WO2019103187A1

WO2019103187A1 - Platform and method for evaluating cognitive function of brain through brain waves

Info

Publication number: WO2019103187A1
Application number: PCT/KR2017/013454
Authority: WO
Inventors: 강승완; 진승현; 홍슬기; 윤현열; 윤혜진; 배영우
Original assignee: 주식회사 아이메디신
Priority date: 2017-11-23
Filing date: 2017-11-24
Publication date: 2019-05-31
Also published as: KR20190059376A

Abstract

The present invention relates to a platform and a method for evaluating the cognitive function of a brain through brain waves, which may include: a filter module unit for removing noise from received brain wave data; an indicator analysis module unit for analyzing the brain wave data's power spectrum and correlation and networks; an indicator database for storing normal people's brain wave data; a comparison module unit for comparing the analysis result of the indicator analysis module unit with the normal people's brain wave data stored in the indicator database; a calculation module unit for calculating the comparison result of the comparison module unit and thereby computing a result on the cognitive function of the brain of a subject being measured; and an output and transmission module unit for tagging text data to a visualized image of the brain function of the subject being measured, which is the calculation result of the calculation module unit, and then transmitting same together.

Description

Brain cognitive function evaluation platform and method through EEG

The present invention relates to a platform and method for evaluating brain cognitive functions through EEG, and more particularly, to a platform and method for evaluating brain cognitive function through EEG which can improve accessibility to users' brain health screening.

Generally, EEG is an objective signal that can evaluate an individual's brain condition and function. Methods for identifying brain health using brain waves have been proposed.

In order to detect, analyze and diagnose EEG, experts with very specialized knowledge are needed, and there are not many medical institutions that can professionally diagnose and evaluate them.

Therefore, it is very difficult and uncomfortable for a general individual to check his / her brain health condition in addition to general health checkups.

Although the aging degree of a biological signal can be analyzed using brain waves as disclosed in, for example, Japanese Laid-Open Patent Application No. 10-2017-0073557 (Early Diagnosis Device for Dementia using Biological Signal Aging Analysis, published on June 28, 2017) It is very difficult to check the brain health practically because there is a problem with the accessibility of the individual to the brain.

In addition, although a personal brain wave measuring apparatus is currently on the market, it is difficult to expect a reliable brain function evaluation by a personal brain wave measuring apparatus.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and it is an object of the present invention to provide a brain cognitive function evaluation platform and a brain cognitive function evaluation method which can improve the accessibility of brain health examination.

Another object of the present invention is to provide a platform and method for evaluating brain cognitive function through brain waves that can perform objectively reliable brain age estimation.

Another object of the present invention is to provide a brain and cognitive function evaluation platform and method for evaluating an objective and reliable traumatic brain injury.

Another object of the present invention is to provide a brain and cognitive function evaluation platform and method for evaluating brain / cognitive function which can determine objective and reliable degree of concentration.

According to an aspect of the present invention, there is provided a platform for evaluating brain cognitive functions through EEG, including a filter module for removing noise from received EEG data, a power spectrum of EEG data, A comparison module unit for comparing an analysis result of the index analysis module unit with brain wave data of a health person stored in the index database; And an output and transmission module for tagging and transmitting the text data to the visualized image of the brain function of the measurement subject, which is the calculation result of the calculation module, together with the output of the calculation module of the calculation subject module can do.

According to an embodiment of the present invention, the filter module unit may classify an EEG signal including measured noise input from a measurement terminal using a neural network as a pure EEG component, a horizontal eye motion component, a vertical eye motion component, , And other noise components, and extracts the pure EEG component.

According to an embodiment of the present invention, a measurement terminal includes measurement sensors for measuring brain waves, an amplification unit for amplifying a measurement signal of the measurement sensors, and a stimulus generator for generating a stimulus for measuring an event- .

According to one embodiment of the present invention, the brain cognitive function is brain age or traumatic brain injury or concentration, and the index database may be stored with power map and brain network intensity information according to the age and gender of the health person.

According to an embodiment of the present invention, the indicator analysis module includes a power spectrum analysis, a functional connectivity of the brain and a network analysis, which mean absolute power and relative power per frequency band, and the functional connectivity includes a default mode network between the constituent areas of a network defined as a default mode network (DMN), an attention mode network (AN), a fronto-parietal network (FPN) and a sensorimotor network (SMN) It may be to calculate functional connectivity.

