KR102538667B1 - Method, device and system for test ad training based on neurofeedback - Google Patents

Abstract

The system includes a first interface for receiving identification information of the client, biometric information of the subject, and brain wave data generated by measuring the subject's brain wave from a client through a network, and an authentication system for storing information of valid clients. A second interface for providing communication, providing identification information to an authentication system through the second interface, generating output data based on biometric information and brain wave data of a client authenticated by the authentication system, and generating output data through the first interface. An EEG processor providing output data to the client may be included.

Description

Method and system for testing and training based on neurofeedback {METHOD, DEVICE AND SYSTEM FOR TEST AD TRAINING BASED ON NEUROFEEDBACK}

The technical idea of the present invention relates to neurofeedback, and more particularly, to a method and system for testing and training based on neurofeedback.

The background art of the present invention is disclosed in Republic of Korea Patent Registration No. 10-2126002 (2020.06.24).
Neurofeedback is an important academic field of brain science that has been continuously researched and developed since it was first discovered at Cambridge University in England in 1934. or to enhance its function. For such neurofeedback, it may be required to accurately measure brain waves from the subject, and it may be required to provide useful information from the measured brain waves to the subject. A high-performance EEG measuring device may be required for the former requirement, while a high-level algorithm and processing of a large amount of collected data may be required for the latter requirement.

The technical idea of the present invention is to provide a method and system capable of efficiently solving the high computational complexity of EEG processing for neurofeedback and providing high convenience and security.

In order to achieve the above object, a system according to one aspect of the technical idea of the present invention is generated by measuring the identification information of the first client, the subject's biometric information, and the subject's brain wave from the first client through the network. A first interface for receiving brain wave data, a second interface for providing communication with the authentication system for storing information of valid first clients, and providing identification information to the authentication system through the second interface, and the authentication system It may include an EEG processor generating output data based on the biometric information and EEG data of the first client authenticated by and providing the output data to the first client through a first interface.

According to an exemplary embodiment of the present invention, the first interface may receive settings for neurofeedback of the subject from a second client through a network, and the EEG processor may correspond to the identification information of the first client. and may provide the identified settings to the first client through the first interface.

According to an exemplary embodiment of the present invention, the settings include a training protocol, a training program type, a training program sequence, a training program running time, a training program condition, a training program difficulty level, a training number, and a training program grade. may contain at least one.

According to an exemplary embodiment of the present invention, the first interface may receive an opinion on the output data from the second client, and the EEG processor may provide the opinion to the first client through the first interface.

According to an exemplary embodiment of the present invention, the system may further include a database for storing settings and opinions and a third interface for providing communication, and the brain wave processing unit may set settings and opinions corresponding to the identification information of the first client. can be obtained from the database.

According to an exemplary embodiment of the present invention, the brain wave processing unit, based on biometric information and brain wave data, basal rhythm index, self-regulation index, attention index, activity index, emotional index, antistress index, left and right brain balance index, brain A plurality of exponents including exponents may be generated.

According to an exemplary embodiment of the present invention, the first interface may further receive an analysis parameter from the first client, and the EEG processor selects at least one index among a plurality of indexes based on the analysis parameter; Output data including at least one exponent may be generated.

According to an exemplary embodiment of the present invention, the second interface may further receive an analysis parameter corresponding to the identification information from the authentication system, and the EEG processing unit selects at least one index from among a plurality of indexes based on the analysis parameter. and generate output data including at least one exponent.

According to an exemplary embodiment of the present invention, the first interface may further receive a registration request including a hardware key from the first client, verify the hardware key, and open the second interface in response to the verified hardware key. Through the authentication system, registration of the first client may be requested, and an encryption key provided from the authentication system may be provided to the first client through the first interface.

According to an exemplary embodiment of the present invention, biometric information and brain wave data may be encrypted based on an encryption key, a decryption key corresponding to the identification information is received from the authentication system through the second interface, and based on the decryption key Thus, biometric information and EEG data can be decoded.

According to an exemplary embodiment of the present invention, the biometric information may include at least one of age, visual acuity, height, weight, blood type, IQ, blood pressure, blood sugar, disease, and medicine taken of the subject.

According to an exemplary embodiment of the present invention, it may further include a third interface for providing communication with a database that stores identification information and EEG data on a plurality of subjects, and the EEG processing unit is authenticated by an authentication system. Output data may be generated by obtaining reference data corresponding to biometric information of the first client obtained from the database through the third interface and processing EEG data based on the reference data.

According to an exemplary embodiment of the present invention, the brain wave processing unit may store at least one of biometric information, brain wave data, and output data of the authenticated first client in a database through a third interface.

According to an exemplary embodiment of the present invention, the brain wave processing unit includes a machine learning model learned by a plurality of samples of identification information and brain wave data, and transmits biometric information and brain wave data of the authenticated first client to the machine learning model. and obtain at least some of the output data from the machine learning model.

