WO2016047922A1

WO2016047922A1 - Method for predicting change in user characteristics by using eeg data

Info

Publication number: WO2016047922A1
Application number: PCT/KR2015/008486
Authority: WO
Inventors: 이기원
Original assignee: (주)와이브레인
Priority date: 2014-09-26
Filing date: 2015-08-13
Publication date: 2016-03-31
Also published as: KR101538916B1

Abstract

Provided is a method for predicting a change in user characteristics by using EEG data. According to one embodiment of the present invention, the method for predicting a change in user characteristics by using EEG data comprises the steps of: providing a transcranial current stimulation (tCS) database in which EEG change data for indicating changes in EEG data of subjects according to a tCS is stored for a plurality of subjects, wherein the EEG change data is stored by being classified according to subject characteristics; obtaining a user's EEG data; extracting, from the tCS database, EEG change data for a subject having characteristics corresponding to user characteristics derived from the user's EEG data and/or user characteristics obtainable from the user; and predicting, in consideration of the extracted EEG change data, a change in user characteristics, occurring when the tCS is applied to the user.

Description

How to predict changes in user characteristics using EEG data

The present invention relates to a method of predicting a change in user characteristics using electroencephalogram (EEG) data, and more particularly, to a change in user characteristics occurring when transcranial current stimulation (tCS) is applied to a user. It relates to a method for predicting using.

Electroencephalogram (EEG) screening technology can predict brain activity by measuring changes in the scalp's potential with brain activity. In particular, the measured EEG data may be used to determine the condition of the patient.

On the other hand, brain electric stimulation technology using a transcranial current stimulation (tCS) is known to be effective in improving cognitive ability and treating mental disorders such as depression and Attention Deficit Hyperactivity Disorder (ADHD).

Therefore, electroencephalography and brain electrical stimulation techniques can be used to identify brain dysfunction, seizures, infections, metabolic diseases, etc., as well as to enable continuous treatment of mental disorders by activating or inhibiting neuronal connections. Can be.

However, the conventional EEG technology focuses on understanding the current state of the patient. However, using the accumulated EEG data effectively, beyond knowing the patient's current state, can predict how the patient's condition will change. Moreover, the accumulated EEG data may be used to predict changes in the patient's condition that may occur when the patient is subjected to transcranial current stimulation.

The technical problem to be solved by the present invention is to provide a method for predicting a change in the user characteristics that can occur when applying a transcranial current stimulation to the user by using the EEG data.

Another technical problem to be solved by the present invention is to provide a method of predicting a change in user characteristics using a plurality of EEG data having a difference in measurement time.

The technical problems of the present invention are not limited to the above-mentioned technical problems, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

In order to solve the above-mentioned technical problems, a method of predicting a change in user characteristics using EEG data according to an embodiment of the present invention, representing a change in the EEG data of the target according to the cranial current stimulation (tCS) Providing a tCS database in which EEG change data is stored for a plurality of objects, wherein the EEG change data is classified and stored according to object characteristics; Obtaining EEG data of the user; Extracting EEG change data for an object having a characteristic corresponding to at least one of a user characteristic derived from the EEG data of the user or a user characteristic obtainable from the user, from the tCS database; And taking into account the extracted EEG change data, predicting a change in user characteristics that occurs when applying a transcranial current stimulus to the user.

According to another aspect of the present invention, there is provided a method of predicting a change in user characteristics using EEG data, wherein the EEG data of a plurality of targets is classified and stored according to the target characteristics. Providing; Acquiring EEG data of a user, wherein acquiring a plurality of EEG data having a difference in measurement time; Comparing EEG data of the user with the EEG data of the object stored in the EEG database, and extracting the EEG data of the object corresponding to the EEG data of the user; Deriving a user characteristic of the user by using a target characteristic for the extracted EEG data; And predicting a change in the user characteristic in consideration of a change in the user characteristic derived according to the plurality of EEG data of the user.

According to the present invention as described above, it is possible to predict the change in the user characteristics that can occur when applying the cranial current stimulation to the user using the EEG data. The predicted results can then be used to adjust the protocol of the transcranial current stimulus to change the user characteristics by a predetermined amount of change.

In addition, according to the present invention, it is possible to predict a change in user characteristics by using a plurality of EEG data having a difference in measurement time.