According to an embodiment of the present invention, the functional connectivity can be expressed by the following equation (1).

Equation 1

(F) is a cross-spectrum between X and Y, Sxx (f) and Syy (f) are a spectrum of X and Y respectively, im is an imaginary part coherent (Imaginary part of coherence), and () is the average of the intervals described in ().

According to an embodiment of the present invention, the comparison module may compare the default mode network with the alpha-domain of the front-end parity network to identify the traumatic brain injury location and extent.

According to one embodiment of the present invention, in the case of the traumatic brain injury patient group, the connectivity between the left brain and the right brain at the same position is larger than that of the healthy control, and the connectivity between the left brain and the right brain is lower have.

According to an embodiment of the present invention, the calculation module unit may calculate the brain age or degree of brain damage through the following equation (2).

Equation 2

X is an index value or brain network intensity of detected EEG data,

Is the mean of the same age and gender index values stored in the index database or the mean of the standard brain network intensities of the same age and gender at the same age and σ is the standard deviation value of the same age and gender data or the standard deviation of the healthy standard brain network intensity value

According to another aspect of the present invention, there is provided a method for evaluating a brain cognition function through EEG comprising the steps of: a) measuring stable state and event induced EEG data at a measuring terminal and transmitting a result of a concentration survey to an analysis service server; b) Calculating the concentration of the EEG data transmitted from the measurement terminal together with the power spectrum of the EEG data, absolute power and relative power of each frequency band, connectivity and network index, theta-beta ratio, and c) Calculating the brain cognitive function level according to the seta-beta ratio and the result of the questionnaire, d) comparing the measured brain cognitive function with the measured brain cognitive function Tagged text data classified according to the brain cognitive function evaluated with the visualized image of the measured person's brain wave data To the user terminal or the measurement terminal.

According to an embodiment of the present invention, the step b) includes power spectral analysis, which refers to absolute power and relative power per frequency band, functional connectivity of the brain, and network analysis. The power spectrum of the brain waves includes the constituent frequency by detecting the amount of specific power, represented by ㎶ ² / Hz or dB / Hz units, the frequency bands absolute power is calculated by adding a configured frequency power for each frequency band, the relative power is the absolute power in a particular frequency band Can be calculated by dividing by the total power calculated in the entire frequency band.

According to an embodiment of the present invention, the step b) includes a power spectrum analysis, which refers to absolute power and relative power per frequency band, functional connectivity and network analysis of the brain, between the constituent areas of a network defined as a default mode network (DMN), an attention mode network (AN), a fronto-parietal network (FPN) and a sensorimotor network (SMN) Functional connectivity can be calculated.

According to an embodiment of the present invention, the step c) may calculate the brain age estimation calculation or the degree of brain damage through the following equation (2).

Equation 2

X is an index value or brain network intensity of detected EEG data,

According to one embodiment of the present invention, the degree of traumatic brain injury is calculated by comparing the calculated connectivity and network index with the standard brain network index of the same gender, calculating the degree of brain damage, If this setting is exceeded, individual indicators of the brain network can be compared to determine the location and extent of brain damage.

The present invention relates to a method and apparatus for transmitting brain wave data of an examinee measured by a standardized method in a primary or secondary medical institution to an analysis service server and automatically analyzing brain age, So that the accessibility can be improved so that the brain health examination can be easily performed.

In addition, the present invention has an effect of determining objective and reliable brain age estimation, concentration measurement, and degree of traumatic brain injury by performing EEG data analysis in consideration of gender.

1 is a block diagram of a brain cognitive function evaluation platform through EEG according to a preferred embodiment of the present invention.

2 is a flowchart of a method for evaluating brain cognition function through EEG according to a preferred embodiment of the present invention.

3 is an electrode arrangement diagram of the measurement sensor.

4 is an absolute power map of frequency bands of the EEG detected in the steady state.

FIG. 5 is a graph of the distribution of the relative cetaphile index of the occipital area of male and female.

Figure 6 is a graph of distribution of the theta-beta ratio (TBR) index of the occipital region of males and females.

7 is a distribution graph of the Seta network index.

8 shows network configuration area information.

FIG. 9 is a flow chart of step S70 in FIG.

10 is a flowchart of a method for evaluating brain cognition function through EEG according to another embodiment of the present invention.

FIG. 11 is a graph of the network connection strength between the traumatic brain injury patient group (mTBI) and the healthy control group (HC) in the DMN alpha network.