An EEG processing method according to one aspect of the technical idea of the present invention includes the steps of receiving client identification information, subject's biometric information, and EEG data generated by measuring the subject's EEG data from a client through a network; valid clients' information Providing identification information to an authentication system that stores, receiving reference data corresponding to biometric information of a client authenticated by the authentication system from a database, generating output data by processing EEG data based on the reference data and providing the output data to the client.

According to an exemplary embodiment of the present invention, an EEG processing method includes receiving a registration request including a hardware key from a client, verifying the hardware key, and requesting registration of the client to an authentication system in response to the verified hardware key. The method may further include providing the encryption key provided from the authentication system to the client.

According to an exemplary embodiment of the present invention, the method may further include receiving a decryption key corresponding to identification information from an authentication system, and decrypting biometric information and brainwave data based on the decryption key.

According to the method and system according to the technical concept of the present invention, operations of high computational complexity can be released from clients, and accordingly, test and training services for neurofeedback can be widely and easily constructed.

In addition, according to the method and system according to the technical idea of the present invention, the risk of leakage of personal information can be reduced and high security can be achieved by separating the identification information of the client and the personal information of the subject.

Effects obtainable in the embodiments of the present invention are not limited to the above-mentioned effects, and other effects not mentioned are common in the technical field to which the present invention belongs from the description of the embodiments of the present invention below. It can be clearly derived and understood by those with knowledge. That is, unintended effects according to the practice of the present invention may also be derived by those skilled in the art from the embodiments of the present invention.

1 is a block diagram illustrating an EEG processing system according to an exemplary embodiment of the present invention.
2 is a flowchart illustrating a method for testing and training based on neurofeedback according to an exemplary embodiment of the present invention.
3 is a block diagram illustrating an EEG processing system according to an exemplary embodiment of the present invention.
4 is a flowchart illustrating a method for testing and training based on neurofeedback according to an exemplary embodiment of the present invention.
5 is a flowchart illustrating a method for testing and training based on neurofeedback according to an exemplary embodiment of the present invention.
6 is a flowchart illustrating a method for testing and training based on neurofeedback according to an exemplary embodiment of the present invention.
7 is a block diagram illustrating an EEG processing system according to an exemplary embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. The embodiments of the present invention are provided to more completely explain the present invention to those skilled in the art. Since the present invention can have various changes and various forms, specific embodiments are illustrated in the drawings and described in detail. However, it should be understood that this is not intended to limit the present invention to the specific disclosed form, and includes all modifications, equivalents, and substitutes included in the spirit and scope of the present invention. In describing each figure, like reference numbers are used for like elements.

Terms used in this application are only used to describe specific embodiments, and are not intended to limit the present invention. Expressions in the singular number include plural expressions unless the context clearly dictates otherwise. In this application, terms such as "comprise" or "have" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that it does not preclude the possibility of the presence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and unless explicitly defined in the present application, interpreted in an ideal or excessively formal meaning. It doesn't work.

In the drawings and descriptions below, a component represented or described as a block may be a hardware block or a software block. For example, each of the components may be an independent hardware block that exchanges signals with each other, or may be a software block executed by at least one processor.

1 is a block diagram illustrating an EEG processing system according to an exemplary embodiment of the present invention. As shown in FIG. 1 , the EEG processing system 10 may access a network 5 and communicate with the first client 2 and the second client 4 through the network 5 . In addition, the brain wave processing system 10 may communicate with the authentication system 7 and the database 9 . In some embodiments, different from that shown in FIG. 1 , authentication system 7 and database 9 may connect to network 5 , and brain wave processing system 10 may connect authentication system via network 5 . (7) and database (9).

The first client 2 may generate brain wave data by measuring the brain wave of the subject 1 . For example, the subject 1 may wear an EEG measuring device disclosed in Korean Patent Registration No. 10-2126002 to which the applicant of the present application holds the patent right, and the first client 2 may receive a plurality of EEG measurements from the EEG measuring device. Signals sensed through the channels may be received. In addition, the first client 2 may receive biometric information input from the test subject 1, such as age, eyesight, height, weight, blood type, IQ, blood pressure, blood sugar, disease, medicine to be taken, and the like. In some embodiments, the first client 2 may be a personal computer such as a desktop computer or a laptop computer, and may be communicatively connected to the EEG measuring device. In addition, the first client 2 may provide the output data provided from the EEG processing system 10 to the test subject 1 through the network 5 through, for example, a display or a print. In some embodiments, the brain wave data may include a series of samples generated by sensing brain waves through a plurality of channels over time, and the first client 2 transmits the brain wave data through the network 5. It can be provided to the brain wave processing system 10.

