaluate the time pressure they feel during the experiment.

5. The method according to claim 1, characterized in that The method further comprises: Based on the preset EEG acquisition equipment, multi-channel EEG data of the test subjects are collected when they perform risk decision-making tasks under time pressure.

6. The method according to claim 1, characterized in that The method further comprises: After preprocessing the EEG data, the EEG data of the frontal area, central area, parietal area and occipital area of ​​the brain corresponding to the preset electrodes are collected; The AR power spectrum was used to calculate the relative power of the EEG data in five EEG frequency bands, namely, δ (1-4 Hz), θ (4-7 Hz), α (8-14 Hz), β (15-30 Hz), and γ (31-45 Hz), to obtain a preset number of relative power features.

7. The method according to claim 1, characterized in that The method further comprises: Establishing a personality trait recognition model based on the SVM classification model, wherein the personality trait recognition model includes two types: risk seeking type and risk aversion type; completing training and validating the personality trait recognition model based on the relative power feature; Personality trait recognition is achieved based on the personality trait recognition model.

8. A personality trait recognition system, characterized in that: The system comprises: The risk decision-making task module under time pressure is used to construct a time pressure induction method and design a risk decision-making task under time pressure; The EEG data acquisition module is used to collect EEG data of the test subject during the risk decision-making task under time pressure; An EEG data processing module is used to analyze the EEG data of the brain area corresponding to the preset electrodes after preprocessing the EEG data to obtain a preset number of relative power characteristics; The risk decision-making personality trait identification module is used to establish a personality trait identification model based on the SVM classification model, and complete the training and verification of the personality trait identification model based on the relative power characteristics.

9. An electronic device, characterized in that: include Processor; and A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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