12 and 13 are graphs showing the connectivity of the DMN alpha network, respectively.

FIG. 14 is a graph of network connection strengths between the traumatic brain injury patient group (mTBI) and the health control group (HC) in the FPN alpha network.

15 and 16 are diagrams comparing the connectivity of the FPN alpha network, respectively.

17 is a graph of the network connection strength between a patient with traumatic brain injury and a healthy control group in the AN network.

FIG. 18 is a graph of the network connection strength between a patient with traumatic brain injury and a healthy control group in the SMN network.

19 shows an example of another item evaluated by the platform of another embodiment of the present invention.

- Explanation of symbols -

100: measuring terminal 110: measuring sensor

120: amplification unit 130: stimulus generation unit

200: Analysis service server 210: Filter module part

220: Index calculation module 230: Index database

240: comparison module module 250: calculation module module

260: output and transmission module module 300: user terminal

Hereinafter, a brain function analysis platform and method according to a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.

The embodiments of the present invention can make various changes and have various embodiments, and specific embodiments will be described in detail with reference to the drawings. It should be understood, however, that the embodiments of the present invention are not limited to specific embodiments, but include all modifications, equivalents, and alternatives falling within the spirit and scope of embodiments of the present invention.

Terms including ordinals, such as first, second, etc., may be used to describe various elements, but the elements are not limited to these terms. The terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the embodiments of the present invention, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component. And / or < / RTI > includes any combination of a plurality of related listed items or any of a plurality of related listed items.

The terminology used in the embodiments of the present invention is used only to describe a specific embodiment and is not intended to limit the embodiments of the present invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In the embodiments of the present invention, terms such as " comprise " or " comprise ", etc. designate the presence of stated features, integers, steps, operations, elements, parts, or combinations thereof, It should be understood that the foregoing does not preclude the presence or addition of other features, numbers, steps, operations, elements, parts, or combinations thereof.

Unless otherwise defined, 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 the embodiments of the present invention belong. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the meaning of the context in the related art and, unless explicitly defined in the embodiments of the present invention, are intended to mean ideal or overly formal .

1 is a block diagram of a brain function analysis platform according to a preferred embodiment of the present invention.

Referring to FIG. 1, the brain-cognitive-function evaluation platform according to the preferred embodiment of the present invention includes a filter module 210 for removing noise from received EEG data, a power spectrum of the EEG data, And an index database 230 for storing EEG data of a healthy person and an analysis result of the index analysis module 220 in accordance with an EEG of the health person stored in the index database 230 A calculation module 250 for calculating the brain cognitive function of the person to be measured by calculating the comparison result of the comparison module 240, And an output and transmission module 260 for tagging and transmitting the text data to the visualized image of the brain function of the measurement subject as a result of the operation.

The brain cognitive function evaluation platform through the EEG may be provided by a specific analysis service server 200 and may include various interfaces for receiving EEG data and concentration test results measured through various measurement terminals 100 have.

Hereinafter, the structure and operation of the brain cognitive function evaluation platform through the EEG according to the preferred embodiment of the present invention will be described in detail. In the following, Refer to the flowchart of the brain cognitive function evaluation method.

First, the measurement terminal 100 may be an EEG device owned by an individual, a terminal provided in a primary or secondary medical institution, and includes an apparatus capable of measuring EEG data and transmitting or uploading data.

The measurement terminal 100 includes a measurement sensor 110 for measuring brain waves and an amplifier 120 for amplifying a measurement signal of the measurement sensor 110. The measurement terminal 100 also measures an event- And a communication unit 140 for transmitting the measured EEG data to the analysis service server 200. [0031] FIG.

EEG is a measurement of an electrical signal generated when a signal is transmitted between brain cells. A measurement sensor (also referred to as a recording electrode, 110) is attached to the scalp and measured (S10).

The attachment position of the measurement sensor 110 is in accordance with the international standard 10-20 system (Nuwer, 1987).

Fig. 3 shows an electrode arrangement diagram of the measurement sensor 110. Fig.

And 19 electrodes (Fp1, Fp2, F7, F8, F3, F4, Fz, T3, T4, C3, C4, Cz, T5, T6, P3, P4, Pz, O1, O2) Since the EEG is a signal containing various frequency components, the EEG is divided into frequency bands in order to observe the characteristics of the constituent frequency components.