The first client 2 may provide neurofeedback to the subject 1 . Neurofeedback may also be referred to as "nerve biofeedback", and may refer to a technique of improving the brain's homeostatic self-regulation ability by measuring brain waves and selectively feeding back the signals to one's own brain. Neurofeedback is an important field of brain science, and many studies are being conducted, new training techniques are being proposed, and new facts are being discovered. For example, plasticity of the brain, which is a characteristic that the brain develops as it is used and develops a neural network by itself as needed, was discovered, and neurogenesis, which is that nerve cells, that is, neurons are also constantly newly generated, was discovered. Accordingly, new brain cells can be continuously created, and a new neural network can be created through learning, so that the brain can be continuously developed, and neurofeedback can maximize these characteristics of the brain. The first client 2 may communicate with the EEG processing system 10 through the network 5, and as will be described later, the expert 3 can communicate with the subject 1 according to the setting in the EEG processing system 10. Neurofeedback can be provided. The first client 2 may provide feedback to the subject 1 through various output devices, such as a display device and a speaker, or provide feedback (eg, sound, vibration, etc.) through output devices included in the EEG measuring device. ) may be communicated with the EEG measurement device so as to be provided.

As a non-limiting example, the first client 2 may be the subject's own computer, such as a desktop computer, a laptop computer, a smart phone, a tablet PC, and the like. In addition, the first client 2 may be installed in a space where the expert 3 is present, and may be, for example, a computing system (eg, a kiosk) operated in a center or institution that provides testing and training based on neurofeedback. , It may be a computing system operated in a health examination center providing neurofeedback as one of the health examination items of the subject 1 or a computing system operated in a medical institution. In this specification, unless otherwise stated, it is assumed that the first client 2 is the subject's 1 own computer, but it will be understood that exemplary embodiments of the present invention are not limited thereto.

The expert 3 may access the EEG processing system 10 to the second client 4 and the network 5 . For example, the expert 1 may be a skilled counselor, doctor, etc. related to the subject 1, and various settings for the neurofeedback of the subject 1 are configured through the second client 4 through the brain wave processing system ( 10) can be provided. Similar to the first client 2, the second client 4 may be the subject's own computer, such as a desktop computer, laptop computer, smart phone, tablet PC, etc., as non-limiting examples. Also, the second client 4 may be located in the same space as the first client 2 or may be located far away. Also, in some embodiments, when the subject 1 and the expert 3 are in the same location, the first client 2 and the second client 4 may be the same. That is, the first client 2, the second client 4, and the EEG processing system 10 may access the network 5, and the network 5 may be a wide area network such as the Internet. Physical distances between the first client 2, the second client 4, and the EEG processing system 10 may not be limited.

EEG processing (or EEG signal/data processing) for neurofeedback may have high computational complexity. For example, brain waves are delta (about 0.1 to 3 Hz, deep sleep state), theta (about 4 to 7 Hz, sleep state), alpha (about 8 to 12 Hz, resting state), and SMR (about 12 to 15 Hz, awake state). , low beta (about 13 ~ 20 Hz, active state), high beta (about 21 ~ 30 Hz, active, excited, stressed state), gamma (about 31 Hz or more, active, information combined state), etc. , processing algorithms for each type may be different. As will be described later, the brain waves of the subject 1 obtained by neurofeedback can be processed based on not only the brain waves previously measured from the subject 1 but also the vast amount of brain wave data collected and stored in the database 9. Accordingly, it may not be practically easy to perform brain wave processing based on neurofeedback in the first client 2 and/or the second client 4, and it may not be easy to construct a service based on neurofeedback. there is.

As described below with reference to the drawings, the brain wave processing system 10 may communicate with the first client 2 and the second client 4 through the network 5, and the second client 4 Brain wave data generated by measuring the brain wave of the subject 1 by neurofeedback based on the setting by the first client 2 is received from the first client 2 and processed remotely, thereby converting the output data to the first client 2 and/or the second client 2. 2 can be provided to the client (4). Accordingly, EEG processing of high computational complexity can be efficiently performed in the EEG processing system 10 having higher performance than the first client 2 . In addition, brain wave data and biometric information of the subject 1 required for brain wave processing can be separated from identification information for authentication of the first client 2, thereby solving problems such as leakage of personal information, High security can be achieved. As a result, the brain wave processing system 10 can widely and easily build services based on neurofeedback.

As shown in FIG. 1 , the EEG processing system 10 may include a first interface 11 , a second interface 12 , a third interface 13 , and an EEG processor 15 . The first interface 11 may provide the EEG processing system 10 with access to the network 5 . For example, the first interface 11 provides identification information of the first client 2, biometric information of the subject 1, and EEG data of the subject 1 from the first client 2 through the network 5. , and identification information of the second client 4, biometric information of the expert 3, setting information of the expert 3 for the subject 1, and the like can be received from the second client 4. . In addition, the first interface 11 may provide output data provided from the EEG processor 15 to the first client 2 and/or the second client 4 through the network 5 .