1 is a flowchart of a method of predicting a change in user characteristics using EEG data according to a first embodiment of the present invention.

FIG. 2 is a flowchart illustrating a method of deriving a user characteristic from user's EEG data in S30 of FIG. 1.

3 is a flowchart illustrating specific steps of S40 of FIG. 1.

4 is a flowchart of a method of predicting a change in user characteristics using EEG data according to a second embodiment of the present invention.

5 illustrates a visual display of user characteristics.

6 is a flowchart of a method of predicting a change in user characteristics using EEG data according to a third embodiment of the present invention.

7 is a flowchart of a method of predicting a change in user characteristics using EEG data according to a fourth embodiment of the present invention.

8 is a flowchart of a method of predicting a change in user characteristics using EEG data according to a fifth embodiment of the present invention.

9 illustrates a visual display of a user characteristic.

FIG. 10 is a diagram illustrating a system capable of performing a method of predicting a change of a user characteristic using EEG data according to embodiments of the present invention.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. Advantages and features of the present invention and methods for achieving them will be apparent with reference to the embodiments described below in detail with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various forms, and only the embodiments are intended to complete the disclosure of the present invention, and the general knowledge in the art to which the present invention belongs. It is provided to fully inform the person having the scope of the invention, which is defined only by the scope of the claims. Like reference numerals refer to like elements throughout.

Unless otherwise defined, all terms (including technical and scientific terms) used in the present specification may be used in a sense that can be commonly understood by those skilled in the art. In addition, the terms defined in the commonly used dictionaries are not ideally or excessively interpreted unless they are specifically defined clearly.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, "comprises" and / or "comprising" does not exclude the presence or addition of one or more other components in addition to the mentioned components.

Hereinafter, a method of predicting a change in user characteristics using EEG data according to embodiments of the present invention will be described with reference to the drawings.

1 to 3, a method of predicting a change in user characteristics using EEG data according to a first embodiment of the present invention will be described. Referring to FIG. 1, a flowchart of a method of predicting a change in a user characteristic using EEG data according to a first embodiment is disclosed. Referring to FIG. 2, FIG. 2 is an example of a user's EEG data in S30 of FIG. 1. A flowchart for describing a method of deriving a user characteristic is disclosed. Referring to FIG. 3, a flowchart illustrating specific steps of S40 of FIG. 1 is disclosed.

According to a method of predicting a change in user characteristics using EEG data according to the first embodiment of the present invention, by using the EEG data stored in the tCS database, You can predict the change reliably.

To this end, first, with reference to FIG. 1, an EEG change data of a target according to the transcranial current stimulus tCS may be provided with a tCS database stored for a plurality of targets (S10). Here, the subject may mean, for example, a subject that is an object of transcranial current stimulus and provides EEG change data.

Specifically, the EEG change data indicating the change of the EEG data of the subject according to the cranial current stimulation may be provided for the tCS database stored for the plurality of subjects, and the EEG change data may be classified according to the object characteristics and stored in the tCS database. Can be.

The EEG change data stored in the tCS database may represent a change in the EEG data generated when applying a head and neck current stimulus to the subject. In other words, the EEG data is the data that can be obtained through the EEG test or EEG measurement, the EEG change data is a difference between the two in that it represents the change of the EEG data according to the cranial current stimulation.

The EEG change data may be measured, for example, by measuring the EEG data of the subject before applying the head and neck current stimulus to the subject, and after applying the head and neck current stimulus to the subject. EEG data of the subject may be included, but is not limited thereto.

Also, in some embodiments, the EEG change data may include information about the type or protocol of the transcranial current stimulus applied to the subject. Types of transcranial current stimulation applied to a subject may include, but are not limited to, transcranial direct current stimulation (tDCS) or transcranial alternating current stimulation (tACS). . In addition, as a protocol for transcranial current stimulation, information about the magnitude of the current applied to the subject or the stimulation time may be included in the EEG change data. Therefore, by confirming the EEG change data, it is possible to determine which transcranial current stimulus caused the change in the EEG data.