In the present invention, a delta (delta, 1 to 4 Hz), ata (4 to 8 Hz), alpha (alpha, 8 to 13 Hz) ~ 21 Hz), Beta 2 (Beta 2,

β

2, 21 ~ 30 Hz) can be used.

Electroencephalogram (EGG), which is an electroencephalogram (EGG) which is normally stable, has an amplitude of about 50,, amplified by the amplification unit 120, and is easily analyzed by the analysis service server 200.

Apart from this stable state of EEG measurement, Event Related Potentials (ERP) related to stimuli are expressions of specific mental processes, which are expressions of brain activity that occur in response to individual events.

The measurement of the event-induced potential uses a stimulus generator 130 having an electrode separate from the measurement sensor 110. That is, since the measurement of the ERP uses the reference electrode together with the recording electrode serving as the measurement sensor 110, and a method of measuring the potential difference between the two electrodes, the negative peak and the positive peak, And its polarity, and latency.

For example, P300 means a static peak with a latency of 300 ms, and P3 means a wave that appears third in the waveform. The brain potential induced by external stimuli is said to be sensory or extrinsic.

In the present invention, it is possible to estimate the brain age more precisely by measuring the event-induced potential with the stable state.

Since the amplitudes of the ERPs are 0.1 to 0.5 picoseconds, they can be amplified through the amplification unit 120 and analyzed by the analysis service server 200.

As described above, the present invention measures the electroencephalograms in the stable state and measures the event evoked potential. The electrical signals amplified by the measured brain waves are transmitted to the analysis service server 200 through the communication unit 140 do.

The analysis service server 200 may be operated by a specialist capable of analyzing brain wave data or may be operated by a tertiary medical institution having specialized personnel.

At this time, amplified EEG data is an electrical signal, and noise may be removed through filtering.

The analysis service server 200 may include a filter module 210 for removing noise from signals input from the measurement terminal 100, and performs an EEG process in step S30.

The filter module 210 may simply remove the noise of the signal, but it may remove other components except the EEG through the in-depth neural network analysis.

Each node layer of the neural network can extract different features from measured EEG data and different layer features can be learned from layer to layer. The characteristics of the lower level can be simple and concrete, and the higher the level, the more complex and abstract the character can be.

The EEG signal including the noise measured by the measurement terminal 100 can be classified into a pure EEG component, a horizontal eye motion component, a vertical eye motion component, a muscle motion component, and other noise components using the feature of the deep neural network have.

Then, based on the classified signal, the remaining noise components other than the EEG component are removed from the input EEG data, and the EEG elimination noise can be finally output.

The in-depth neural network that can be used for the depth neural network analysis may be Convolution Neural Network (CNN), Recurrent Neural Network (RNN) or Hybrid Neural Network (HNN).

In the case of transmitting the brain wave data from which the noise has been removed in the measurement terminal 100, the filter module unit 210 may not be used.

The analysis service server 200 may be a computer such as a PC, a notebook computer, or the like, and may use separately manufactured hardware. The filter module 210 for performing noise cancellation may be hardware or software that can be filtered. The filter module 210 filters the electrical signals of the received measurement terminal 100 and extracts valid signals.

The analysis service server 200 further includes an indicator analysis unit 220, an indicator database 230, a comparison module unit 240, an operation module unit 250, and an output and transmission module unit 260.

The electrical signals obtained by amplifying the EEG filtered by the filter module 210 are analyzed by the index analyzer 220 as shown in step S40. The analysis includes power spectral analysis, which refers to absolute and relative power per frequency band, functional connectivity of the brain, and network analysis.

The power spectrum of the EEG detects the magnitude of the power according to the constituent frequency of the EEG, expressed in units of ² / Hz or dB / Hz.

Absolute power is a value obtained by adding the constituent frequency power to each frequency band. Relative power is a value obtained by dividing the absolute power in the specific frequency band by the total power calculated in the entire frequency band.

These calculated values are part of the basis indicator for determining the age of the brain, which is one example of brain function analysis provided by the present invention.

FIG. 4 shows an absolute power map for each frequency band of the EEG detected in the stable state.

In FIG. 4, the absolute power of each frequency band is divided into gender and age group, and it can be seen that even in the same age range, there is a difference in absolute power among frequency bands according to gender.

The difference in EEG according to these genders can be confirmed by various measurement results, and the following explains the difference in characteristics of EEG between different sexes of the same age range.