The second interface 12 can provide access to the authentication system 7 . The authentication system 7 may store information of valid clients, and the authentication system 7 may authenticate the first client 2 and the second client 4 based on the stored information. In addition, the authentication system 7 may generate a secret key used for encryption/decryption of data, as described below with reference to FIGS. 3 and 4 . Instead of the brain wave processing system 10 directly authenticating the first client 2 and the second client 4, a separate authentication system 7 authenticates the first client 2 and the second client 4. Accordingly, processing of biometric information and EEG data of the subject 1 may be separated from authentication of the first client 2 and the second client 4 . For example, the biometric information of the subject 1 may not include any information by which the subject 1 can be identified, while the first client 2 has its own identification information (eg, FIG. 4 ). hardware key) to the brain wave processing system 10. The second interface 12 may communicate with the authentication system 7 via a local network or with the authentication system 7 via a point-to-point connection.

A third interface 13 may provide access to a database 9 . The database 9 may store biometric information and EEG data of a plurality of subjects. In some embodiments, biometric information and EEG data of the subject 1 provided from the first client 2 and output data corresponding thereto may be stored in the database 9 . Differently from that shown in FIG. 1, the first client 2 accesses the database 9 through the network 5 and the first client 2 performs EEG processing based on the data stored in the database 9. However, it may not be easy to implement the hardware/software for processing the vast amount of data stored in the database 9 in the first client 2, and the operating subject of the first client 2 may incur high costs. In addition, allowing the expert 3 to set the first client 2 to settings suitable for the subject 1 may be possible only when the subject 1 and the expert 3 are in the same space. The brain wave processing system 10 builds a service based on neurofeedback by providing brain wave processing results to the first client 2 and the second client 4, which can be implemented at low cost based on high security. can facilitate

The EEG processor 15 may generate EEG data including useful information by processing the EEG data. For example, the brain wave processor 15 may receive identification information of the first client 2 , biometric information of the subject 1 , and brain wave data through the first interface 11 . In addition, the brain wave processor 15 may receive identification information of the second client 2 and setting information provided by the expert 3 through the first interface 11 . In some embodiments, the brain wave processing unit 15 provides the identification information of the first client 2 and/or the second client 4 to the authentication system 7 so that the first client 2 and/or the second client 2 While the client 4 can be authenticated, when the authentication of the first client 2 and/or the second client 4 succeeds, output data can be generated by processing the biometric information and brainwave data of the subject 1. can

In some embodiments, the database 9 may store EEG data generated by the subject 1 through neurofeedback training and training achievement, and the EEG processing unit 15 may store the EEG of the subject 1 according to training. and change in achievement can be analyzed. In addition, the brain wave processing unit 15 may grasp the neurofeedback effect of the subject 1 based on the analysis result, and based on this, the expert 3 and/or the brain wave processing unit 15 determine the training method of the subject 1 in the future. and direction can be determined. In some embodiments, the EEG processor 15 may receive settings defined for the subject 1 by the expert 3 from the second client 4 and store them in the database 9 . For example, the expert 3, through the second client 4, provides training protocols, types of training programs, order of training programs, execution time of training programs, conditions of training programs, difficulty of training programs, number of training sessions, The grade of the training program and the like can be set for the subject (1). The brain wave processing unit 15 may identify settings corresponding to the identification information of the first client 2 so that neurofeedback is performed based on the settings stored in the database 9, and the identified settings in the first client 2 Settings may be provided, and the first client 2 may provide neurofeedback to the subject 1 according to settings provided from the brain wave processing system 10 . In some embodiments, the expert 3 may check the EEG data of the subject 1 and/or data generated by the EEG processor 15 from the EEG data through the second client 4, and the subject 1 ) By analyzing the EEG data and/or achievement level, the user's comment may be provided to the EEG processing system 10 . The brain wave processing unit 15 may store the opinion provided by the expert 3 in the database 9, identify the opinion corresponding to the identification information of the first client 2, and send the identified opinion to the first client ( It can be provided to the subject (1) through 2). For example, the subject 1's BQ report and training result report may be prepared by the expert 3, stored in the database 9, and provided to the subject 1.

In some embodiments, database 9, in EEG data collected by a plurality of subjects, statistical values (eg, mean, spread) of the above-described indices corresponding to various conditions (eg, age, etc.) may be stored, and the brain wave processing unit 15 may generate output data by analyzing the brain wave data based on statistical values corresponding to the biometric information of the subject 1 . In some embodiments, time domain samples included in EEG data may be transformed into a frequency domain through, for example, Fourier transform, and EEG data may be analyzed in the frequency domain. For example, peaks, maximum peak values, left-right coherence, and the like may be detected in the frequency domain, and exponents described below may be calculated based on the detected values.