On the other hand, as described above, the EEG change data may be classified according to the target characteristics and stored in the tCS database. To this end, the EEG change data may include information on object characteristics reflecting at least one of personal information, environmental information, and disease information about the corresponding object. Specifically, the personal information may include, for example, information on the subject's age, gender, condition information (sleepiness, stress, etc.), and an emotional state, and the environmental information may include information about a subject's living area, etc. The information may include, but is not limited to, information about the type of disease possessed by the subject and the severity of the disease (hardness, moderate, altitude, etc.).

Information about the subject characteristics may be obtained from the subject or derived from EEG data before the transcranial current stimulus is applied to the subject, but is not limited thereto. The derivation of information on the object characteristic from the EEG data before the transcranial current stimulus is applied to the object may be possible by checking the pattern of the EEG representing the object characteristic from the EEG data, but is not limited thereto.

Therefore, according to the method for predicting the change of the user characteristic using the EEG data according to the first embodiment, since each of the EEG change data stored in the tCS database is classified according to the target characteristic, the user of the user in the step described later It may be easy to extract EEG change data of a target having a characteristic corresponding to the characteristic.

In addition, according to the method for predicting the change of the user characteristic using the EEG data according to the first embodiment, since sufficient accumulated data is secured in that the EEG change data for a plurality of objects is stored in the tCS database, Reliability can be increased for predicting changes in user characteristics.

Subsequently, with reference to FIG. 1, EEG data of the user may be acquired (S20).

Specifically, the EEG data of the user may be obtained by directly measuring the EEG data from the user using an EEG measuring device, but the present invention is not limited thereto, and the user may obtain the EEG data of the user by receiving the measured EEG data of the user. It may be.

Meanwhile, in the present specification, a user and an object refer to objects that are distinguished from each other, and a user is an object for which a change in user characteristics is predicted using EEG data, and the object provides EEG change data to predict a change in user characteristics. It may be a target. However, past EEG change data or past EEG data for the user may be stored in the tCS database or the EEG database, respectively, in which case the user may be one of the targets. That is, the data stored in the tCS database or the EEG database may be historical data of the user or historical data of another person.

Subsequently, with reference to FIG. 1, EEG change data for an object having a characteristic corresponding to the user characteristic may be extracted (S30).

Specifically, the EEG change data for the object having a characteristic corresponding to at least one of a user characteristic derived from the EEG data of the user or a user characteristic obtainable from the user may be extracted from the tCS database.

For reference, the user characteristic may reflect at least one of personal information, environmental information, and disease information. Specifically, the personal information may include, for example, information about the user's age, gender, condition information (sleepiness, stress, etc.) and emotional state, and the environmental information may include information about the user's living area, etc. The information may include, but is not limited to, information about the type of disease possessed by the user and the severity of the disease (hardness, moderate, altitude, etc.).

In this step, EEG change data that can be used as basic data for prediction can be detected to predict a change in user characteristics that occurs when a user applies a head and neck current stimulus. However, since the EEG change data of the object is used as the basic data for prediction based on the similarity or identity between the user and the object, the EEG change data for the object having a common point with the user should be detected. To this end, it is necessary to extract the EEG change data for the object having a characteristic corresponding to at least one of the user characteristics of the user.

For example, with respect to disease information among user characteristics, EEG change data for an object having the same disease as the user and the same severity of the user and the disease may be detected. To this end, various detection methods may be used, and EEG change data having a pattern corresponding to a specific region representing disease information among user EEG data may be extracted. Specifically, the EEG data of the subject measured before applying the head and neck current stimulus to the subject among the EEG change data may be used for comparison in the present extraction process, but is not limited thereto.

However, even if the EEG data is not directly compared with each other, in relation to disease information among user characteristics, EEG change data for an object having the same disease as the user and having the same severity of the disease as the user may be detected. For example, if the target characteristic is pre-classified for the EEG change data and the user characteristic is pre-derived for the user's EEG data, the user characteristic of the user is compared by comparing the classification result for the target characteristic with the derivation result for the user characteristic. EEG change data for an object having a characteristic corresponding to at least one of the data may be extracted.

In addition, with respect to personal information or environment information among user characteristics, EEG change data for an object of the same age as the user or living in the same region as the user may be detected from the tCS database.

