KR20220025477A - Method, device and system for test ad training based on neurofeedback - Google Patents

Abstract

A system comprises: a first interface for receiving, from a client through a network, brain wave data, generated by measuring identification information of the client, biometric information of a subject, and a brain wave of the subject; a second interface providing communication with an authentication system that stores information of valid clients; and a brain wave processing unit configured to provide identification information to the authentication system through the second interface, generate output data based on biometric information and brain wave data of the client authenticated by the authentication system, and provide the output data to the client through the first interface.

Description

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.

Neurofeedback is an important field of brain science that has been continuously researched and developed since it was first discovered at the University of Cambridge in England in 1934. or to enhance its function. For such neurofeedback, it may be required to accurately measure EEG from the subject, and it may be required to provide useful information to the subject from the measured EEG. For the former requirement, a high-performance EEG measurement device may be required, while for the latter requirement, a high-level algorithm and processing of a large amount of collected data may be required.

The technical idea of the present invention provides a method and system that can efficiently solve the high computational complexity of brain wave processing for neurofeedback and provide high convenience and security.

In order to achieve the above object, the 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 biometric information of the subject, and the brain wave of the subject from the first client through a network. A first interface for receiving EEG data, a second interface for providing communication with an authentication system that stores information of valid first clients, and a second interface for providing identification information to the authentication system through the second interface, the authentication system It may include an EEG processing unit that generates output data based on the biometric information and EEG data of the first client authenticated by , and provides the output data to the first client through the first interface.

According to an exemplary embodiment of the present invention, the first interface may receive a setting for neurofeedback of a subject from a second client through a network, and the EEG processing unit corresponds to the identification information of the first client The identified setting may be identified, and the identified setting may be provided to the first client through the first interface.

According to an exemplary embodiment of the present invention, the setting may be selected from among a training protocol, a type of training program, an order of a training program, an execution time of the training program, conditions of the training program, difficulty of the training program, the number of training sessions, and a grade of the training program. It may include 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 brain wave processing unit 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 third interface for providing communication with a database for storing settings and opinions, and the EEG processing unit, 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 EEG processing unit, based on biometric information and EEG data, basic rhythm index, self-regulation index, attention index, activity index, emotional index, anti-stress index, left and right brain balance index, brain A plurality of exponents including the exponent can be created.

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 processing unit selects at least one index among a plurality of indexes based on the analysis parameter, Output data including at least one index 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, based on the analysis parameter, includes at least one index among the plurality of indexes. may be selected, and output data including at least one index may be generated.

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 configure the second interface in response to the verified hardware key. Through this, a registration of the first client may be requested from the authentication system, 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, receive a decryption key corresponding to the identification information 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 a subject's age, eyesight, height, weight, blood type, IQ, blood pressure, blood sugar, disease, and medication.

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

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

According to an exemplary embodiment of the present invention, the EEG processing unit includes a machine learning model learned by a plurality of samples of identification information and EEG data, and uses the biometric information and EEG data of the authenticated first client as a machine learning model. and may obtain at least a portion of the output data from the machine learning model.

The brain wave processing method according to one aspect of the present invention comprises the steps of receiving, from a client, the client's identification information, the subject's biometric information, and the brain wave data generated by measuring the subject's brain wave, through a network, effective client information Providing identification information to an authentication system storing 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 from the authentication system in response to the verified hardware key and 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 the identification information from the authentication system, and decrypting the biometric information and EEG data based on the decryption key.

According to the method and system according to the technical idea of the present invention, it is possible to release the operation of high computational complexity from the client, and accordingly, a test and training service for neurofeedback can be extensively and easily constructed.

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

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 art 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 of practicing the present invention may also be derived by a person of ordinary skill in the art from the embodiments of the present invention.

1 is a block diagram showing 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 of ordinary skill in the art. Since the present invention may have various changes and may have various forms, specific embodiments will be illustrated in the drawings and described in detail. However, this is not intended to limit the present invention to the specific disclosed form, it should be understood to include all modifications, equivalents and substitutes included in the spirit and scope of the present invention. In describing each figure, like reference numerals are used for like elements.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present application, terms such as “comprise” or “have” are intended to designate that a feature, number, step, operation, component, part, or a combination thereof described in the specification exists, but one or more other features It should be understood that the existence or addition of numbers, steps, operations, components, parts or combinations thereof is not precluded in advance.