FIG. 5 is a graph of distribution of relative theta band indexes of male and female in the occipital area, FIG. 6 is a graph of distribution of theta-beta ratio (TBR) index of male and female occipital regions, and FIG. 7 is a graph of distribution of theta network index .

As can be seen from FIGS. 5 to 7, the curves representing the mean of males and females in each of the indicators shown in the figure are different from each other. According to these characteristics, the brain age is estimated Doing so can cause errors.

In addition, the present invention uses not only the power spectrum of the detected EEG, but also the functional connectivity and network of the brain as an index of brain age determination.

The functional connectivity of the brain is calculated by calculating the EEG sources at 5810 positions in the cerebral cortex and then using the default mode network (DMN), the attention network (AN), the fronto- the functional connectivity between the constituent areas of the network defined by the parietal network (FPN) and the sensorimotor network (SMN).

Various indicators can be used as functional connectivity indicators, and imaginary coherence (iCoh) is used in the present invention. The functional connectivity index can be calculated by the following equation (1).

F is the frequency, Sxy (f) is the cross-spectrum between X and Y, Sxx (f) and Syy (f) are the spectra of X and Y, respectively. im means imaginary part of coherence, and () is the mean of the interval described in (). The value of iCo _xy is determined between 0 and 1.

If the value of iCoh _xy is 0, it means that the two signals at the positions of X and Y at a given frequency are linearly independent. Conversely, a value of 1 means that the two signals are at maximum correlated at a given frequency.

The network configuration area can be identified using the functional connectivity, and the network configuration area information is shown in FIG.

The computation of the brain network is a total connection strength index that calculates the mean value of the total connectivity index after calculating the brain connectivity between each network constitution area. The clustering coefficient, the path length, And < / RTI > centrality.

In this way, the indicator analyzer 220 of the analysis service server 200 analyzes not only the power spectrum but also the association and the network, and the comparison module 240 compares the indicators stored in the indicator database 230, And the analysis result of the index analyzing unit 220 are compared.

It is possible to estimate brain age by all indicators through the process of step S50.

Then, as in step S60, the calculation module 250 of the analysis service server 200 estimates the brain age by averaging the brain ages of the respective indicators.

The calculation formula of the brain age estimation can be expressed by the following equation (2).

In Equation (2), X is an index value of the detected EEG data,

Is the mean value of the same age and gender database calculated from the healthy standard EEG database and σ is the same age and gender database standard deviation value calculated from the healthy standard EEG database.

The brain age estimate (Z) is a value of zero, a positive integer or a negative integer, and thus is equal to, or greater or less than, the standard brain age of a healthy person.

The brain age analyzed in the analysis unit 20 is output through the output and transmission module unit 260 as in step S70. Here, the term output refers to the output of a report that can be easily understood by a person to be measured, which may be a concept including a display on a display device, and should be understood as a concept including transmission to another device using communication.

The present invention proposes an automated method for performing step S70.

More specifically, in the process of outputting by the output and transmission module 260, contents are tagged and outputted together with the brain wave data.

9 is a detailed flowchart of step S70.

In step S71, it is determined whether the final brain age estimated value Z analyzed by the analysis service server 200 is less than a predetermined range value, and whether brain wave data is within a normal range.

If the brain age estimated value Z is within the normal range, the state of the brain wave data is regarded as a 'normal' state and the contents corresponding to the 'normal' state are tagged in the visualized image of brain wave data at step S720.

Here, 'normal' means that the estimated brain age (Z) is equal to the standard brain age, which is 0, and the normal range is 0, and the predetermined brain age range is set. It can be determined that it is in the normal range.

If the brain age estimation value Z is not in the normal range, the brain age estimation value Z confirms whether the number is positive or negative.

That is, it is determined whether the brain age estimated value Z is larger or smaller than the EEG database average of health (S73).

If the brain age estimation value (Z) is positive, the brain age state of the measurement subject is identified as " increase " and the content corresponding to the increase state is tagged (S74).

Conversely, if the number is negative, the brain age state is identified as 'reduced', and the contents corresponding to the 'reduced' state are tagged in the visualized image of the brain wave data (S75).

Accordingly, the corresponding contents can be tagged to the visualized image of brain wave data according to the result of classifying brain age.

The content may be text data describing each brain wave data, and the output and transmission module 260 may tag the text data to the visualized image of brain wave data of the measurement subject and output the same together.

