1. A method for detecting psychological traits of a user through deep learning, the method comprising at least the steps of: providing to the user a predetermined sequence of virtual environments, the each virtual environment of the sequence of virtual environments being related to a predetermined valence and having a predetermined duration; tracking a gaze direction of user, throughout the course of the series of virtual environments; acquiring at least one electroencephalography signal through an electroencephalography sensing node; computing an event-related potential waveform from electroencephalography signal; calculating a mismatch factor for each event related potentials during deviant sensory stimulation; and determining psychological trait based on the calculated mismatch factor and gaze direction .

2. The method according to claim 1, wherein the psychological trait is a mental disorder, depression, anxiety or stress.

3. The method according to claim 1, wherein the virtual environments being displayed on a virtual realty device.

4. The value chain system of claim 3, wherein the virtual reality device outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to analyse event-related potential waveform to determine psychological characteristic of user.

5. The method according to claim 1, wherein the virtual environment is created by utilising Bayesian network, deep learning and random forest.

6. A system for detecting psychological traits of a user through deep learning comprising: a virtual reality device is arranged to provide a predetermined sequence of virtual environments, the each virtual environment of the sequence of virtual environments being related to a predetermined valence and having a predetermined duration; a gaze detector is arranged to track a gaze direction of user, throughout the course of the series of virtual environments; electroencephalography sensing node is arranged to acquire at least one electroencephalography signal; a computing node is arranged to compute an event-related potential waveform from electroencephalography signal; a first control unit is arranged to calculate a mismatch factor for each event related potentials during deviant sensory stimulation; and a second control unit is arranged to determine psychological trait based on the calculated mismatch factor and gaze direction.

7. The system of claim 6, wherein the psychological trait is a mental disorder, depression, anxiety or stress.

8. The system of claim 6, wherein the virtual reality device outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to analyse event-related potential waveform to determine psychological characteristic of user.

9. The system of claim 6, wherein the virtual environment is created by utilising Bayesian network, deep learning and random forest.   VIRTUAL REALITY DEVICE BASED PSYCHOLOGICAL TRAIT DETECTION Abstract Providing the user with a sequence of virtual environments, each of which is related to a predetermined valence and has a predetermined duration, is one of the steps in a method for detecting psychological traits of a user through deep learning that may be implemented in accordance with embodiments of the present disclosure. In certain implementations, the user's gaze direction is followed as they go through a sequence of VR settings. It may also be possible for embodiments to include the acquisition of at least one electroencephalography signal through an electroencephalography sensing node. In certain implementations, the electroencephalogram signal is used to calculate an event-related potential waveform. Deviant sensory stimulation may also include the calculation of a mismatch factor for each event associated potential. In certain embodiments, the mismatch factor and eye gazing data are used to infer a person's personality type. , Claims:Claims :

1. A method for detecting psychological traits of a user through deep learning, the method comprising at least the steps of: providing to the user a predetermined sequence of virtual environments, the each virtual environment of the sequence of virtual environments being related to a predetermined valence and having a predetermined duration; tracking a gaze direction of user, throughout the course of the series of virtual environments; acquiring at least one electroencephalography signal through an electroencephalography sensing node; computing an event-related potential waveform from electroencephalography signal; calculating a mismatch factor for each event related potentials during deviant sensory stimulation; and determining psychological trait based on the calculated mismatch factor and gaze direction .

2. The method according to claim 1, wherein the psychological trait is a mental disorder, depression, anxiety or stress.

3. The method according to claim 1, wherein the virtual environments being displayed on a virtual realty device.

4. The value chain system of claim 3, wherein the virtual reality device outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to analyse event-related potential waveform to determine psychological characteristic of user.

5. The method according to claim 1, wherein the virtual environment is created by utilising Bayesian network, deep learning and random forest.

6. A system for detecting psychological traits of a user through deep learning comprising: a virtual reality device is arranged to provide a predetermined sequence of virtual environments, the each virtual environment of the sequence of virtual environments being related to a predetermined valence and having a predetermined duration; a gaze detector is arranged to track a gaze direction of user, throughout the course of the series of virtual environments; electroencephalography sensing node is arranged to acquire at least one electroencephalography signal; a computing node is arranged to compute an event-related potential waveform from electroencephalography signal; a first control unit is arranged to calculate a mismatch factor for each event related potentials during deviant sensory stimulation; and a second control unit is arranged to determine psychological trait based on the calculated mismatch factor and gaze direction.