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 a commonly used dictionary 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, it should be interpreted in an ideal or excessively formal meaning doesn't happen

In the drawings and description below, a component indicated or described as one block may be a hardware block or a software block. For example, each of the components may be an independent hardware block that sends and receives signals to and from 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 brain wave processing system 10 may be connected to a network 5 , and may communicate with a first client 2 and a second client 4 through the network 5 . Further, the brainwave processing system 10 may communicate with an authentication system 7 and a database 9 . In some embodiments, different from that shown in FIG. 1 , the authentication system 7 and the database 9 may connect to the network 5 , and the brain wave processing system 10 may connect to the authentication system via the network 5 . (7) and a database (9).

The first client 2 may generate EEG data by measuring EEG of the subject 1 . For example, the subject 1 may wear an EEG measuring device disclosed in Korean Patent Publication No. 10--2126002, to which the applicant of the present application is a patent holder, and the first client 2 may receive a plurality of EEG from the EEG measuring device. Signals sensed through channels may be received. Also, the first client 2 may receive biometric information input from the subject 1 , such as age, eyesight, height, weight, blood type, IQ, blood pressure, blood sugar, disease, medication, and the like. In some embodiments, the first client 2 may be a personal computer, such as a desktop computer, a laptop computer, and may be communicatively connected to an EEG measuring device. In addition, the first client 2 may provide the output data provided from the brain wave processing system 10 through the network 5 to the subject 1, for example, through a display, a print, or the like. In some embodiments, the EEG data may include a series of samples generated by sensing EEG through a plurality of channels over time, and the first client 2 transmits EEG data through a 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 "neural biofeedback", and may refer to a technique for improving the homeostatic self-regulating ability of the brain by measuring EEG and selectively feeding back this signal with one's brain. Neurofeedback is an important field of brain science, and a lot of research is being done, new training techniques are being proposed, and new facts are being discovered. For example, plasticity of the brain, a characteristic that the brain develops the more it is used and develops a neural network on its own as needed, was discovered, and neurogenesis, in which neurons, that is, neurons, are constantly newly created. Accordingly, new brain cells can be continuously created, and a new neural network can be created through learning, so that the brain can continuously develop, and neurofeedback can maximize these brain characteristics. The first client 2 may communicate with the brain wave processing system 10 through the network 5, and as will be described later, as the expert 3 sets the brain wave processing system 10 in the subject 1, Neurofeedback can be provided. The first client 2 may provide feedback to the subject 1 through various output devices, such as a display device, a speaker, etc., and feedback (eg, sound, vibration, etc.) through output devices included in the EEG measurement device. ) may be communicated with the EEG measurement device to be provided.

The first client 2 may be, as a non-limiting example, the subject 1'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 with the expert 3, for example, may be a computing system (eg, a kiosk) operated in a center or institution that provides testing and training based on neurofeedback. , may be a computing system operated in a health examination center that provides 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 1 own computer, but it will be understood that the exemplary embodiments of the present invention are not limited thereto.

The expert 3 can connect to the brainwave processing system 10 to the second client 4 and to the network 5 . For example, the expert 1 may be an experienced counselor, a doctor, etc. related to the subject 1, and set various settings for neurofeedback of the subject 1 through the second client 4 to the EEG processing system ( 10) can be provided. Similar to the first client 2 , the second client 4 may be, by way of non-limiting example, the subject 1 's own computer, such as a desktop computer, a laptop computer, a smart phone, a tablet PC, and the like. In addition, the second client 4 may be in the same space as the first client 2 or may be remote. Also, in some embodiments, when the subject 1 and the expert 3 are in the same place, 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 brain wave processing system 10 may connect to the network 5 , for example, the network 5 may be a wide area network such as the Internet, and accordingly The physical distance between the first client 2 , the second client 4 and the brain wave 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), SMR (about 12-15 Hz, awake state) , low beta (about 13 to 20 Hz, active state), high beta (about 21 to 30 Hz, active, excited, stressed state), and gamma (about 31 Hz or higher, active, information binding state), etc. , a processing algorithm for each type may be different. As will be described later, the brainwaves of the subject 1 acquired by neurofeedback may be processed based on a vast amount of brainwave data collected and stored in the database 9 as well as the brainwaves previously measured from themselves. Accordingly, it may not be practically easy to perform EEG 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.