Meanwhile, the user characteristic may be derived from, for example, the acquired EEG data of the user, or may be obtained from the user, but is not limited thereto. For example, as a part related to disease information among user characteristics, information about what disease the user has and information about the severity of the user's disease can be derived by analyzing the user's EEG data. As part of the personal information, the information about the age of the user may be information obtainable from the user.

Hereinafter, a method of deriving user characteristics from EEG data of a user will be described with reference to FIG. 2.

First, referring to FIG. 2, the EEG data of a plurality of objects may be classified and stored according to object characteristics (S31). Here, the object may mean, for example, a subject who is an object of EEG measurement and provides EEG data.

The EEG database may store EEG data measured for a plurality of objects. Therefore, according to the present embodiment, since sufficient accumulated data is secured, the reliability of deriving user characteristics can be improved.

The EEG data stored in the EEG database may be classified in advance according to the object characteristic, and the EEG data may include information on the object characteristic reflecting at least one of personal information, environmental information, and disease information about the corresponding object.

Subsequently, referring to FIG. 2, EEG data of a target corresponding to the EEG data of the user may be extracted from the EEG database (S32).

Specifically, by comparing the EEG data of the user and the EEG data of the object stored in the EEG database, the EEG data of the object corresponding to the EEG data of the user may be extracted from the EEG database. However, the EEG data of the user and the EEG data of the target need not be the same, for example, by confirming that the user's EEG data has the same or similar pattern as the specific pattern included in the target EEG data, so as to confirm the user's EEG data. EEG data of a target corresponding to may be extracted, but is not limited thereto.

Meanwhile, the EEG data of the user may be obtained at step S20 of obtaining the above-described user's EEG data.

Subsequently, with reference to FIG. 2, a user characteristic of the user may be derived using the target characteristic for the extracted EEG data (S33).

Specifically, the EEG data of the object stored in the EEG database is classified according to the object characteristic in advance, and accordingly, each object characteristic having the corresponding EEG data is derived in advance. Therefore, since the target characteristic of the extracted EEG data is likely to be the same as or similar to the user characteristic of the user, the target characteristic of the extracted EEG data may be derived as the user characteristic of the user.

For example, if the EEG data of the subject who is mild cognitive impairment (MCI) is determined to correspond to the EEG data of the user and extracted from the EEG database, the user is also likely to have mild cognitive impairment. User characteristics can be derived as having mild cognitive impairment.

That is, as described above, the user characteristic of the user may be derived through the steps of the method of deriving the user characteristic from the EEG data of the user, but is not limited thereto.

In addition, in addition to the user characteristics being derived from the EEG data, the user characteristics may be obtained from the user. For example, a user characteristic may be directly input from a user and stored in a related database, and a user characteristic obtained from the user may be provided through the corresponding database.

Subsequently, referring to FIG. 1, in consideration of the extracted EEG change data, it is possible to predict a change in user characteristics that occurs when a user applies a head and neck current stimulus (S40).

That is, since there is a common feature between the user and the object, the change that occurs when applying the head and neck current stimulus to the object is likely to occur similarly to the user. Therefore, through the extracted EEG change data, by checking how the EEG data of the subject changed according to the transcranial current stimulus, it is possible to predict a change in user characteristics that occurs when applying the transcranial current stimulus to the user.

Specifically, referring to FIG. 3, the step (S40) of predicting the change of the user characteristic may include the following two steps. First, referring to FIG. 3, target characteristics that vary according to the transcranial current stimulus tCS may be analyzed based on the extracted EEG change data (S41).

As described above, since the EEG change data may include the EEG data of the subject measured before applying the head and neck current stimulus to the subject, and the EEG data of the subject measured after applying the head and neck current stimulus to the subject, By comparing the EEG data, it is possible to analyze what object characteristics vary according to the cranial current stimulation. In addition, the analysis may confirm the amount of change in the object characteristic to see how the object characteristic has changed.

However, when there are a plurality of extracted EEG change data, characteristics corresponding to the user may be different for each target, and target characteristics that vary according to the cranial current stimulation may also be different. For example, if there is EEG change data of a subject of the same type as a user and a disease, and EEG change data of a subject of the same age as a user, analyzing the EEG change data of each subject has different target characteristics depending on the cranial current stimulation. can be different.