7. The system of claim 6, wherein the psychological trait is a mental disorder, depression, anxiety or stress.

8. The system of claim 6, wherein the virtual reality device outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to analyse event-related potential waveform to determine psychological characteristic of user.

9. The system of claim 6, wherein the virtual environment is created by utilising Bayesian network, deep learning and random forest.

Description:VIRTUAL REALITY DEVICE BASED PSYCHOLOGICAL TRAIT DETECTION
Field of the Invention
[0001] The present invention relates generally to virtual reality, more particularly to a system and method to detect the psychological traits of a user.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] People judge other people based solely on their appearance, making assumptions about personality traits (such as kindness, dependability), aptitudes (such as intelligence, precision), and even professions (such as teacher, carer, or lawyer). Such assessments exhibit a strong degree of connection, according to psychological study (different people interpreting the same face image in a similar manner). Also, psychological studies have discovered some association between the appearance of a person's face and their performance in the real world (successful CEO, winning martial arts fighter, etc.).
[0004] To study and provide advice on interactions in the social and professional spheres, psychologists, counsellors, coaches, and therapists compile data on an individual's personal attributes and those of others. Yet, it is evident that various people possess varying degrees of judgmental ability, some judgements may be based solely on prejudice, and in any case, it is impracticable to rely on human judgement to effectively and repeatedly handle large amounts of data.
[0005] Various technological solutions (e.g., digital credentials based on personality and health-based evaluation, A kind of psychological counselling system based on virtual reality, etc.) are disclosed in patent literature. Few of the exemplary documents are discussed below.
[0006] CN106295112A (By: SUZHOU QIZHAN INFORMATION TECHNOLOGY) discloses a psychological consultation system based on virtual reality. The psychological consultation system comprises virtual reality glasses and a psychological consultation network platform. The psychological consultation network platform connects the patient's mobile phone and the doctor's mobile phone through a mobile phone network. The virtual reality glasses are provided with a mobile phone card slot. The psychological consultation network platform includes a psychological testing unit, a psychologist database, a charging unit, a mobile phone forwarding call unit and a medical treatment voice storage unit. The psychological testing unit stores a video file for a virtual reality scene viewed by the virtual reality glasses. The psychological consultation system has the video file preset by the psychological consultation network platform, and the patient can see the video of the specific scene before the treatment, can experience the specific virtual scene, and can improve the efficiency of consultation when the viewing experience of the video is taken as a starting point .
[0007] CN115641942A (By: WITRIGHT TECHNOLOGY) discloses a virtual reality-based examination psychology training method, device and system, belonging to the field of virtual reality; after the students wear the virtual reality equipment and the physiological index acquisition equipment, the virtual reality equipment shows the students with scenes which are completely the same as the real examination mode and the real examination flow, so that the students have a better substitution feeling; and then acquiring physiological indexes of the students in the examination process in real time, judging the psychological conditions of the students in the examination process through the physiological indexes such as skin temperature, skin current, heart rate, brain waves and beta waves, and if judging that the students have psychological problems through the first physiological index data, sending an abnormal prompt to the students through virtual reality equipment. The scheme of the application can provide the student with the scene completely the same as the real examination, can detect whether the psychological problem appears in the student, can give the student suggestion after the psychological problem appears to the student makes clear the psychological state of self, and the student self-regulation of being convenient for has improved examination training effect greatly.