Hereinafter, 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 , EEG data generated by measuring EEG of the subject 1 by neurofeedback based on the setting by 2 can be provided to the client (4). Accordingly, brain wave processing of high computational complexity can be efficiently performed in the brain wave processing system 10 having higher performance than the first client 2 . In addition, EEG data and biometric information of the subject 1 required for EEG processing can be separated from identification information for authentication of the first client 2, and thus problems such as personal information leakage can be solved, High security can be achieved. As a result, the brain wave processing system 10 can make it possible to build a wide range of services based on neurofeedback.

1 , the EEG processing system 10 may include a first interface 11 , a second interface 12 , a third interface 13 , and an EEG processing unit 15 . The first interface 11 may provide a connection to the network 5 to the brain wave processing system 10 . For example, the first interface 11 is connected to the first client 2 from the first client 2 through the network 5, identification information of the first client 2, biometric information of the subject 1, and EEG data of the subject 1 can receive, from the second client 4, identification information of the second client 4, biometric information of the expert 3, setting information of the expert 3 for the subject 1, etc. can be received . In addition, the first interface 11 may provide the output data provided from the brain wave processing unit 15 to the first client 2 and/or the second client 4 through the network 5 .

The second interface 12 may 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 will be described later 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 may authenticate the first client 2 and the second client 4 . Accordingly, the 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 that can identify the subject 1, while the first client 2 has its own unique identification information (eg, in FIG. 4 ). hardware key) to the brain wave processing system 10 to be authenticated. The second interface 12 may communicate with the authentication system 7 via a local network, or may communicate with the authentication system 7 via a one-to-one connection.

The third interface 13 may provide access to the database 9 . The database 9 may store biometric information and EEG data of a plurality of subjects. In some embodiments, the 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 . Different 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 data stored in the database 9 . may be considered, but it may not be easy for hardware/software to process a large amount of data stored in the database 9 to be implemented in the first client 2, and as an operating subject of the first client 2 may incur high costs. In addition, it may be possible only when the subject 1 and the expert 3 are in the same space to allow the expert 3 to set the first client 2 a setting suitable for the subject 1 . The EEG processing system 10 provides EEG processing results to the first client 2 and the second client 4 that can be implemented at low cost based on high security, thereby constructing a service based on neurofeedback can facilitate

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

In some embodiments, the database 9 may store EEG data and training achievement generated by the subject 1 through neurofeedback training, and the EEG processing unit 15 may store EEG data of the subject 1 according to the training. and change in achievement can be analyzed. In addition, the EEG 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 EEG processing unit 15 may train the subject 1 in the future. and direction can be determined. In some embodiments, the EEG processing unit 15 may receive the 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, the training protocol, the type of training program, the sequence of the training program, the execution time of the training program, the conditions of the training program, the difficulty of the training program, the number of training, The level of the training program and the like may be set for the subject 1 . The brain wave processing unit 15 may identify a setting corresponding to the identification information of the first client 2 so that neurofeedback is performed based on the setting stored in the database 9, and the first client 2 identified The settings may be provided, and the first client 2 may provide neurofeedback to the subject 1 according to the settings provided from the EEG processing system 10 . In some embodiments, the expert 3 may check the brain wave data and/or data generated by the brain wave processing unit 15 from the brain wave data of the subject 1 through the second client 4 , and the subject 1 ) by analyzing the EEG data and/or achievement, it is possible to provide a comment (comment) of the person to the EEG processing system (10). The brain wave processing unit 15 may store the opinion provided from the expert 3 in the database 9, may identify the opinion corresponding to the identification information of the first client 2, and display the identified opinion to the first client ( It can be provided to the subject (1) through 2). For example, the BQ report of the subject 1 and the training result report may be prepared by the expert 3 , may be stored in the database 9 , and may be provided to the subject 1 .

In some embodiments, the database 9 may store, in EEG data collected by a plurality of subjects, statistical values (eg, average, dispersion) of the aforementioned indices corresponding to various conditions (eg, age, etc.) may be stored, and the EEG processing unit 15 may generate output data by analyzing EEG data based on statistical values corresponding to the biometric information of the subject 1 . In some embodiments, samples in the time domain included in the EEG data may be transformed into the frequency domain through, for example, Fourier transform, and the EEG may be analyzed in the frequency domain. For example, peaks, a maximum peak value, coherence, etc. may be detected in the frequency domain, and indices to be described later may be calculated based on the detected values.