Subsequently, with reference to FIG. 3, it is possible to predict a change in user characteristic that occurs when a user applies a head and neck current stimulus based on the analyzed change amount of the characteristic (S42).

Specifically, when applying the head and neck current stimulation to the user, it can be predicted that there will be a change in the user characteristic by the amount of change in the analyzed target characteristic. And, when there are a plurality of extracted EEG change data, it is possible to predict the change of the user characteristic by reflecting the change amount of each analyzed target characteristic, for example, that the user characteristic will change as much as the average of the analyzed changes of the characteristic of the characteristic. You can also predict.

However, it is possible to predict that the change in the user's characteristics will occur to correspond to the amount of change in the analyzed object's characteristics only when the user's head and neck current stimulation is to be applied to the user using the same protocol as the analyzed object. Therefore, if a user intends to apply a transcranial current stimulus to a user with a different protocol than the analyzed subject, the amount of change in the analyzed characteristic of the subject, the protocol of the transcranial current stimulus applied to the analyzed subject, and the By considering the protocol, it is possible to predict changes in user characteristics that occur when a user applies a transcranial current stimulus.

Hereinafter, a method of predicting a change in user characteristics using EEG data according to a second embodiment of the present invention will be described with reference to FIGS. 4 to 5. However, the following description will focus on differences from a method of predicting a change in user characteristics using EEG data according to the first embodiment of the present invention. Referring to FIG. 4, a flowchart of a method of predicting a change in user characteristics using EEG data according to a second embodiment of the present invention is disclosed. Referring to FIG. 5, a diagram illustrating a visual display of user characteristics is shown. Is initiated.

First, referring to FIG. 4, S10, S20, S30, and S40 are the same as those described in the method for predicting the change of the user characteristic using the EEG data according to the first embodiment of the present invention, and the second embodiment of the present invention. In the method for predicting the change in the user characteristic using the EEG data according to the example, after predicting the change in the user characteristic, visually displaying the change in the user characteristic predicted according to the head and neck current stimulation (tCS) (S50) ) May be further included.

Referring to FIG. 5, EEG data for a user may be displayed in a space composed of three biomarkers, and the severity of disease (normal, MCI, Alzheimer's) may be displayed in the space as a user characteristic. However, the change in the user's characteristics visually displayed, that is, the change in the user's state, may reflect the change in the user's condition and the change in the emotional state of the user, in addition to the change in the severity of the disease.

For example, the past and present of how user characteristics are changing according to the transcranial current stimulus and the future of how it will change in the future can be visually displayed at the same time. First, the first point P1 may be past EEG data, and the second point P2 may be current EEG data. That is, the user characteristic is changed from the first point P1 to the second point P2 along the dotted line through the transcranial current stimulus, and it is confirmed that the degree of Alzheimer's is improving.

Subsequently, the third point P3 is future EEG data and may be a point predicted according to the present embodiment. In other words, it is predicted that the user state is changed from the second point P2 to the third point P3 when the transcranial current stimulus is applied to the user, and that the user characteristic is predicted to improve from Alzheimer's to MCI. .

According to the method for predicting the change of the user characteristic using the EEG data according to the third embodiment of the present invention, since the change of the user characteristic is visually displayed, the change of the user characteristic can be easily grasped.

On the other hand, a change in user characteristics may be visually displayed on a display (not shown), for example, and the display may be unlimited, such as a smartphone or a monitor.

Hereinafter, a method of predicting a change in user characteristics using EEG data according to a third embodiment of the present invention will be described with reference to FIG. 6. However, the following description will focus on differences from a method of predicting a change in user characteristics using EEG data according to the first embodiment of the present invention. Referring to FIG. 6, a flowchart of a method of predicting a change in user characteristics using EEG data according to a third embodiment of the present invention is disclosed.

First, referring to FIG. 6, S10, S20, S30, and S40 are the same as those described in the method for predicting the change of the user characteristic using the EEG data according to the first embodiment of the present invention, and the third embodiment of the present invention. According to the example, the method of predicting the change of the user characteristic by using the EEG data, after predicting the change of the user characteristic, in consideration of the predicted change in the user characteristic changes to the user to change the user characteristic by a predetermined change amount It may further comprise the step (S60) of adjusting the protocol of the two current stimulation.