[0008] CN107799165A (By: GUANGZHOU BOWEI INTELLIGENT TECHNOLOGY) relates to a psychological assessment method based on a virtual reality technology. Virtual scenarizing processing is carried out on a psychological scale; answers, behaviors, and physiological data of a subject are collected in real time; on the basis of the answer options of the subject, intelligent jumping of problems of the psychological scale is completed; the scale content is analyzed comprehensively and intelligently, three kinds of data are trained by a convolution neural network and a recurrent neural network and then feature fusion is carried out, the processed information is inputted into a softmax layer to obtain a psychological assessment model; with the psychological assessment model, the psychological assessment result of the subject is compared with a doctor tag, loss function calculation and gradient reversed conduction are carried out to correct the answer options of the subject intelligent; and then the psychological, behavior and the corrected answer data are calculated by the psychological assessment model to obtain a final assessment result. According to the invention, on the basis of combination of VR, intelligent sensing, big data analysis, artificial intelligence and other technologies with the traditional psychological assessment method, the accuracy of psychological assessment is improved and the medical resources are saved effectively.
[0009] However, the technological solutions for trait detection suffers from various limitations such as, inaccuracy, etc. Therefore, more advancement in this field of technology is required. More specifically, to a system and method to detect the psychological traits of a user through a virtual reality device.
Summary
[00010] The present invention relates generally to virtual reality, more particularly to a system and method to detect the psychological traits of a user by using virtual reality device.
[00011] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00012] The following paragraphs provide additional support for the claims of the subject application.
[00013] Embodiments of the present disclosure may include a method for detecting psychological traits of a user through deep learning, the method including at least the steps of providing to the user a predetermined sequence of virtual environments, each virtual environment of the sequence of virtual environments being related to a predetermined valence and having a predetermined duration.
[00014] Embodiments may also include tracking a gaze direction of user, throughout the course of the series of virtual environments. Embodiments may also include acquiring at least one electroencephalography signal through an electroencephalography sensing node. Embodiments may also include computing an event-related potential waveform from electroencephalography signal. Embodiments may also include calculating a mismatch factor for each event related potentials during deviant sensory stimulation. Embodiments may also include determining psychological trait based on the calculated mismatch factor and gaze direction.
[00015] In some embodiments, the psychological trait may be a mental disorder, depression, anxiety or stress. In some embodiments, the virtual environments being displayed on a virtual realty device. In some embodiments, the virtual reality device outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to analyse event-related potential waveform to determine psychological characteristic of user. In some embodiments, the virtual environment may be created by utilising Bayesian network, deep learning and random forest.
[00016] Embodiments of the present disclosure may also include a system for detecting psychological traits of a user through deep learning including a virtual reality device may be arranged to provide a predetermined sequence of virtual environments, the each virtual environment of the sequence of virtual environments being related to a predetermined valence and having a predetermined duration.
[00017] Embodiments may also include a gaze detector may be arranged to track a gaze direction of user, throughout the course of the series of virtual environments. Embodiments may also include electroencephalography sensing node may be arranged to acquire at least one electroencephalography signal. Embodiments may also include a computing node may be arranged to compute an event-related potential waveform from electroencephalography signal. Embodiments may also include a first control unit may be arranged to calculate a mismatch factor for each event related potentials during deviant sensory stimulation. Embodiments may also include a second control unit may be arranged to determine psychological trait based on the calculated mismatch factor and gaze direction.
[00018] In some embodiments, the psychological trait may be a mental disorder, depression, anxiety or stress. In some embodiments, the virtual reality device outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to analyse event-related potential waveform to determine psychological characteristic of user. In some embodiments, the virtual environment may be created by utilising Bayesian network, deep learning and random forest.