The EEG processing unit 15 includes various indices including basic rhythm index, self-regulation index, attention index, activity index, emotional index, anti-stress index, left and right brain balance index, brain index by processing biometric information and EEG data. output data can be generated. Basal rhythm is an EEG that is emitted when the eyes are closed and is resting in a pseudo state, and the standards may be different depending on age, and may be used as a basis for judging 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 regulating three states of rest, attention, and concentration upon awakening, and the self-regulation index is a value that evaluates the brain's ability to self-regulate for these three states. can The attention index is an index indicating the degree of brain arousal and resistance to disease or stress, and the degree of brain arousal may be determined according to age standards. The activity index, as an index indicating the degree of brain activity, may be used as a basis for judging mental activity, thinking ability, and behavioral tendency. The emotional index may be referred to as an emotional index, and may indicate emotional stability and instability. The anti-stress index (or stress index) may indicate resistance to physical and mental fatigue caused by internal and external environmental factors. Physical stress can represent a state of tension, anxiety, and excitement of the human body, while mental stress can represent a state of psychological tension, anxiety, and excitement. The left-right balance brain index is an index indicating the balance between the left and right brain, and the balance may be determined based on the symmetry of the amplitude and the phase of the EEG. Brain index may be an index that comprehensively evaluates brain function based on the above-mentioned indices, and is different from IQ or EQ in that it represents the brain's response and control ability determined based on brain wave measurement and neurofeedback. , it can contain more accurate and broader information.

In some embodiments, the first interface 11 may further receive the analysis parameter from the first client 2 and/or the second client 4 via the 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 only through the first interface 11 . For example, the EEG processing unit 15 may select at least one index from among the above-described indices based on the analysis parameter, and may generate output data including the selected at least one index. In some embodiments, the authentication system 7 may store information about the functions required by each of the valid clients. Accordingly, the authentication system 7 transmits, via the second interface 12 , the analysis parameters corresponding to the identification information of the first client 2 and/or the second client 4 provided via the second interface 12 . It may be provided to the EEG processing unit 15, and the EEG 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. 1 . In the case of the second client 4 of FIG. 1 , transmission of biometric information and generation of output data corresponding to the biometric information may be omitted in FIG. 2 , and similarly as shown in FIG. 2 , Authentication may also be performed. Hereinafter, content overlapping with the description of FIG. 1 among the description of FIG. 2 will be omitted.

Referring to FIG. 2 , in step S21 , the client 22 may acquire the subject's biometric information and EEG data. For example, the client 22 may receive EEG data from an EEG measurement device worn by the subject, and may receive biometric information from an input provided by the subject.

In step S22 , the client 22 may transmit identification information, biometric information, and EEG data to the EEG processing system 24 . The identification information is identification information of the client 22 , and may be independent of the subject who is the owner of the biometric information and EEG data. The identification information is information unique to 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 the identification information to the authentication system 26 . For example, the brain wave processing unit (eg, 15 in FIG. 1 ) of the brain wave processing system 24 transmits identification information to the authentication system ( 26) can be provided.

In step S24 , the authentication system 26 may attempt to authenticate the client 22 . For example, the authentication system 26 may store information about valid clients, and may 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 brain wave processing system 24 . For example, the authentication result may indicate authentication success or authentication failure of the client 22 .

In step S26 , the EEG processing system 24 may generate output data based on the biometric information and EEG 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 authentication success of the client 22 . On the other hand, different 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 brain wave processing unit) fails to authenticate the client 22 . may transmit data representing , and may not generate output data.

In step S27 , the brain wave processing system 24 may transmit the output data to the client 22 . In step S28, the client 22 may provide the output data to the subject (or expert). For example, the output data may include at least one index generated by processing biometric information and EEG data, and the client 22 provides the at least one index to the subject in various forms, such as numerically and/or graphically. 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 registrar 37 and an encryption engine 39 . In the description of FIG. 3 , it is assumed that the brain wave processing system 30 communicates with the first client 2 , the second client 4 , the authentication system 7 and the database 9 of FIG. 1 , Contents overlapping with the description will be omitted.