As described above, it is possible to predict that the change of the user characteristic will occur only when the user intends to apply the transcranial current stimulus to the user by the same protocol as the analyzed object. Thus, if the variation in the analyzed characteristic of the subject is less than the predetermined variation or exceeds the predetermined variation, the protocol of the cranial current stimulus applied to the user to be different from the protocol of the transcranial current stimulus applied to the analyzed subject is adjusted. Can be. Therefore, according to the method of predicting the change of the user characteristic by using the EEG data according to the third embodiment of the present invention, the change of the user characteristic may occur as much as the planned change amount.

Hereinafter, referring to FIG. 7, a method of predicting a change in user characteristics using EEG data according to a fourth embodiment of the present invention will be described. However, the following description will focus on differences from a method of predicting a change in user characteristics using EEG data according to the first embodiment of the present invention. Referring to FIG. 7, a flowchart of a method of predicting a change in user characteristics using EEG data according to a fourth embodiment of the present invention is disclosed.

First, referring to FIG. 7, the EEG data of a plurality of targets may be classified and stored according to a target characteristic (S110).

Subsequently, with reference to FIG. 7, a plurality of EEG data having a difference in measurement time may be acquired for the user (S120).

Specifically, the step of obtaining the EEG data of the user, may be a step of obtaining a plurality of EEG data having a difference in the measurement time. For example, a plurality of EEG data of one day interval may be acquired for the user, but the present invention is not limited thereto.

Subsequently, referring to FIG. 7, the EEG data of the user may be compared with the EEG data of the object stored in the EEG database, and the EEG data of the object corresponding to the EEG data of the user may be extracted from the EEG database (S130).

For example, by confirming that the user's EEG data has the same or similar pattern as the specific pattern included in the target EEG data, the target EEG data corresponding to the user's EEG data may be extracted, but is not limited thereto.

Subsequently, with reference to FIG. 7, a user characteristic of the user may be derived using the target characteristic for the extracted EEG data (S140).

Subsequently, the change in the user characteristic may be predicted in consideration of the change aspect of the user characteristic derived according to the plurality of EEG data of the user (S150).

For example, it is possible to grasp the degree of change in how the user characteristics derived according to the respective EEG data change with measurement time. Therefore, taking into account the degree of the identified change, it is possible to predict how much the user characteristic will change when measured later.

Hereinafter, a method of predicting a change in user characteristics using EEG data according to a fifth embodiment of the present invention will be described with reference to FIGS. 8 and 9. However, the following description will focus on differences from a method of predicting a change in user characteristics using EEG data according to the fourth embodiment of the present invention. Referring to FIG. 8, a flowchart of a method of predicting a change in user characteristics using EEG data according to a fifth embodiment of the present invention is disclosed. Referring to FIG. 9, a diagram illustrating a visual display of user characteristics is shown. Is initiated.

First, referring to FIG. 8, S110, S120, S130, S140, and S150 are the same as those described in the method for predicting the change of the user characteristic using the EEG data according to the fourth embodiment of the present invention. The method of predicting the change of the user characteristic using the EEG data according to the embodiment may further include visually displaying the predicted change of the user characteristic after predicting the change of the user characteristic (S160).

Referring to FIG. 9, EEG data for a user may be displayed in a space composed of three biomarkers, and the severity of disease (normal, MCI, Alzheimer's) may be displayed in the space as a user characteristic.

For example, the past and present of how user characteristics are changing and the future of how they will change may be visually displayed simultaneously. First, the first point P1 may be past EEG data, and the second point P2 may be current EEG data. That is, it can be seen that the user characteristic is changed from the first point P1 to the second point P2 along the solid line and deteriorates to Alzheimer's in the MCI.

Subsequently, the third point P3 is future EEG data and may be a point predicted according to the present embodiment. That is, it can be seen that the user state is expected to change from the second point P2 to the third point P3, and the user characteristic is predicted to deteriorate in Alzheimer's. The criterion of this prediction may be, for example, an aspect of the change of the user characteristic from the first point P1 to the second point P2.

Hereinafter, a system capable of performing a method of predicting a change in user characteristics using EEG data according to embodiments of the present invention will be described with reference to FIG. 10. Referring to FIG. 10, a diagram illustrating a system capable of performing a method of predicting a change in user characteristics using EEG data according to embodiments of the present invention is disclosed.

