KR20190059364A - Brain age estimation method and system using brain wave analysis - Google Patents

Abstract

The present invention relates to a method and system for estimating brain age through EEG analysis, comprising a measuring unit for measuring a stable state and an event-induced potential EEG, and a controller for removing noise from the EEG measured by the measuring unit, And an output unit for analyzing relative power, connectivity, and network index, and calculating an estimated brain age value in comparison with a health standard EEG index classified by gender and age, and an output unit for outputting an analysis result of the analysis unit.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a brain age estimation method and system using brain wave analysis,

The present invention relates to a brain age estimation method and system using EEG analysis, and more particularly, to a method and system for estimating the brain age by comparing brain EEG indicators with healthy EEG indices constructed by ages and genders will be.

Generally, the human brain grows and ages as it gets older. If the brain is unable to undergo normal growth and develops later than its age, it is likely to be linked to diseases such as brain developmental delays. Conversely, if the brain shows aging faster than age, degenerative diseases such as dementia may occur.

Therefore, measuring the objective brain age accurately measures the degree of brain growth and aging of the individual, and is a measure for prescribing or administering the disease according to the situation.

In connection with this, in Patent Document 10-2017-0073557 (Early Diagnosis Device for Dementia using Biological Signal Aging Analysis, published on June 28, 2017), it is possible to analyze the biological signal aging degree by measuring brain waves, Contents are described.

The above patent discloses a method of estimating the brain age reflecting the low frequency bias of the main peak position of the power spectrum obtained by brain wave measurement.

However, such a conventional brain age estimation method is difficult to be an objective index because it estimates the brain age using only a fragmentary result, and there is a problem that the reliability of the brain age diagnosed using the same is insufficient.

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 an objective and reliable brain age estimation method and system using various indicators.

Another problem to be solved by the present invention is to provide a method and system for estimating the brain age more objectively and reliably using the difference in characteristics of brain waves according to gender.

Another object of the present invention is to provide a method and system for generating an estimation result report of brain age by automatic tagging.

According to an aspect of the present invention, there is provided a brain age estimation system using brain wave analysis, comprising: a measurement unit for measuring a stable state and an event-induced potential brain wave; An analysis unit for analyzing the power spectrum, the absolute and relative power of each frequency band, the connectivity and the network index, and calculating the brain age estimation value in comparison with the health standard EEG index classified by gender and age; And an output unit for outputting the output signal.

According to an embodiment of the present invention, the measuring unit may include measurement sensors for measuring brain waves, an amplifying unit for amplifying measurement signals of the measurement sensors, and a stimulus generator for generating a stimulus for measuring an event- .

According to an embodiment of the present invention, the analysis unit may include a filter unit that removes noise from the EEG signals measured by the measurement unit, and a power spectrum analyzing unit that analyzes the correlation with the power spectrum of the noise- A comparator for comparing an analysis result of the index analyzer with brain wave data of a healthy person stored in an index database; And an operation unit for calculating an estimated value.

According to an embodiment of the present invention, the indicator analyzing unit includes a power spectrum analysis, which refers to absolute power and relative power per frequency band, functional connectivity of the brain, and network analysis, and the power spectrum of the brain waves includes detecting the size of the 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 indicator analyzing unit includes a power spectrum analysis, an absolute power and a relative power for each frequency band, a functional connectivity of the brain, and a network analysis. The functional connectivity includes a default mode network functional network between configuration areas of a network defined as a mode network (DMN), an attention 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 functional connectivity can be expressed by the following equation (1).

Equation 1

In the above equation (1), f is a frequency, Sxy (f) is a cross-spectrum between X and Y, Sxx (f) and Syy (f) are X and Y spectrums, respectively. im means imaginary part of coherence, and () is the mean of the interval described in ().

According to an embodiment of the present invention, the arithmetic unit may perform a brain age estimation calculation using Equation (2) below.

는 건강인 표준 뇌파 데이터베이스로부터 산출한 동일 연령대 및 성별 데이터베이스 평균값이고, σ는 건강인 표준 뇌파 데이터베이스로부터 산출한 동일 연령대 및 성별 데이터베이스 표준편차값X is an index value of detected EEG data,

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

According to an embodiment of the present invention, the output unit may tag the text data to the visualized image of the brain wave data of the measurement subject and output them together.

According to another aspect of the present invention, there is provided a brain age estimation method using brain wave analysis, comprising the steps of: a) measuring EEG data; b) filtering EEG data to remove noise; c) Calculating the absolute absolute power and relative power, connectivity, and network index of the subject; and d) comparing the estimated brain age of the subject with the standard EEG indicator of the same gender.

According to an embodiment of the present invention, e) tagging the text data classified according to the estimated brain age and outputting together with the visualized image of the brain wave data of the measurement subject.