The EEG processing unit 15 includes various indices including basal rhythm index, self-regulation index, attention index, activity index, emotional index, anti-stress index, left and right brain balance index, and encephalopathy by processing biometric information and brain wave data. output data can be generated. The basal rhythm is an EEG generated when the eyes are closed and resting in a false state, and the criterion may be different depending on the age, and can be used as a basis for determining the degree of brain development, aging, and stability. The self-regulation index can serve as a basic measure of brain health and activity. The brain can control the activity rhythm by autonomously controlling the three states of rest, attention, and concentration at the time of awakening, and the self-regulation index is a value that evaluates the brain's self-regulation ability for these three states. can The attention index is an index representing the degree of brain arousal and resistance to disease or stress, and the degree of brain arousal can be determined according to an age standard. The activity index, as an index representing the degree of brain activity, can be used as a basis for determining mental activity, thinking ability, and behavioral tendencies. The emotional index may be referred to as an emotional index, and may indicate an emotional stability or unstable state. The anti-stress index (or stress index) may indicate resistance to physical and mental fatigue caused by internal and external environmental factors. Physical stress may represent a state of tension, anxiety, and excitement in the human body, while mental stress may represent a state of psychological tension, anxiety, and excitement. The left-right balance brain index is an index representing the balance between the left brain and the right brain, and the balance can be determined based on the symmetry of the amplitude and the symmetry of the phase of the brain waves. The brain index may be an index that comprehensively evaluates brain function based on the above-mentioned indexes, and unlike IQ or EQ, in that it represents the response and control ability of the brain determined based on the measurement of brain waves and neurofeedback. However, it can contain more accurate and broader information.

In some embodiments, first interface 11 may further receive analysis parameters from first client 2 and/or second client 4 via network 5 . The brain wave processing unit 15 may select at least some of the brain wave processing functions based on the analysis parameters provided through the first interface 11 . For example, the EEG processor 15 may select at least one index from among the above-described indexes based on the analysis parameter, and may generate output data including the selected at least one index. In some embodiments, authentication system 7 may store information about the capabilities each of the valid clients requires. Accordingly, the authentication system 7 transmits analysis parameters corresponding to the identification information of the first client 2 and/or the second client 4 provided through the second interface 12 through the second interface 12. It may be provided to the brain wave processing unit 15, and the brain wave processing unit 15 may select at least one index based on the analysis parameter.

2 is a flowchart illustrating a method for testing and training based on neurofeedback according to an exemplary embodiment of the present invention. The client 22, the brain wave processing system 24, and the authentication system 26 of FIG. 2 may correspond to the first client 2, the brain wave processing system 10, and the authentication system 7 of FIG. In the case of the second client 4 of FIG. 1, the transmission of biometric information and the generation of output data corresponding to the biometric information in FIG. 2 may be omitted. Authentication may also be performed. Hereinafter, among the descriptions of FIG. 2 , contents overlapping those of the descriptions of FIG. 1 will be omitted.

Referring to FIG. 2 , in step S21, the client 22 may obtain biometric information and EEG data of the subject. For example, the client 22 may receive EEG data from an EEG measuring device worn by a test subject, and receive biometric information from an input provided by the test subject.

In step S22, the client 22 may transmit identification information, biometric information, and brain wave data to the brain wave processing system 24. The identification information is identification information of the client 22, and may be irrelevant to the subject who is the owner of the biometric information and brain wave data. The identification information is unique information of the client 22, and may include information issued from the authentication system 26, as described below with reference to FIG. 4 in some embodiments.

In step S23, the brain wave processing system 24 may transmit identification information to the authentication system 26. For example, in order to determine whether the biometric information and brain wave data provided in step S22 are provided from valid clients, the brain wave processing unit (eg, 15 in FIG. 1 ) of the brain wave processing system 24 transmits the identification information to the authentication system ( 26) can be provided.

At step S24, authentication system 26 may attempt to authenticate client 22. For example, the authentication system 26 may store information about valid clients and determine whether the client 22 is a valid client based on the identification information received in step S23. Then, in step S25, the authentication system 26 may transmit the authentication result to the EEG processing system 24. For example, the authentication result may indicate authentication success or authentication failure of the client 22 .

In step S26, the brain wave processing system 24 may generate output data based on the biometric information and brain wave data provided by the authenticated client 22. For example, the brain wave processing system 24 may generate output data when the authentication result provided in step S25 indicates that the authentication of the client 22 is successful. On the other hand, differently from that shown in FIG. 2, when the authentication result provided in step S25 indicates authentication failure of the client 22, the brain wave processing system 24 (or the brain wave processing unit) informs the client 22 of authentication failure. Data representing may be transmitted, and output data may not be generated.

In step S27, the EEG processing system 24 may transmit output data to the client 22. In step S28, the client 22 may provide output data to the test subject (or expert). For example, the output data may include at least one index generated by processing biometric information and brain wave data, and the client 22 provides the at least one index in various forms, such as numerical values and/or graphs, to the subject. can be displayed.

3 is a block diagram illustrating an EEG processing system 30 according to an exemplary embodiment of the present invention. Similar to the EEG processing system 10 of FIG. 1, the EEG processing system 30 of FIG. 3 includes a first interface 31, a second interface 32, a third interface 33, and an EEG processing unit 35. and may further include a client registration unit 37 and an encryption engine 39. In the description of FIG. 3, it is assumed that the EEG processing system 30 communicates with the first client 2, the second client 4, the authentication system 7, and the database 9 of FIG. Descriptions and redundant contents will be omitted.