Brief Description of the Drawings
[00019] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00020] FIG. 1 is a flowchart illustrating a method for detecting psychological traits of a user by using virtual reality device, according to some embodiments of the present disclosure.
[00021] FIG. 2 is a block diagram illustrating a system to detect psychological traits of a user by using virtual reality device, according to some embodiments of the present disclosure.
Detailed Description
[00022] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00023] The use of the terms a and an and the and at least one and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term at least one followed by a list of one or more items (for example, at least one of A and B) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms comprising, having, including, and containing are to be construed as open-ended terms (i.e., meaning including, but not limited to,) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., such as) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00024] The present invention relates generally to virtual reality, more particularly to a system and method to detect the psychological traits of a user.
[00025] FIG. 1 is a flowchart illustrating a method for detecting psychological traits of a user by using virtual reality device, according to some embodiments of the present disclosure. In some embodiments, at 110, the method may include providing to the user a predetermined sequence of virtual environments, the each virtual environment of the sequence of virtual environments being related to a predetermined valence and having a predetermined duration. At 120, the method may include tracking a gaze direction of user, throughout the course of the series of virtual environments. At 130, the method may include acquiring at least one electroencephalography signal through an electroencephalography sensing node. At 140, the method may include computing an event-related potential waveform from electroencephalography signal. At 150, the method may include calculating a mismatch factor for each event related potentials during deviant sensory stimulation. At 160, the method may include determining psychological trait based on the calculated mismatch factor and gaze direction. At least the steps of, the method may include 110 to 160.
[00026] In some embodiments, the psychological trait may be a mental disorder, depression, anxiety or stress. In some embodiments, the virtual environments being displayed on a virtual realty device. In some embodiments, the virtual reality device may output a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to analyse event-related potential waveform to determine psychological characteristic of user. In some embodiments, the virtual environment may be created by utilising Bayesian network, deep learning and random forest.
[00027] FIG. 2 is a block diagram illustrating a system to detect psychological traits of a user by using virtual reality device, according to some embodiments of the present disclosure. In some embodiments, the system 200 may include electroencephalography 230 sensing node may be arranged to acquire at least one electroencephalography signal, a computing node 250 may be arranged to compute an event-related potential waveform from electroencephalography signal, a first control unit 240 may be arranged to calculate a mismatch factor for each event related potentials during deviant sensory stimulation, and a second control unit 260 may be arranged to determine psychological trait based on the calculated mismatch factor and gaze direction.
[00028] In some embodiments, the system 200 may also include a virtual reality device 210 may be arranged to provide a predetermined sequence of virtual environments, the each virtual environment of the sequence of virtual environments being related to a predetermined valence. The system 200 may also include a gaze detector 220 may be arranged to track a gaze direction of user, throughout the course of the series of virtual environments. The virtual reality device 210 may include a predetermined duration 212.
[00029] In some embodiments, the psychological trait may be a mental disorder, depression, anxiety or stress. In some embodiments, the virtual reality device 210 may output a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to analyse event-related potential waveform to determine psychological characteristic of user. In some embodiments, the virtual environment may be created by utilising Bayesian network, deep learning and random forest.
[00030] Providing the user with a sequence of virtual environments, each of which is related to a predetermined valence and has a predetermined duration, is one of the steps in a method for detecting psychological traits of a user through deep learning that may be implemented in accordance with embodiments of the present disclosure.
[00031] In certain implementations, the user's gaze direction is followed as they go through a sequence of VR settings. It may also be possible for embodiments to include the acquisition of at least one electroencephalography signal through an electroencephalography sensing node. In certain implementations, the electroencephalogram signal is used to calculate an event-related potential waveform. Deviant sensory stimulation may also include the calculation of a mismatch factor for each event associated potential. In certain embodiments, the mismatch factor and eye gazing data are used to infer a person's personality type.
[00032] A mental illness, state of sadness, anxiety, or stress may represent the underlying psychological feature in some contexts. In certain implementations, a virtual reality device is used to show simulated worlds. In certain implementations, the virtual reality device reports simulation results to the machine learning system, which then trains a model to analyse the user's event-related potential waveform in order to infer personality traits. Bayesian networks, deep learning, and random forests are only some of the techniques that may be used to construct the simulated setting in various implementations.
[00033] It is possible that embodiments of the present disclosure include a system for detecting psychological traits of a user through deep learning, wherein a virtual reality device is arranged to provide a predetermined sequence of virtual environments, wherein each environment in the sequence of virtual environments is related to a predetermined valence and has a predetermined duration.
[00034] A gaze detector configured to follow the user's line of sight throughout a set of virtual worlds is another feature of certain embodiments. Electroencephalography sensing nodes may also be configured to acquire at least one electroencephalography signal in certain embodiments. Furthermore, in certain implementations, a computational node is set up to derive a potential waveform associated with an event from an electroencephalogram. In certain implementations, a first control unit is set up to determine a mismatch factor for each event-related potential recorded during abnormal sensory input. To further assess a subject's mental make-up using the mismatch factor and eye tracking, a second control unit may be set up in certain embodiments.
[00035] A mental illness, state of sadness, anxiety, or stress may represent the underlying psychological feature in some contexts. In certain implementations, the virtual reality device reports simulation results to the machine learning system, which then trains a model to analyse the user's event-related potential waveform in order to infer personality traits. Bayesian networks, deep learning, and random forests are only some of the techniques that may be used to construct the simulated setting in various implementations.
[00036] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00037] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00038] The term non-transitory storage device or storage or memory, as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00039] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00040] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