According to an embodiment of the present invention, the step a) can measure the steady state and event-induced potential brain waves.

According to an embodiment of the present invention, the step c) may analyze the power spectrum and the association and the network in the noise-canceled EEG signals through the step b).

According to an embodiment of the present invention, the step c) 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 frequencies 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 c) includes a power spectral analysis, a functional connectivity and a network analysis of the brain, which means absolute power and relative power per frequency band, 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 d) may perform a brain age estimation calculation using the following equation (2).

Equation 2

는 건강인 표준 뇌파 데이터베이스로부터 산출한 동일 연령대 및 성별 데이터베이스 평균값이고, σ는 건강인 표준 뇌파 데이터베이스로부터 산출한 동일 연령대 및 성별 데이터베이스 표준편차값X is an index value of detected EEG data,

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

The present invention has the effect of estimating the brain age using the functional connectivity and the network index in addition to the power spectrum of the EEG, thereby achieving a more objective and reliable brain age estimation.

Particularly, in consideration of the specificity of brain waves according to gender, the present invention can provide a more objective and reliable brain age estimation considering gender of a subject to be measured.

In addition, the present invention can automatically generate a report according to the brain age estimation result, thereby preventing occurrence of errors in the report generation, preventing a specialist from being input into the report generation, It is effective.

1 is a block diagram of a brain age estimation system through EEG analysis according to a preferred embodiment of the present invention.
FIG. 2 is a flowchart of a brain age estimation method using an EEG analysis 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 an illustration of a visualized image of EEG data.

Hereinafter, a method and system for estimating brain age through EEG analysis 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 age estimation system through EEG analysis according to a preferred embodiment of the present invention.

Referring to FIG. 1, a brain age estimation system based on EEG analysis according to a preferred embodiment of the present invention includes a measurement unit 10 for measuring a stable state and an event-induced potential EEG, An analyzer 20 for analyzing the power spectra, absolute and relative power, connectivity, and network indices for the frequency bands, comparing them with healthy standard EEG indicators, and outputting the analysis results of the analyzer 20 And an output unit 30 for outputting the image data.

Hereinafter, the structure and operation of the brain age estimation system based on the EEG analysis according to the preferred embodiment of the present invention will be described in detail. FIG. 2 is a flowchart illustrating an EEG analysis according to a preferred embodiment of the present invention. Refer to the flowchart of the brain age estimation method.

The measurement unit 10 includes a measurement sensor 11 for measuring brain waves and an amplification unit 12 for amplifying a measurement signal of the measurement sensor 11. The measurement unit 10 measures an event- And a stimulus generator 13 for generating a stimulus.

EEG is an electrical signal generated when a signal is transmitted between brain cells. A measurement sensor (also referred to as a recording electrode 11) is attached to the scalp (S10).

The attachment position of the measurement sensor 11 is in accordance with the international standard 10-20 system (Nuwer, 1987).

Fig. 3 shows an electrode arrangement diagram of the measurement sensor 11. 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.

The electroencephalogram (EGG), which is an electroencephalogram (EGG) which is normally stable, has an amplitude of about 50., Amplified by the amplification unit 12, and is easily analyzed by the analysis unit 20.

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 13 having an electrode separate from the measurement sensor 11. That is, since the measurement of the ERP uses the reference electrode together with the recording electrode which is the measurement sensor 11, and the potential difference between the two electrodes is measured, the negative peak and the positive peak are detected by the skull 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 amplitude of the ERP is in the range of 0.1 to 0.5 kPa, the amplification unit 12 amplifies the amplified signal and analyzes it in the analysis unit 20 including the averaging process.

As described above, the present invention measures the EEG in conjunction with the measurement of the stable EEG, and the electrical signals obtained by amplifying the measured EEG signals are input to the analyzer 20 in step S20 of FIG.

The analysis unit 20 includes a filter unit 21 for removing noise from the signals input from the measurement unit 10 and performs an EEG preprocessing in step S30.

The filter unit 21 may simply remove the noise of the signal, but it may remove other components except the EEG through the deep 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 measuring unit 10 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 features 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).

The pure brain wave component can be input to the analysis unit 20 through the deep neural network.

The analysis unit 20 may be a computer such as a PC or a notebook computer, or may use hardware produced separately. The filter unit 21 for performing noise cancellation may be hardware or software that can be filtered. The filter unit 21 filters an electrical signal of the input unit 10 and extracts only valid signals.

The analysis unit 20 further includes an indicator analysis unit 22, an indicator database 23, a comparison unit 24, and an operation unit 25.

The electric signals obtained by amplifying the EEG filtered by the filter unit 21 are analyzed by the index analyzer 22 as 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 values are part of the baseline indicator to determine the age of the brain.

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 the distribution of the theta-beta ratio (TBR) index of the occipital region of male and female, 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 a centrality indicator.