In addition, the content may include clinical interpretation information according to an estimated brain age. The contents can be configured by assigning unique values such as an identification number according to the difference between the estimated brain age and the age of the measurement subject, and the contents can be searched and tagged according to the brain age estimation result. Clinical interpretation information may include suggestions for maintenance of healthy brain age when the estimated brain age is high or low compared to the biological age of the estimated brain age.

10 is a flowchart of a method of analyzing brain function according to another embodiment of the present invention.

2 is a method of estimating brain age as an example of brain function analysis, and FIG. 10 is an example of analyzing and diagnosing traumatic brain damage as another example of brain function analysis.

In the traumatic brain injury analysis, only the EEG data in the stable state among the data transmitted from the measurement terminal 100 is used. This is because it is possible to make a more accurate comparison when the brain is in a stable state, as compared to the case where it responds to a new stimulus.

The index analyzer 220 calculates the connectivity and the network index from the stable EEG data received from the measuring terminal 100 (S100).

The calculation of connectivity and network index is the same as the calculation method described above with reference to Fig.

Various indicators can be used as the functional connectivity indicator. In the present invention, the iCoh expressed in Equation (1) is used. The value of iCoh _xy is determined between 0 and 1, and when iCoh _xy is 0, it means that the two signals at the positions of X and Y at a given frequency are linearly independent. Conversely, a value of 1 means that the two signals are at maximum correlated at a given frequency.

In this way, the indicator analyzer 220 of the analysis service server 200 analyzes the connectivity and the network, and the comparison module 240 compares the indicators stored in the network indicator database 230 with the indicator The analysis result of the analysis unit 220 is compared.

Then, the calculation module 250 calculates the intensity of each index, calculates an average, and calculates the degree of brain damage (Z) using the average. The calculation formula at this time can use Equation (2). Where X is the intensity of the detected index,

Is the same age and gender database mean value as calculated from the network index database of the health standard and σ is the same age and gender database standard deviation value calculated from the healthy standard network index database.

Next, the brain damage degree (Z) value calculated as in step S300 is determined to determine whether or not the brain is damaged.

The degree of brain damage (Z) is zero or a positive real or negative real value, and the brain damage level is determined using the positive real or negative real value.

If the degree of brain damage (Z) is 0, it is determined that there is no damage since it is the same as the standard network index of the healthy person, and the degree of brain damage can be judged according to the magnitude of the real number. If the absolute value of the degree of brain damage (Z) is less than 2.5 in consideration of the individual deviation of the calculated brain damage degree (Z), it can be judged that there is no brain damage.

Next, if the absolute value of the degree of brain damage (Z) exceeds 2.5 as a result of the determination in step S300, the degree of damage is diagnosed by comparing the network indicators of the same age, gender, health, and the detected network index as in step S400.

In step S400, the damage connectivity region and the degree of damage may be estimated by separately comparing the brain connectivity (network composition connectivity) of the health group and the brain damage patients.

An example of the network configuration connectivity may be the connectivity value between MFG_L and MFG_R, and step S400 compares all connectivity to determine the location and extent of the damage.

The brain network connection strength of the brain damage patient in a certain position of the brain may be stronger or weaker than the brain network connection strength of the health person, and the position and degree of the damage are judged by considering the difference of the network by the position.

Then, in step S500, the output and transmission module 260 transmits the analysis result of traumatic brain injury. At this time, the content can be automatically tagged and output.

Applicants of the present invention compared the steady state brain networks of twenty patients with traumatic brain injury and twenty healthy persons of the same age and sex.

11, the difference in the network intensity between the traumatic brain injury patient group (mTBI) and the healthy control group (HC) in the DMN alpha network is remarkable. In other words, the intensity of the DMN alpha network can be compared to determine the traumatic brain injury.

FIG. 12 shows that the healthy control group (HC) in the DMN alpha network exhibits stronger connectivity than the traumatic brain injury patient group (mTBI), and FIG. 13 shows that the traumatic brain injury patient group (mTBI) HC), as compared with the case of the non-HC.

12 and 13, in the case of the traumatic brain injury patient group (mTBI), the connectivity between the left and right hemispheres (MFG _L -MFG _R ) is larger than that of the healthy control group (HC) Lt; RTI ID = 0.0 > connectivity. &Lt; / RTI >

In this same brain network, the connectivity of the traumatic brain injury patient (mTBI) may show a stronger or weaker connection than the HC (healthy control group) The occurrence of damage can be detected, and the degree of damage can be confirmed using the difference in strength of connectivity.