The client registration unit 37 may communicate with the first interface 31 and the second interface 32 . Accordingly, the client registration unit 37 can communicate with the first client 2 and/or the second client 4 through the first interface 31 and the authentication system 7 through the second interface 32. ) can communicate with. The client registration unit 37 may perform a function of registering a new client to the authentication system 7 as a valid client. For example, the client registration unit 37 may receive registration requests of the first client 2 and/or the second client 4 through the first interface 31, and through the second interface 32. Registration of the first client 2 and/or the second client 4 may be requested to the authentication system 7 . An example of the operation of the client registration unit 37 will be described later with reference to FIG. 4 .

The encryption engine 39 may perform a decryption operation based on the encryption key. For example, the encryption engine 39 may receive a decryption key from the authentication system 7 through the second interface 32, and based on the decryption key, the encrypted data received through the first interface 31 It can be decoded by doing so, and the decoded data can be provided to the brain wave processing unit 35. An example of the operation of the encryption engine 39 will be described later with reference to FIG. 5 .

4 is a flowchart illustrating a method for testing and training based on neurofeedback according to an exemplary embodiment of the present invention. Specifically, the flowchart of FIG. 4 shows an example of the operation of the EEG processing system 30 of FIG. 3 . As described above with reference to FIG. 3 , brain wave processing system 44 of FIG. 4 may include a client registry, which may register client 42 with authentication system 46 as a valid client.

Referring to FIG. 4 , in step S41, the client 42 may transmit a registration request to the EEG processing system 44. In some embodiments, the registration request may include a hardware key. For example, when the client 42 of FIG. 4 is the first client 2 of FIG. 1, the brain wave measuring device worn by the test subject may be supplied by the operator of the brain wave processing system 44, and the hardware key is the brain wave measuring device. It can have a value unique to the measuring device and can be issued by its operator. In some embodiments, the hardware key may be written on the surface of the EEG processing device or a certificate, and the subject (or expert) may input the written hardware key into the client 42 . Also, in some embodiments, the EEG processing device may include a non-volatile memory (eg, flash memory, EPROM, OTP memory, etc.) that stores the hardware key, and the client 42 receives the hardware key from the EEG processing device. may receive. In addition, when the client 42 is the second client 4 of FIG. 1, the expert 4 sends a registration request including a key previously issued by the operator of the EEG processing system 44 to the EEG processing system 44. can transmit In some embodiments, the key may be included as a hardware key in a recording medium provided by an operator of the EEG processing system 44, such as a USB memory device, a CD, or the like.

In step S42, the EEG processing system 44 may verify the hardware key. For example, the EEG processing system 44 (or the client registration unit) may determine whether the hardware key included in the registration request of step S41 is legitimately issued. In some embodiments, the EEG processing system 44 may determine whether the hardware key is included in the hardware key list, and may verify the hardware key using an algorithm used in generating the hardware key. If the verification of the hardware key is successful, step S43 may be subsequently performed as shown in FIG. 4, while if the verification of the hardware key fails, the EEG processing system 44 transmits data representing the verification failure of the hardware key. to the client 42.

In step S43, the brain wave processing system 44 may request registration of the client to the authentication system 46. For example, the EEG processing system 44 may transmit the hardware key received in step S41 or data (eg, digest) generated from the hardware key to the authentication system 46 .

At step S44, authentication system 46 may register client 42. For example, the authentication system 46 may add the information contained in the request of the EEG processing system 44 to a list of valid clients. In some embodiments, authentication system 46 may maintain a list of clients (ie, 2 in FIG. 1 ) for subjects and a list of clients 4 for experts. Additionally, authentication system 46 may generate identification information and encryption keys. For example, the authentication system 46 may generate identification information and an encryption key based on information included in the request of the brain wave processing system 44, and accordingly, the identification information and the encryption key are bound to the corresponding information. ) can be For example, the authentication system 46 may generate a key pair including a public key and a private key based on an asymmetric encryption algorithm.

In step S45, the authentication system 46 may transmit the identification information and the encryption key to the EEG processing system 44. For example, the authentication system 46 may transmit the shared key to the brain wave processing system 44 as an encryption key. In step S46, the EEG processing system 44 may transmit identification information and an encryption key to the client 42. The client 42 may store the identification information and the encryption key received in step S46, and as described above with reference to FIG. 2, if the client 42 is the subject's client, the subject's data (ie, biometric information, EEG data) may be transmitted to the EEG processing system 44. An example of the operation of the client 42 using the encryption key received in step S46 will be described later with reference to FIG. 5 .

5 is a flowchart illustrating a method for testing and training based on neurofeedback according to an exemplary embodiment of the present invention. Specifically, the flowchart of FIG. 5 shows a case in which the client 52 is a client of a test subject (eg, 2 in FIG. 1 ) as an example of the operation of the EEG processing system 30 of FIG. 3 . As described above with reference to FIG. 3 , the EEG processing system 54 of FIG. 5 may include an encryption engine, and the encryption engine may decrypt encrypted data based on a decryption key. In the following, descriptions of FIG. 5 and overlapping descriptions of FIG. 2 will be omitted.