 

Claims
I/We Claim:
1. A method for detecting psychological traits of a user through deep learning, the method comprising at least the steps of: providing to the user a predetermined sequence of virtual environments, the each virtual environment of the sequence of virtual environments being related to a predetermined valence and having a predetermined duration; tracking a gaze direction of user, throughout the course of the series of virtual environments; acquiring at least one electroencephalography signal through an electroencephalography sensing node; computing an event-related potential waveform from electroencephalography signal; calculating a mismatch factor for each event related potentials during deviant sensory stimulation; and determining psychological trait based on the calculated mismatch factor and gaze direction .
2. The method according to claim 1, wherein the psychological trait is a mental disorder, depression, anxiety or stress.
3. The method according to claim 1, wherein the virtual environments being displayed on a virtual realty device.
4. The value chain system of claim 3, wherein the virtual reality device outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to analyse event-related potential waveform to determine psychological characteristic of user.
5. The method according to claim 1, wherein the virtual environment is created by utilising Bayesian network, deep learning and random forest.
6. A system for detecting psychological traits of a user through deep learning comprising:
a virtual reality device is arranged to provide a predetermined sequence of virtual environments, the each virtual environment of the sequence of virtual environments being related to a predetermined valence and having a predetermined duration;
a gaze detector is arranged to track a gaze direction of user, throughout the course of the series of virtual environments;
electroencephalography sensing node is arranged to acquire at least one electroencephalography signal;
a computing node is arranged to compute an event-related potential waveform from electroencephalography signal;
a first control unit is arranged to calculate a mismatch factor for each event related potentials during deviant sensory stimulation; and
a second control unit is arranged to determine psychological trait based on the calculated mismatch factor and gaze direction.
7. The system of claim 6, wherein the psychological trait is a mental disorder, depression, anxiety or stress.

8. The system of claim 6, wherein the virtual reality device outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to analyse event-related potential waveform to determine psychological characteristic of user.
9. The system of claim 6, wherein the virtual environment is created by utilising Bayesian network, deep learning and random forest.

 

VIRTUAL REALITY DEVICE BASED PSYCHOLOGICAL TRAIT DETECTION
Abstract
Providing the user with a sequence of virtual environments, each of which is related to a predetermined valence and has a predetermined duration, is one of the steps in a method for detecting psychological traits of a user through deep learning that may be implemented in accordance with embodiments of the present disclosure. In certain implementations, the user's gaze direction is followed as they go through a sequence of VR settings. It may also be possible for embodiments to include the acquisition of at least one electroencephalography signal through an electroencephalography sensing node. In certain implementations, the electroencephalogram signal is used to calculate an event-related potential waveform. Deviant sensory stimulation may also include the calculation of a mismatch factor for each event associated potential. In certain embodiments, the mismatch factor and eye gazing data are used to infer a person's personality type. , Claims:Claims
I/We Claim:
1. A method for detecting psychological traits of a user through deep learning, the method comprising at least the steps of: providing to the user a predetermined sequence of virtual environments, the each virtual environment of the sequence of virtual environments being related to a predetermined valence and having a predetermined duration; tracking a gaze direction of user, throughout the course of the series of virtual environments; acquiring at least one electroencephalography signal through an electroencephalography sensing node; computing an event-related potential waveform from electroencephalography signal; calculating a mismatch factor for each event related potentials during deviant sensory stimulation; and determining psychological trait based on the calculated mismatch factor and gaze direction .
2. The method according to claim 1, wherein the psychological trait is a mental disorder, depression, anxiety or stress.
3. The method according to claim 1, wherein the virtual environments being displayed on a virtual realty device.
4. The value chain system of claim 3, wherein the virtual reality device outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to analyse event-related potential waveform to determine psychological characteristic of user.
5. The method according to claim 1, wherein the virtual environment is created by utilising Bayesian network, deep learning and random forest.
6. A system for detecting psychological traits of a user through deep learning comprising:
a virtual reality device is arranged to provide a predetermined sequence of virtual environments, the each virtual environment of the sequence of virtual environments being related to a predetermined valence and having a predetermined duration;
a gaze detector is arranged to track a gaze direction of user, throughout the course of the series of virtual environments;
electroencephalography sensing node is arranged to acquire at least one electroencephalography signal;
a computing node is arranged to compute an event-related potential waveform from electroencephalography signal;
a first control unit is arranged to calculate a mismatch factor for each event related potentials during deviant sensory stimulation; and
a second control unit is arranged to determine psychological trait based on the calculated mismatch factor and gaze direction.
7. The system of claim 6, wherein the psychological trait is a mental disorder, depression, anxiety or stress.

8. The system of claim 6, wherein the virtual reality device outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to analyse event-related potential waveform to determine psychological characteristic of user.
9. The system of claim 6, wherein the virtual environment is created by utilising Bayesian network, deep learning and random forest.