14, the network intensity of the traumatic brain injury patient group (mTBI) and the healthy control group (HC) are significantly different from those of the DMN alpha network, and the traumatic brain injury can be judged using the intensity of the DMN alpha network.

FIG. 15 shows the case where the health control group (HC) exhibits stronger connectivity than the traumatic brain injury patient group (mTBI) in the FPN alpha network, and FIG. 16 shows that the traumatic brain injury patient group (mTBI) HC), as compared with the case of the non-HC.

15 and 16, in the case of the traumatic brain injury patient group (mTBI), the connectivity between the left brain and the right brain (DLPFC _L -DLPFC _R, etc.) is larger than that of the healthy control group (HC) Lt; RTI ID = 0.0 > connectivity. &Lt; / RTI >

On the other hand, in the AN network of FIG. 17, the difference in intensity between the HCs and the mTBIs is not clearly distinguished for all the frequency bands, Traumatic brain injury is difficult to judge.

This also applies to the SMN network shown in Fig.

The degree of brain damage analyzed as described above is output through the output and transmission module 260. Since the concept and operation of the output are described in detail above, the description of the example of judging the traumatic brain injury is omitted.

Referring to FIG. 19, the brain-cognitive function evaluation platform through the EEG according to the present invention can determine the concentration (attention) of a person to be measured by using the inputted concentration test result and the setter-beta ratio (TBR) index.

Concentration is a basic cognitive function that regulates the perception. It is a process of information selection and processing that recognizes and responds to stimuli from the external environment or individuals. The brain structures related to this concentration include the reticular activating system, superior colliculi of midbarain), thalamus, parietal lobe, prefrontal cortex, and frontal lobe.

In the present invention, the concentration is evaluated using theta-beta ratio, which is an EEG indicator of the prefrontal lobe.

Concentration score and theta - beta ratio of the majority of the youth showed that the higher the concentration score, the higher the theta - beta ratio.

As described above, in the present invention, an individual's concentration can be evaluated using a seta-beta ratio together with a concentration score through a concentration test.

The score obtained by analyzing the concentration evaluation questionnaire at this time can be input directly through the input device of the evaluation service server 200 including the platform, and can be inputted using the data of the uploaded file.

The output and transmission module 260 transmits to the user terminal 300 whether or not the brain age or traumatic brain injury, which is a result of brain function analysis, is measured. The transmission at this time may include transmission to an application installed in the user terminal 300, transmission of a multimedia message, and the like.

In addition, the output and transmission module 260 may transmit the brain function analysis result to the measuring terminal 100.

In addition, the concept of 'transmission' is stored in the cloud server by encrypting the evaluation result in the output and transmission module unit 260, accessing the cloud server using the authenticated user terminal 300 or the measurement terminal 100 It includes the concept that can be.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit and scope of the invention will be.

INDUSTRIAL APPLICABILITY According to the present invention, a measurement terminal for measuring brain waves and an analysis service server for performing analysis are separated from each other, thereby facilitating brain function analysis without the assistance of an EEG expert.

Claims

A filter module for removing noise from received EEG data;

An indicator analysis module for analyzing the power spectrum of the EEG data and the association and the network;

An index database storing EEG brainwave data;

A comparison module for comparing an analysis result of the index analysis module with EEG brainwave data stored in the index database;

A calculation module unit for calculating a comparison result of the comparison module to calculate a brain cognitive function result of the measurement subject; And

And an output and transmission module for tagging and transmitting text data to a visualized image of the brain function of the measurement subject, which is a result of the operation of the calculation module.
The method according to claim 1,

The filter module unit includes:

The EEG signal including the measured noise input from the measuring terminal is classified into a pure EEG component, a horizontal eye motion component, a vertical eye motion component, a muscle motion component, and other noise components using a deep neural network, A brain-cognitive function evaluation platform via brain waves.
The method according to claim 1,

The measuring terminal,

Measuring sensors for measuring brain waves;

An amplifying unit amplifying a measurement signal of the measurement sensors; And

A brain-cognitive function evaluation platform via brain waves including a stimulus generator that generates a stimulus to measure an event-induced potential brain wave.
The method of claim 3,

The brain cognitive function is brain age or traumatic brain injury or concentration,

Wherein the index database stores power map and brain network strength information according to the age and gender of the health person.
The method of claim 3,

The indicator analysis module,

Power spectral analysis, meaning absolute power and relative power per frequency band, functional connectivity and network analysis of the brain,

The functional connectivity may include a default mode network (DMN), an attention mode network (AN), a fronto-parietal network (FPN), and a sensorimotor network Characterized in that it calculates functional connectivity between the defined areas of the network.
6. The method of claim 5,

Preferably,

Wherein the brain cognitive function evaluation platform is expressed by the following equation (1).