Referring to FIG. 5 , in step S51, the client 52 may encrypt the subject's biometric information and EEG data. For example, the client 52 may generate encrypted data by encrypting the subject's biometric information and brain wave data obtained from the brain wave measuring device using the encryption key of FIG. 4 . Then, in step S52, the client 52 may transmit the encrypted data and identification information to the EEG processing system 54. In step S53, the brain wave processing system 54 may transmit identification information to the authentication system 56, and in step S54, the authentication system 56 may authenticate the client 52 based on the identification information.

In step S55, the authentication system 56 may transmit the authentication result and the decryption key to the EEG processing system 54. For example, authentication system 56 may obtain a decryption key corresponding to an authenticated client 52 . In some embodiments, a key pair may be generated upon client registration in FIG. 4 (asymmetric encryption), the encryption key of FIG. 4 may be the shared key of the key pair, and the decryption key of FIG. 5 may be the private key of the key pair. there is. Also, in some embodiments, the encryption key of FIG. 4 and the decryption key of FIG. 5 may be the same (symmetric encryption).

In step S56, the brain wave processing system 54 may decode the data. For example, the brain wave processing system 54 (or encryption engine) may generate decrypted data by decrypting the encrypted data received in step S52 using the decryption key received in step S55. Any encryption algorithm may be used for data encryption and decryption, and symmetric encryption algorithms such as Known-PlainText, Chosen-PlainText, Differential, and Linear methods may be employed, and Diffie-Hellman key exchange, DSS (digital signature standard) ), ElGamal, ECC, Rivest-Shamir-Adleman (RSA), etc. may be employed.

In step S7, the brain wave processing system 54 may generate output data based on the decoded data, and in step S58, the brain wave processing system 54 may transmit the output data to the client 52. In step S59, the client 52 may provide output data to the test subject.

6 is a flowchart illustrating a method for testing and training based on neurofeedback according to an exemplary embodiment of the present invention. Specifically, the flowchart of FIG. 6 shows an example of a method of generating output data based on database 60 . In some embodiments, the method of FIG. 6 may be performed by the EEG processor 15 of FIG. 1 , and FIG. 6 will be described with reference to FIG. 1 below.

In step S62, an operation of acquiring reference data corresponding to biometric information may be performed. For example, the EEG processor 15 may receive reference data corresponding to biometric information received through the first interface 11 from the database 60 through the third interface 13 . In some embodiments, the EEG processor 15 may obtain EEG data, statistical data, and the like corresponding to a range of age and/or gender of the subject 1 as reference data.

In step S64, an operation of generating output data based on the EEG data and the reference data may be performed. For example, the EEG processor 15 may analyze the EEG data based on the reference data and generate output data including the analysis result.

In step S66, an operation of storing biometric information, EEG data, and/or output data in a database may be performed. For example, the brain wave processing unit 15 may store the biometric information and brain wave data used to generate the output data in the database 60, or may store the output data generated in step S64 in the database 60. Accordingly, in the database 60, biometric information and EEG information, excluding information for identifying the subject 1, may be collected, and the collected data may be used for subsequent processing of EEG data.

7 is a block diagram illustrating an EEG processing system 70 according to an exemplary embodiment of the present invention. Specifically, FIG. 7 shows an EEG processing system 70 including an EEG processing unit 75 and a model learning unit 76 as some components. As described above with reference to FIG. 1 and the like, the EEG processor 75 may receive biometric information and EEG data as input data IN, and may generate output data OUT by processing the EEG data.

Compared to the EEG processing system 10 of FIG. 1 , the EEG processing unit 75 of FIG. 7 may include a machine learning model (ML), and the EEG processing system 70 of FIG. 7 may include a model learning unit 76 may further include. The machine learning model ML may be in a state learned by a plurality of samples of identification information and EEG data, for example, identification information and EEG data of a plurality of subjects stored in the database 9 of FIG. 1, and the machine learning model ( ML) can be executed by any hardware designed to execute, such as a neural processing unit (NPU). The machine learning model ML may receive input data IN or processed data from the input data IN, and may output at least a portion of the output data OUT in response to the received data. The machine learning model (ML) may be a model based on an artificial neural network, a decision tree, a support vector machine, a regression analysis, a Bayesian network, a genetic algorithm, or the like.

The model learning unit 76 may train the machine learning model ML based on the input data IN and the output data OUT. For example, the machine learning model ML may include an artificial neural network, and the model learning unit 76 may train the machine learning model ML based on a training technique such as back propagation. . Accordingly, the brain wave processing unit 75 may re-learn the subject's biometric information and brain wave data. The machine learning model (ML) includes, but is not limited to, a convolution neural network (CNN), a region with convolution neural network (R-CNN), a region proposal network (RPN), a recurrent neural network (RNN), and a stacking neural network (S-DNN). -based deep neural network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restricted Boltzmann Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification networks, etc. may be included.