Referring to FIG. 10, the prediction system 1 may include a controller 10, a storage 20, a display 30, an EEG measurement device 40, and a tCS device 50. However, since the configuration illustrated in FIG. 10 may not be an essential configuration, the prediction system may be implemented using more or fewer components than those illustrated in FIG. 10.

The controller 10 controls the overall prediction system 1, and each step of the method for predicting the change of the user characteristic using the EEG data according to the embodiments of the present invention may be performed by the controller 10. .

The storage unit 20 may store data necessary for the prediction system 1, and may include, for example, a tCS database 21 and an EEG database 22, but the database in the storage unit 20 is not limited thereto. .

The display unit 30 may visualize the prediction result, and the type thereof is not limited. In addition, the EEG measuring device 40 may measure the EEG data of the user, and the tCS device 50 may apply the head and neck current stimulation to the user.

Although embodiments of the present invention have been described above with reference to the accompanying drawings, those skilled in the art to which the present invention pertains may implement the present invention in other specific forms without changing the technical spirit or essential features thereof. I can understand that. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive.

Claims

Providing a tCS database in which EEG change data indicating a change in EEG data of a subject according to transcranial current stimulation (tCS) is stored for a plurality of subjects, wherein the EEG change data are classified and stored according to subject characteristics. providing a tCS database;

Obtaining EEG data of the user;

Extracting EEG change data for an object having a characteristic corresponding to at least one of a user characteristic derived from the EEG data of the user or a user characteristic obtainable from the user, from the tCS database; And

Predicting a change in user characteristics that occurs when applying a head and neck current stimulus to the user in consideration of the extracted EEG change data

A method for predicting a change in user characteristics using EEG data, including.
The method of claim 1,

In view of the extracted EEG change data, the step of predicting a change in user characteristics that occurs when applying the head and neck current stimulation to the user,

Analyzing a target characteristic that depends on the transcranial current stimulus based on the extracted EEG change data, and a change in user characteristic that occurs when applying the transcranial current stimulus to the user based on the amount of change of the analyzed target characteristic Predicting

A method for predicting a change in user characteristics using EEG data, including.
The method of claim 1,

After predicting the change in the user characteristic, visually displaying the change in the user characteristic predicted according to the cranial current stimulation

Further comprising, EEG data using the method for predicting the change of the user characteristics.
The method of claim 1,

A method for predicting a change in user characteristics using EEG data, wherein the user characteristics and target characteristics reflect at least one of personal information, environmental information, and disease information.
The method of claim 1,

User characteristics derived from the user's EEG data,

Providing an EEG database in which EEG data of a plurality of targets is classified and stored according to target characteristics;

Comparing the EEG data of the user with the EEG data of the object stored in the EEG database, and extracting the EEG data of the object corresponding to the EEG data of the user from the EEG database;

Deriving a user characteristic of the user by using a target characteristic for the extracted EEG data

A method of predicting a change in user characteristics using EEG data, which is derived through.
The method of claim 1,

After predicting the change of the user characteristic, adjusting the protocol of the transcranial current stimulus applied to the user to change the user characteristic by a predetermined change amount in consideration of the predicted change of the user characteristic.

Further comprising, EEG data using the method for predicting the change of the user characteristics.
Providing an EEG database in which EEG data of a plurality of objects are classified and stored according to object characteristics;

Acquiring EEG data of a user, wherein acquiring a plurality of EEG data having a difference in measurement time;

Comparing the EEG data of the user with the EEG data of the object stored in the EEG database, and extracting the EEG data of the object corresponding to the EEG data of the user from the EEG database;

Deriving a user characteristic of the user by using a target characteristic for the extracted EEG data; And

Predicting a change in the user characteristic in consideration of a change in the user characteristic derived according to the plurality of EEG data of the user

A method for predicting a change in user characteristics using EEG data, including.
The method of claim 7, wherein

Predicting a change in user characteristics, and then visually displaying a predicted change in user characteristics

Further comprising, EEG data using the method for predicting the change of the user characteristics.
The method of claim 7, wherein

A method for predicting a change in user characteristics using EEG data, wherein the user characteristics and target characteristics reflect at least one of personal information, environmental information, and disease information.