The client registrar 37 may communicate with the first interface 31 and the second interface 32 . Accordingly, the client registrar 37 may communicate with the first client 2 and/or the second client 4 via the first interface 31 , and via the second interface 32 , the authentication system 7 . ) can communicate with The client registration unit 37 may perform a function of registering a new client with the authentication system 7 as a valid client. For example, the client registrar 37 may receive a registration request of the first client 2 and/or the second client 4 via the first interface 31 , and via the second interface 32 . Registration of the first client 2 and/or the second client 4 may be requested from the authentication system 7 . An example of the operation of the client registrar 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 via the second interface 32 , and based on the decryption key the encrypted data received via the first interface 31 . can be decoded, 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 below 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 operation of the brain wave processing system 30 of FIG. 3 . As described above with reference to FIG. 3 , the brainwave processing system 44 of FIG. 4 may include a client registrar, which may register the client 42 with the 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 EEG measurement device worn by the subject may be supplied from an operator of the EEG processing system 44 , and the hardware key is the EEG It may have a value unique to the measuring device and may be issued by the operator in question. In some embodiments, the hardware key may be written on the surface of the brain wave processing device, a certificate, or the like, and the subject (or expert) may input the written hardware key into the client 42 . Further, in some embodiments, the brain wave 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 brain wave 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 issued in advance from the operator of the EEG processing system 44 to the EEG processing system 44 . can be transmitted In some embodiments, the key may be included in a recording medium provided by the operator of the brain wave processing system 44, such as a USB memory device, a CD, etc. as a hardware key.

In step S42, the brain wave processing system 44 may verify the hardware key. For example, the brain wave 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 brain wave processing system 44 may determine whether the hardware key is included in the hardware key list, and 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 returns data indicating the failure of the verification of the hardware key It can be transmitted to the client 42 .

In step S43 , the brain wave processing system 44 may request the authentication system 46 to register the client. For example, the brain wave 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 .

In step S44 , the authentication system 46 may register the client 42 . For example, the authentication system 46 may add the information included in the request of the brain wave 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. The authentication system 46 may also generate identification information and encryption keys. For example, the authentication system 46 may generate the identification information and the encryption key based on the 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 brain wave processing system 44 . For example, the authentication system 46 may transmit the shared key to the brainwave processing system 44 as an encryption key. In step S46 , the brain wave processing system 44 may transmit the identification information and the 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 , when the client 42 is a client of the subject, the subject's data (ie, biometric information; brain wave data) together with the brain wave 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 illustrates a case in which the client 52 is a client of a subject (eg, 2 in FIG. 1 ) as an example of the operation of the brain wave processing system 30 of FIG. 3 . As described above with reference to FIG. 3 , the brain wave processing system 54 of FIG. 5 may include an encryption engine, and the encryption engine may decrypt encrypted data based on a decryption key. Hereinafter, content overlapping with the description of FIG. 2 among the description of FIG. 5 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 EEG data obtained from the EEG 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 brain wave processing system 54 . In step S53 , the brain wave processing system 54 may transmit the 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 brain wave processing system 54 . For example, the authentication system 56 may obtain a decryption key corresponding to the 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 a shared key of the key pair, and the decryption key of FIG. 5 may be a 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 encryption and decryption of data, for example, a symmetric encryption algorithm such as Known-PlainText, Chosen-PlainText, Differential, Linear method may be employed, Diffie-Hellman key exchange, DSS (digital signature standard) ), an asymmetric encryption algorithm such as 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 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 the database 60 . In some embodiments, the method of FIG. 6 may be performed by the EEG processing unit 15 of FIG. 1 , and FIG. 6 will be described below with reference to FIG. 1 .

In step S62, an operation of obtaining reference data corresponding to the biometric information may be performed. For example, the brain wave processing unit 15 may receive reference data corresponding to the biometric information received through the first interface 11 from the database 60 through the third interface 13 . In some embodiments, the EEG processing unit 15 may obtain EEG data, statistical data, etc. corresponding to the age and/or gender range 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 brain wave processing unit 15 may analyze the brain wave data based on the reference data, and may generate output data including the analysis result.

In step S66, an operation of storing biometric information, brain wave data, and/or output data in the database may be performed. For example, the EEG processing unit 15 may store the biometric information and EEG data used to generate the output data in the database 60 , or store the output data generated in step S64 in the database 60 . Accordingly, biometric information and EEG information may be collected in the database 60 , except for information that can identify the subject 1 , 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 processing unit 75 may receive biometric information and EEG data as input data IN, and may generate output data OUT by processing the EEG data.

Compared with 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 is 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) may be executed by any hardware designed to execute, for example, a neural processing unit (NPU). The machine learning model ML may receive the 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 learner 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 EEG processing unit 75 may relearn the subject's biometric information and EEG data. Machine learning model (ML), as a non-limiting example, CNN (Convolution Neural Network), R-CNN (Region with Convolution Neural Network), RPN (Region Proposal Network), RNN (Recurrent Neural Network), S-DNN (Stacking) -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, It may include a classification network and the like.