As above, exemplary embodiments have been disclosed in the drawings and specifications. Although the embodiments have been described using specific terms in this specification, they are only used for the purpose of explaining the technical idea of the present invention, and are not used to limit the scope of the present invention described in the claims or limiting meaning. . Therefore, those of ordinary skill in the art will understand that various modifications and equivalent other embodiments are possible therefrom. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the appended claims.

Claims (17)

As a system for testing and training based on neurofeedback,
a first interface configured to receive identification information of the first client, biometric information of the subject, and brain wave data generated by measuring the subject's brain wave, from a remote first client through a network;
a second interface configured to provide communication with an authentication system that stores information of registered first clients; and
The identification information is provided to the authentication system through the second interface, and when authentication of the first client is successful by the authentication system, the statistical value corresponding to the biometric information of the authenticated first client and the brain wave data An EEG processor configured to generate output data using values detected in the frequency domain of and provide the output data to the first client through the first interface;
The first interface is configured to receive settings for neurofeedback of the subject from a second client different from the first client through the network;
The system according to claim 1 , wherein the brain wave processing unit is configured to identify a setting corresponding to the identification information of the first client and to provide the identified setting to the first client through the first interface. delete The method of claim 1,
The setting includes at least one of a training protocol, a type of training program, an order of a training program, an execution time of a training program, a condition of a training program, a difficulty of a training program, a number of training sessions, and a grade of the training program. system. The method of claim 1,
the first interface is configured to receive an opinion on the output data from the second client;
The system characterized in that the brain wave processing unit is configured to provide the opinion to the first client through the first interface. The method of claim 4,
further comprising a third interface configured to provide communication with a database storing the settings and the opinion;
The system, characterized in that the brain wave processing unit is configured to obtain settings and opinions corresponding to the identification information of the first client from the database. The method of claim 1,
The output data includes at least one of a plurality of indices including a basal rhythm index, a self-regulation index, an attention index, an activity index, an emotional index, an antistress index, a left and right brain balance index, and a brain index. system. The method of claim 6,
the first interface is configured to further receive an analysis parameter from the first client;
The system, characterized in that the brain wave processing unit is configured to select at least one index from among the plurality of indexes based on the analysis parameter, and to generate the output data including the at least one index. The method of claim 6,
the second interface is configured to further receive an analysis parameter corresponding to the identification information from the authentication system;
The system, characterized in that the brain wave processing unit is configured to select at least one index from among the plurality of indexes based on the analysis parameter, and to generate the output data including the at least one index. The method of claim 1,
The first interface is configured to further receive a registration request including a hardware key from the first client;
The hardware key is verified, the authentication system requests registration of the first client through the second interface in response to the verified hardware key, and the encryption key provided from the authentication system is transmitted through the first interface. 1 system further comprising a client registry configured to provide to clients. The method of claim 9,
The biometric information and the brain wave data are encrypted based on the encryption key,
The system further comprising an encryption engine configured to receive a decryption key corresponding to the identification information from the authentication system through the second interface, and to decrypt the biometric information and the brainwave data based on the decryption key. The method of claim 1,
The system according to claim 1 , wherein the biometric information includes at least one of age, eyesight, height, weight, blood type, IQ, blood pressure, blood sugar, disease, and medicine to be taken of the subject. The method of claim 1,
Further comprising a third interface configured to provide communication with a database storing identification information and EEG data for a plurality of subjects;
The system, characterized in that the brain wave processing unit is configured to obtain the statistical value corresponding to the biometric information of the first client authenticated by the authentication system from the database through the third interface. The method of claim 12,
The brain wave processing unit is configured to store at least one of the biometric information, brain wave data, and output data of the authenticated first client in the database through the third interface. The method of claim 1,
The brain wave processing unit includes a machine learning model learned by a plurality of samples of identification information and brain wave data, and is configured to provide the authenticated biometric information and the brain wave data of the first client to the machine learning model. ,
Wherein the output data includes an output of the machine learning model. As a method for testing and training based on neurofeedback,
receiving identification information of the first client, biometric information of the subject, and brain wave data generated by measuring the subject's brain wave from a remote first client through a network;
providing the identification information to an authentication system that stores information of registered clients;
receiving statistical value data corresponding to the biometric information of the authenticated first client from a database when authentication of the first client by the authentication system succeeds;
generating output data using the statistical values and values detected in the frequency domain of the EEG data;
providing the output data to the first client;
receiving settings for neurofeedback of the subject from a second client different from the first client through the network;
identifying settings corresponding to the identification information of the first client; and
EEG processing method performed by a system comprising providing the identified settings to the first client via the network. The method of claim 15
receiving a registration request including a hardware key from the first client;
verifying the hardware key and requesting registration of the first client with the authentication system in response to the verified hardware key; and
Further comprising providing the encryption key provided from the authentication system to the first client, the brain wave processing method performed by the system. The method of claim 16
Receiving a decryption key corresponding to the identification information from the authentication system; and
Further comprising decoding the biometric information and the brain wave data based on the decoding key, the brain wave processing method performed by the system.

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