In this way, the indicator analyzer 22 of the analyzer 20 analyzes not only the power spectrum but also the association and the network, and the comparator 24 compares the indicators stored in the indicator database 23 with the indicators And the analysis results of the index analyzing unit 22 are compared.

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

Then, as in step S60, the operation unit 25 of the analysis unit 20 estimates the brain age by obtaining an average brain age for each index.

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

는 건강인 표준 뇌파 데이터베이스로부터 산출한 동일 연령대 및 성별 데이터베이스 평균값이고, σ는 건강인 표준 뇌파 데이터베이스로부터 산출한 동일 연령대 및 성별 데이터베이스 표준편차값이다.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.

In other brain age estimation methods other than the above average method, a higher weight is assigned to the indexes indicated by a certain reliable frequency band, and correction can be made to affect the average value. In particular, the seta is a highly reliable frequency band with very similar results as the result of the brain age estimation, and can give a higher weight to the power spectrum of the seta.

The brain age analyzed in the analysis unit 20 is output through the output unit 30 as in step S70. Here, the term "output" refers to an output of a report that can be easily understood by a person to be measured, which may be a concept including display on a display device or direct printing on paper, etc., and includes transmission to another device using communication Should be understood.

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

More specifically, in the process of outputting from the output unit 30, the contents are tagged and output 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 unit 20 is less than a predetermined range value and whether the brain wave data is within the 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 (S72).

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. An example of a visualized image of EEG data is shown in Fig.

The content may be text data describing each brain wave data, and the output unit 30 may tag the text data to the visualized image of the brain wave data of the measurement subject and output it 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. For example, you can suggest exercise or diet that is right for your condition.

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.

10: Measuring section 11: Measuring sensor
12: Amplification unit 13: Stimulation unit
20: Analysis section 21: Filter section
22: Index analysis section 23: Index database
24: comparator 25:
30: Output section

Claims (15)

A measuring unit for measuring a stable state and an event-induced vestibular wave;
The EEG measured by the measurement unit was removed, and two or more selected from the power spectrum, the absolute and relative power of each frequency band, the connectivity and the network index were analyzed, and compared with the standard EEG indexes classified by sex and age, An analyzing unit for calculating an estimated age value; And
And an output unit for outputting an analysis result of the analysis unit. The method according to claim 1,
Wherein the measuring unit comprises:
Measuring sensors for measuring brain waves;
An amplifying unit amplifying a measurement signal of the measurement sensors; And
A brain age estimation system based on EEG analysis including a stimulus generator that generates a stimulus to measure an event-induced potential brain wave. The method according to claim 1,
However,
A filter unit for removing noise from the EEG signals measured by the measuring unit;
An index analyzer for analyzing a network and a correlation with a power spectrum of noise-canceled EEG signals in the filter unit;
An index database storing EEG brainwave data;
A comparing unit comparing the analysis result of the index analyzing unit with EEG brainwave data stored in the index database; And
And an arithmetic unit for calculating an estimated brain age value of the measurement subject by calculating a comparison result of the comparison unit. The method of claim 3,
Wherein the index analyzer comprises:
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. The method of claim 3,
Wherein the index analyzer comprises:
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 defined regions of the defined network. 6. The method of claim 5,
Preferably,
Wherein the brain age estimation system is characterized by being expressed by the following equation (1).
(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 operation unit,
Wherein the brain age estimation calculation is performed through Equation (2) below.
(2)

X is an index value of detected EEG data,

Is the average value of the same age and gender database calculated from the standard EEG database, and σ is the mean age and gender database standard deviation value calculated from the standard EEG database The method according to claim 1,
The output unit includes:
Tagging text data on a visualized image of brain wave data of a subject to be measured, and outputting a suggestion for maintaining a healthy brain age when the biological age is high or low compared with a biological age. a) measuring EEG data;
b) filtering EEG data to remove noise;
c) calculating a power spectrum, absolute power and relative power by frequency band, connectivity and network index;
d) A method for estimating the brain age by EEG analysis including a step of calculating an estimated brain age of a subject to be measured, in comparison with a standard EEG indicator of the same sex. 10. The method of claim 9,
e) tagging the text data classified according to the estimated brain age, and outputting the tagged data together with a visualized image of brain wave data of the measurement subject. 11. The method of claim 10,
The step a)
Wherein said brain age estimation method is characterized by measuring a stable state and an event induced evoked brain wave. 12. The method of claim 11,
The step c)
And analyzing the relationship between the power spectrum and the network in the noise-canceled EEG signals through the step b). 13. The method of claim 12,
The step c)
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. 13. The method of claim 12,
The step c)
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 computing functional connectivity between the constituent regions of the defined network. 13. The method of claim 12,
The step d)
Wherein the brain age estimation calculation is performed through Equation (2) below.
(2)

X is an index value of detected EEG data,

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

Images