Equation 1

(F) is a cross-spectrum between X and Y, Sxx (f) and Syy (f) are a spectrum of X and Y respectively, im is an imaginary part coherent () Denotes an imaginary part of coherence, and () denotes an average
The method of claim 3,

The comparison module unit,

Wherein the default mode network is compared with an alpha wave region of the frontal parity network to identify the location and extent of traumatic brain injury.
8. The method of claim 7,

In the alpha wave region,

The brain cognitive function evaluation platform is characterized in that the connectivity between the left brain and the right brain is greater in the traumatic brain injury patients than in the healthy controls, and the connectivity between the left brain and the other right brain is lower.
The method of claim 3,

The calculation module may include:

Wherein the brain age or brain damage degree is calculated through the following equation (2).

Equation 2

X is an index value or brain network intensity of detected EEG data,
Is the mean of the same age and gender index values stored in the index database or the mean of the standard brain network intensities of the same age and gender at the same age and σ is the standard deviation value of the same age and gender data or the standard deviation of the healthy standard brain network intensity value
a) measuring stable state and event-induced potential brain wave data at the measuring terminal and transmitting the result of the concentration survey to the analysis service server;

b) calculating a power spectrum of the EEG data transmitted from the measuring terminal, absolute power and relative power of each frequency band, connectivity and network index, and theta-beta ratio and the concentration according to the survey result;

c) comparing the measured brain ages or the standard network strengths of the subjects to be measured with the standardized EEG indicators or network intensities of the same sex, and calculating the brain cognitive function according to the seta-beta ratio and the results of the questionnaire; And

d) tagging the text data classified according to the brain cognitive function evaluated together with the evaluated brain cognitive function, and transmitting to the user terminal or the measurement terminal together with the visualized image of the measured brain wave data of the subject, Cognitive function assessment method.
11. The method of claim 10,

The step b)

Power spectral analysis, meaning absolute power and relative power per frequency band, functional connectivity and network analysis of the brain,

The power spectrum of the EEG detects the magnitude of the electric power according to the constituent frequencies of the EEG, expressed in units of 2 / Hz or dB / Hz,

Wherein the absolute power for each frequency band is calculated by adding a constituent frequency power to each frequency band,

Wherein the relative power is calculated by dividing the absolute power in a specific frequency band by the total power calculated in the entire frequency band.
11. The method of claim 10,

The step b)

Power spectral analysis, meaning absolute power and relative power per frequency band, functional connectivity and network analysis of the brain,

The functional connectivity may include a default mode network (DMN), an attention mode network (AN), a fronto-parietal network (FPN), and a sensorimotor network And calculating functional connectivity between the constituent regions of the defined network. &Lt; RTI ID = 0.0 > 18. < / RTI >
11. The method of claim 10,

The step c)

Wherein the brain age estimation calculation or the brain damage degree is calculated through the following equation (2).

Equation 2

X is an index value or brain network intensity of detected EEG data,
Is the mean of the same age and gender index values stored in the index database or the mean of the standard brain network intensities of the same age and gender at the same age and σ is the standard deviation value of the same age and gender data or the standard deviation of the healthy standard brain network intensity value
11. The method of claim 10,

The degree of traumatic brain injury,

The calculated connectivity and network index are compared with the standard gender health standard brain network index, the degree of brain damage is calculated,

Wherein when the absolute value of the degree of brain damage exceeds a predetermined value, individual indicators of the brain network are compared to determine the position and degree of brain damage.
13. The method of claim 12,

Preferably,

Wherein the brain cognitive function is expressed by the following equation (1).

(F) is a cross-spectrum between X and Y, Sxx (f) and Syy (f) are a spectrum of X and Y respectively, im is an imaginary part coherent () Denotes an imaginary part of coherence, and () denotes an average