Exemplary embodiments have been disclosed in the drawings and specification as described above. Although the embodiments have been described using specific terms in this specification, these are used only for the purpose of explaining the technical idea of the present invention and not used to limit the meaning or the scope of the present invention described in the claims . Therefore, it will be understood by those skilled in the art that various modifications and equivalent other embodiments are possible therefrom. Accordingly, the true technical protection scope of the present invention should be defined by the technical spirit of the appended claims.

Claims (17)

a first interface, configured to receive, from a first client, identification information of the first client, biometric information of the subject, and EEG data generated by measuring EEG of the subject;
a second interface configured to provide communication with an authentication system that stores information of valid first clients; and
providing the identification information to the authentication system through the second interface, generating output data based on the biometric information and the EEG data of the first client authenticated by the authentication system, and using the first interface and an EEG processor configured to provide the output data to the first client via The method according to claim 1,
The first interface is configured to receive settings for neurofeedback of the subject from a second client via the network,
The EEG processing unit is configured to identify a setting corresponding to the identification information of the first client, and provide the identified setting to the first client via the first interface. 3. The method according to claim 2,
The setting comprises 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, conditions of a training program, difficulty of a training program, the number of training sessions, and a grade of the training program system. 3. The method according to claim 2,
the first interface is configured to receive an opinion on the output data from the second client;
The EEG processing unit is configured to provide the opinion to the first client via the first interface. 5. The method of claim 4,
a third interface configured to provide communication with a database storing the settings and the comments;
The EEG processing unit is configured to obtain settings and opinions corresponding to the identification information of the first client from the database. The method according to claim 1,
The brain wave processing unit, based on the biometric information and the brain wave data, a plurality of indices including basic rhythm index, self-regulation index, attention index, activity index, emotional index, anti-stress index, left and right brain balance index, brain index A system configured to generate 7. The method of claim 6,
the first interface is configured to further receive an analysis parameter from the first client,
The EEG processing unit is configured to select at least one exponent from among the plurality of exponents based on the analysis parameter, and generate the output data including the at least one exponent. 7. 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 EEG processing unit is configured to select at least one exponent from among the plurality of exponents based on the analysis parameter, and generate the output data including the at least one exponent. the first interface is configured to further receive a registration request including a hardware key from the first client;
verify the hardware key, request registration of the first client with the authentication system through the second interface in response to the verified hardware key, and send the encryption key provided from the authentication system through the first interface 1 The system further comprising a client registrar configured to provide to clients. 10. The method of claim 9,
The biometric information and the brain wave data are encrypted based on the encryption key,
and an encryption engine configured to receive a decryption key corresponding to the identification information from the authentication system through the second interface, and decrypt the biometric information and the EEG data based on the decryption key. The method according to claim 1,
The biometric information may include at least one of age, eyesight, height, weight, blood type, IQ, blood pressure, blood sugar, disease, and medication of the subject. The method according to claim 1,
a third interface configured to provide communication with a database storing brain wave data and identification information for the plurality of subjects;
The brain wave processing unit obtains reference data corresponding to the biometric information of the first client authenticated by the authentication system from the database through the third interface, and processes the brain wave data based on the reference data. A system configured to generate output data. 13. The method of claim 12,
The EEG processing unit is configured to store at least one of the biometric information, EEG data, and the output data of the authenticated first client in the database through the third interface. The method according to 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 provides the biometric information and the brain wave data of the authenticated first client to the machine learning model, and obtain at least a portion of the output data from a machine learning model. receiving, from a client, identification information of the client, biometric information of a subject, and EEG data generated by measuring EEG of the subject through a network;
providing the identification information to an authentication system that stores information of valid clients;
receiving reference data corresponding to the biometric information of the client authenticated by the authentication system from a database;
generating output data by processing the brain wave data based on the reference data; and
EEG processing method comprising the step of providing the output data to the client. 16. The method of claim 15,
receiving a registration request including a hardware key from the client;
verifying the hardware key and requesting registration of the client from the authentication system in response to the verified hardware key; and
EEG processing method further comprising the step of providing the encryption key provided from the authentication system to the client. 17. The method of claim 16,
receiving a decryption key corresponding to the identification information from the authentication system; and
The method further comprising the step of decrypting the biometric information and the brain wave data based on the decryption key.

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